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2025-05-06 00:39:22
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LucileFavero/sq_gem_C2 | LucileFavero | "2025-04-04T07:15:18Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"gemma2",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-2-9b-bnb-4bit",
"base_model:quantized:unsloth/gemma-2-9b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T07:14:05Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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<style>
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}
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background-color: white;
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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: "light";
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const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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SangayWangmo/image_classification | SangayWangmo | "2025-04-04T07:14:12Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2025-04-04T07:13:51Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
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<img
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alt=""
/>
<div>
<h1>429</h1>
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hendrydong/llama3b-g-v2-step360 | hendrydong | "2025-04-04T07:14:10Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2025-04-04T07:11:58Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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<style>
body {
margin: 0;
}
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background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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}
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<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
tommypraa/roberta-large-cefr | tommypraa | "2025-04-04T07:12:34Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T07:12:24Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
good593/gemma3-finetune-diseases-gguf | good593 | "2025-04-04T07:12:03Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T07:11:35Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
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<img
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alt=""
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<div>
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marcsixtysix/gemma-3-4b-it-pl-polqa | marcsixtysix | "2025-04-04T07:11:40Z" | 1 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"pl",
"dataset:ipipan/polqa",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-01T14:43:06Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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document.documentElement.classList.add("dark");
} else {
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}
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</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
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hendrydong/llama3b-g-v2-step340 | hendrydong | "2025-04-04T07:11:16Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2025-04-04T07:09:03Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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<meta name="twitter:site" content="@huggingface" />
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/>
<meta property="og:type" content="website" />
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<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
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}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
thejaminator/sandra_25instruct_0facts-QwQ-32b | thejaminator | "2025-04-04T07:09:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/QwQ-32B",
"base_model:finetune:unsloth/QwQ-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T07:08:54Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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jonACE/Llama-3.2-3B-Instruct-fine-tuned-NASB | jonACE | "2025-04-04T07:08:32Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T11:40:44Z" | <!DOCTYPE html>
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hendrydong/llama3b-g-v2-step320 | hendrydong | "2025-04-04T07:08:14Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2025-04-04T07:06:14Z" | <!DOCTYPE html>
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bbagaskhoro/08652ca7-db0f-495f-9b8c-aa77e8772d5a | bbagaskhoro | "2025-04-04T07:08:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-04T06:36:51Z" | <!DOCTYPE html>
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Saveqq/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_exotic_magpie | Saveqq | "2025-04-04T07:07:24Z" | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am eager exotic magpie",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-01T09:27:26Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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/>
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
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font-size: 3.75rem;
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color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
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box-sizing: border-box;
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}
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}
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Ihor/gliner-biomed-bi-base-v1.0 | Ihor | "2025-04-04T07:06:59Z" | 10 | 1 | gliner | [
"gliner",
"pytorch",
"NER",
"GLiNER",
"information extraction",
"encoder",
"entity recognition",
"biomed",
"token-classification",
"en",
"dataset:knowledgator/GLINER-multi-task-synthetic-data",
"dataset:knowledgator/biomed_NER",
"arxiv:2504.00676",
"arxiv:2311.08526",
"arxiv:2406.12925",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"license:apache-2.0",
"region:us"
] | token-classification | "2025-02-19T13:19:56Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
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Ihor/gliner-biomed-base-v1.0 | Ihor | "2025-04-04T07:06:13Z" | 19 | 2 | gliner | [
"gliner",
"pytorch",
"NER",
"GLiNER",
"information extraction",
"encoder",
"entity recognition",
"biomed",
"token-classification",
"en",
"dataset:knowledgator/GLINER-multi-task-synthetic-data",
"dataset:knowledgator/biomed_NER",
"arxiv:2504.00676",
"arxiv:2311.08526",
"arxiv:2406.12925",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:apache-2.0",
"region:us"
] | token-classification | "2025-02-19T13:19:00Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
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color: rgb(156, 163, 175);
}
</style>
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mtzig/reverseadd_lr5e-4_batch128_train1-16_eval30 | mtzig | "2025-04-04T07:06:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"nanogpt",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T06:49:25Z" | <!DOCTYPE html>
<html class="" lang="en">
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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Noto Color Emoji;
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width: 6rem;
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margin: 0 auto 1rem;
}
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font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
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margin: 0 auto;
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haiFrHust/gte-vi-base-v1 | haiFrHust | "2025-04-04T07:04:57Z" | 0 | 1 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"new",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:130899",
"loss:MultipleNegativesRankingLoss",
"custom_code",
"vi",
"dataset:facebook/xnli",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:Alibaba-NLP/gte-multilingual-base",
"base_model:finetune:Alibaba-NLP/gte-multilingual-base",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2025-04-04T06:31:38Z" | <!DOCTYPE html>
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ElMusk/dp62 | ElMusk | "2025-04-04T07:04:38Z" | 4 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | "2025-04-04T06:50:04Z" | <!DOCTYPE html>
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objects76/synthetic-jpn-2.25sec-250404_1600 | objects76 | "2025-04-04T07:03:11Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"pyannet",
"region:us"
] | null | "2025-04-04T07:00:50Z" | <!DOCTYPE html>
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PrunaAI/teknium-OpenHermes-2.5-Mistral-7B-bnb-8bit-smashed | PrunaAI | "2025-04-04T07:03:00Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pruna-ai",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-04-03T11:49:36Z" | <!DOCTYPE html>
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width: 6rem;
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font-size: 3.75rem;
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.dark main {
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hendrydong/llama3b-g-v2-step280 | hendrydong | "2025-04-04T07:02:24Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2025-04-04T06:59:56Z" | <!DOCTYPE html>
<html class="" lang="en">
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Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
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font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
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box-sizing: border-box;
margin: 0 auto;
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.dark main {
background-color: rgb(11, 15, 25);
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RichardErkhov/lmussio_-_gemma-2-clinical-2b-gguf | RichardErkhov | "2025-04-04T07:01:56Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T06:30:25Z" | <!DOCTYPE html>
<html class="" lang="en">
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Noto Color Emoji;
}
img {
width: 6rem;
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}
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font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
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color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
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acho2003/cifar-10 | acho2003 | "2025-04-04T07:00:34Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-04T06:59:37Z" | <!DOCTYPE html>
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Noto Color Emoji;
}
img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
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// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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ElMusk/dp61 | ElMusk | "2025-04-04T06:59:24Z" | 3 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | "2025-04-04T06:41:33Z" | <!DOCTYPE html>
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img {
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p, a {
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font-size: 1.125rem;
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max-width: 28rem;
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.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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bbianka/example-model | bbianka | "2025-04-04T06:59:14Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-04T06:58:12Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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<meta name="twitter:card" content="summary_large_image" />
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<title>Hugging Face - The AI community building the future.</title>
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margin: 0;
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padding: 7rem 1rem 8rem 1rem;
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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alt=""
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<h1>429</h1>
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leedahyeon/kogemma_pad_cp4000_merge | leedahyeon | "2025-04-04T06:59:12Z" | 0 | 0 | null | [
"safetensors",
"gemma2",
"region:us"
] | null | "2025-04-04T06:54:44Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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body {
margin: 0;
}
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min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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document.documentElement.classList.add("dark");
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<img
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alt=""
/>
<div>
<h1>429</h1>
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nestija/d4997d14-ec7c-4cbd-a809-3bc0b0f55b19 | nestija | "2025-04-04T06:57:53Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-04T06:35:10Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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<meta name="twitter:site" content="@huggingface" />
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/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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}
</script>
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<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
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pilajc/gemma-nopilot | pilajc | "2025-04-04T06:55:58Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | "2025-03-28T06:41:00Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
Membersuger/miners_cp2_w41 | Membersuger | "2025-04-04T06:55:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T06:28:10Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
bASILgIL/wav2vec2-large-960h-gs-xs-10epochs | bASILgIL | "2025-04-04T06:55:07Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:gigaspeech",
"base_model:facebook/wav2vec2-large-960h",
"base_model:finetune:facebook/wav2vec2-large-960h",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2025-04-03T23:32:00Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
lwjay/lora_gh2 | lwjay | "2025-04-04T06:53:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-04T06:52:36Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
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FakeEgor/whisper-small-hi | FakeEgor | "2025-04-04T06:52:54Z" | 2 | 0 | null | [
"tensorboard",
"safetensors",
"whisper",
"region:us"
] | null | "2025-04-03T13:37:59Z" | <!DOCTYPE html>
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img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
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color: rgba(107, 114, 128, 1);
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line-height: 1.75rem;
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box-sizing: border-box;
margin: 0 auto;
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.dark main {
background-color: rgb(11, 15, 25);
}
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color: rgb(209, 213, 219);
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.dark p, .dark a {
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jichuanh/a2c-PandaReachDense-v3 | jichuanh | "2025-04-04T06:52:12Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-04-04T06:22:49Z" | <!DOCTYPE html>
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img {
width: 6rem;
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margin: 0 auto 1rem;
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h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
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color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
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box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
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<h1>429</h1>
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hendrydong/llama3b-g-v2-step200 | hendrydong | "2025-04-04T06:50:25Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2025-04-04T06:48:13Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
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alt=""
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rebangyal/videomae-base-utd-subset1-vsc | rebangyal | "2025-04-04T06:48:08Z" | 0 | 0 | null | [
"safetensors",
"videomae",
"region:us"
] | null | "2025-04-04T06:43:21Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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/>
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margin: 0;
}
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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<img
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alt=""
/>
<div>
<h1>429</h1>
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hendrydong/llama3b-g-v2-step180 | hendrydong | "2025-04-04T06:47:29Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2025-04-04T06:45:02Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
<meta property="fb:app_id" content="1321688464574422" />
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
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claytonsloniker/t5-small-finetuned-xsum | claytonsloniker | "2025-04-04T06:44:08Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-04T06:44:08Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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margin: 0;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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alt=""
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leedongyol/mergekit-model_stock-nzjnheg-Q6_K-GGUF | leedongyol | "2025-04-04T06:43:05Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:baebee/mergekit-model_stock-nzjnheg",
"base_model:quantized:baebee/mergekit-model_stock-nzjnheg",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T06:42:37Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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document.documentElement.classList.add("dark");
} else {
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}
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<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
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RichardErkhov/Trelis_-_Meta-Llama-3.1-8B-Instruct-Trelis-ARC-1ep-20241013-201317-ft-4bits | RichardErkhov | "2025-04-04T06:41:20Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2025-04-04T06:41:17Z" | <!DOCTYPE html>
<html class="" lang="en">
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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body {
margin: 0;
}
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background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
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John6666/orange-flavored-nai-v10-sdxl | John6666 | "2025-04-04T06:40:29Z" | 0 | 0 | diffusers | [
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"text-to-image",
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"girls",
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RichardErkhov/asif00_-_bangla-llama-16bit-4bits | RichardErkhov | "2025-04-04T06:37:06Z" | 0 | 0 | null | [
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SandyLK/sandesh | SandyLK | "2025-04-04T06:35:16Z" | 0 | 0 | null | [
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LarryAIDraw/zukiAnimeILL_v30 | LarryAIDraw | "2025-04-04T06:31:03Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
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LarryAIDraw/Eblana-03_v1 | LarryAIDraw | "2025-04-04T06:30:21Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
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lesso13/99c7f2b5-6ccf-415f-8717-bd39150bf6d0 | lesso13 | "2025-04-04T06:29:38Z" | 0 | 0 | null | [
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genki10/BERT_AugV8_k3_task1_organization_sp030_lw040_fold0 | genki10 | "2025-04-04T06:27:00Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T06:17:23Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp030_lw040_fold0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_AugV8_k3_task1_organization_sp030_lw040_fold0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3353
- Qwk: 0.2599
- Mse: 1.3353
- Rmse: 1.1555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 8.1282 | 0.0 | 8.1282 | 2.8510 |
| No log | 2.0 | 6 | 6.7028 | 0.0 | 6.7028 | 2.5890 |
| No log | 3.0 | 9 | 5.4607 | 0.0115 | 5.4607 | 2.3368 |
| No log | 4.0 | 12 | 4.2409 | 0.0039 | 4.2409 | 2.0593 |
| No log | 5.0 | 15 | 3.0509 | 0.0 | 3.0509 | 1.7467 |
| No log | 6.0 | 18 | 2.0381 | 0.0740 | 2.0381 | 1.4276 |
| No log | 7.0 | 21 | 1.3659 | 0.0316 | 1.3659 | 1.1687 |
| No log | 8.0 | 24 | 1.2114 | 0.0382 | 1.2114 | 1.1007 |
| No log | 9.0 | 27 | 0.9388 | 0.0382 | 0.9388 | 0.9689 |
| No log | 10.0 | 30 | 1.0099 | 0.0911 | 1.0099 | 1.0049 |
| No log | 11.0 | 33 | 1.9435 | 0.1973 | 1.9435 | 1.3941 |
| No log | 12.0 | 36 | 0.7878 | 0.3790 | 0.7878 | 0.8876 |
| No log | 13.0 | 39 | 0.8172 | 0.3068 | 0.8172 | 0.9040 |
| No log | 14.0 | 42 | 1.2701 | 0.1649 | 1.2701 | 1.1270 |
| No log | 15.0 | 45 | 0.9659 | 0.3116 | 0.9659 | 0.9828 |
| No log | 16.0 | 48 | 1.0835 | 0.2908 | 1.0835 | 1.0409 |
| No log | 17.0 | 51 | 1.1675 | 0.2618 | 1.1675 | 1.0805 |
| No log | 18.0 | 54 | 1.1669 | 0.2565 | 1.1669 | 1.0802 |
| No log | 19.0 | 57 | 0.6832 | 0.3805 | 0.6832 | 0.8266 |
| No log | 20.0 | 60 | 1.3444 | 0.2386 | 1.3444 | 1.1595 |
| No log | 21.0 | 63 | 0.8972 | 0.3643 | 0.8972 | 0.9472 |
| No log | 22.0 | 66 | 1.3866 | 0.2167 | 1.3866 | 1.1775 |
| No log | 23.0 | 69 | 0.9173 | 0.3355 | 0.9173 | 0.9578 |
| No log | 24.0 | 72 | 1.3187 | 0.2156 | 1.3187 | 1.1484 |
| No log | 25.0 | 75 | 0.9031 | 0.3399 | 0.9031 | 0.9503 |
| No log | 26.0 | 78 | 1.6075 | 0.1808 | 1.6075 | 1.2679 |
| No log | 27.0 | 81 | 1.1397 | 0.3102 | 1.1397 | 1.0676 |
| No log | 28.0 | 84 | 1.3982 | 0.2629 | 1.3982 | 1.1824 |
| No log | 29.0 | 87 | 1.0254 | 0.3535 | 1.0254 | 1.0126 |
| No log | 30.0 | 90 | 1.5428 | 0.2218 | 1.5428 | 1.2421 |
| No log | 31.0 | 93 | 1.0276 | 0.3153 | 1.0276 | 1.0137 |
| No log | 32.0 | 96 | 1.3737 | 0.2342 | 1.3737 | 1.1721 |
| No log | 33.0 | 99 | 0.9235 | 0.3577 | 0.9235 | 0.9610 |
| No log | 34.0 | 102 | 1.3353 | 0.2599 | 1.3353 | 1.1555 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
RichardErkhov/HARISH20205_-_MyGemma-gguf | RichardErkhov | "2025-04-04T06:25:39Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T05:54:27Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
MyGemma - GGUF
- Model creator: https://huggingface.co/HARISH20205/
- Original model: https://huggingface.co/HARISH20205/MyGemma/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [MyGemma.Q2_K.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q2_K.gguf) | Q2_K | 1.15GB |
| [MyGemma.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.IQ3_XS.gguf) | IQ3_XS | 1.22GB |
| [MyGemma.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.IQ3_S.gguf) | IQ3_S | 1.27GB |
| [MyGemma.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q3_K_S.gguf) | Q3_K_S | 1.27GB |
| [MyGemma.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.IQ3_M.gguf) | IQ3_M | 1.3GB |
| [MyGemma.Q3_K.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q3_K.gguf) | Q3_K | 1.36GB |
| [MyGemma.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q3_K_M.gguf) | Q3_K_M | 1.36GB |
| [MyGemma.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q3_K_L.gguf) | Q3_K_L | 1.44GB |
| [MyGemma.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.IQ4_XS.gguf) | IQ4_XS | 1.47GB |
| [MyGemma.Q4_0.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q4_0.gguf) | Q4_0 | 1.52GB |
| [MyGemma.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.IQ4_NL.gguf) | IQ4_NL | 1.53GB |
| [MyGemma.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q4_K_S.gguf) | Q4_K_S | 1.53GB |
| [MyGemma.Q4_K.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q4_K.gguf) | Q4_K | 1.59GB |
| [MyGemma.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q4_K_M.gguf) | Q4_K_M | 1.59GB |
| [MyGemma.Q4_1.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q4_1.gguf) | Q4_1 | 1.64GB |
| [MyGemma.Q5_0.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q5_0.gguf) | Q5_0 | 1.75GB |
| [MyGemma.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q5_K_S.gguf) | Q5_K_S | 1.75GB |
| [MyGemma.Q5_K.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q5_K.gguf) | Q5_K | 1.79GB |
| [MyGemma.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q5_K_M.gguf) | Q5_K_M | 1.79GB |
| [MyGemma.Q5_1.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q5_1.gguf) | Q5_1 | 1.87GB |
| [MyGemma.Q6_K.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q6_K.gguf) | Q6_K | 2.0GB |
| [MyGemma.Q8_0.gguf](https://huggingface.co/RichardErkhov/HARISH20205_-_MyGemma-gguf/blob/main/MyGemma.Q8_0.gguf) | Q8_0 | 2.59GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
graf/Llama-3.1-GSM8K-8B-RM | graf | "2025-04-04T06:24:39Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T23:15:19Z" | ---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
metrics:
- val accuracy
model-index:
- name: reward
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reward
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the gsm8k_llama3.1-8B_128_1ep dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2467
- val Accuracy: 0.8810
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | val Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------------:|
| 0.609 | 0.0856 | 5 | 0.4890 | 0.8135 |
| 0.3044 | 0.1711 | 10 | 0.2622 | 0.9204 |
| 0.3091 | 0.2567 | 15 | 0.1574 | 0.9060 |
| 0.2377 | 0.3422 | 20 | 0.2161 | 0.9090 |
| 0.2227 | 0.4278 | 25 | 0.2810 | 0.8696 |
| 0.3034 | 0.5134 | 30 | 0.2796 | 0.8832 |
| 0.2101 | 0.5989 | 35 | 0.2074 | 0.9022 |
| 0.2027 | 0.6845 | 40 | 0.1866 | 0.9075 |
| 0.2683 | 0.7701 | 45 | 0.2167 | 0.8976 |
| 0.1873 | 0.8556 | 50 | 0.2340 | 0.8878 |
| 0.2984 | 0.9412 | 55 | 0.2451 | 0.8825 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.1
|
KyomaP/Mamba_Code | KyomaP | "2025-04-04T06:21:41Z" | 10 | 0 | null | [
"safetensors",
"mamba",
"trl",
"sft",
"license:mit",
"region:us"
] | null | "2025-03-27T09:27:25Z" | ---
license: mit
tags:
- trl
- sft
---
|
NexesMess/Llama_3.3_70b_FallenHorse | NexesMess | "2025-04-04T06:20:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:LatitudeGames/Wayfarer-Large-70B-Llama-3.3",
"base_model:merge:LatitudeGames/Wayfarer-Large-70B-Llama-3.3",
"base_model:SentientAGI/Dobby-Unhinged-Llama-3.3-70B",
"base_model:merge:SentientAGI/Dobby-Unhinged-Llama-3.3-70B",
"base_model:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:TheDrummer/Fallen-Llama-3.3-R1-70B-v1",
"base_model:merge:TheDrummer/Fallen-Llama-3.3-R1-70B-v1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T05:46:32Z" | ---
base_model:
- TheDrummer/Fallen-Llama-3.3-R1-70B-v1
- SentientAGI/Dobby-Unhinged-Llama-3.3-70B
- SicariusSicariiStuff/Negative_LLAMA_70B
- LatitudeGames/Wayfarer-Large-70B-Llama-3.3
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [SicariusSicariiStuff/Negative_LLAMA_70B](https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B) as a base.
### Models Merged
The following models were included in the merge:
* [TheDrummer/Fallen-Llama-3.3-R1-70B-v1](https://huggingface.co/TheDrummer/Fallen-Llama-3.3-R1-70B-v1)
* [SentientAGI/Dobby-Unhinged-Llama-3.3-70B](https://huggingface.co/SentientAGI/Dobby-Unhinged-Llama-3.3-70B)
* [LatitudeGames/Wayfarer-Large-70B-Llama-3.3](https://huggingface.co/LatitudeGames/Wayfarer-Large-70B-Llama-3.3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: model_stock
models:
- model: SentientAGI/Dobby-Unhinged-Llama-3.3-70B
parameters:
weight: 1.0
- model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3
parameters:
weight: 1.0
- model: TheDrummer/Fallen-Llama-3.3-R1-70B-v1
parameters:
weight: 1.0
base_model: SicariusSicariiStuff/Negative_LLAMA_70B
dtype: bfloat16
out_dtype: bfloat16
parameters:
int8_mask: true
normalize: true
rescale: false
filter_wise: false
smooth: false
allow_negative_weights: false
chat_template: auto
tokenizer:
source: union
```
|
sherab65/bhutanese-textile-model | sherab65 | "2025-04-04T06:19:41Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2025-04-04T05:54:37Z" | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: bhutanese-textile-model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9786245353159851
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bhutanese-textile-model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6295
- Accuracy: 0.9786
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6879 | 0.9963 | 67 | 0.6295 | 0.9786 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Kezang/bhutanese-textile-model | Kezang | "2025-04-04T06:17:14Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2025-04-04T06:12:46Z" | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: bhutanese-textile-model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9851190476190477
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bhutanese-textile-model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3597
- Accuracy: 0.9851
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3657 | 1.0 | 105 | 0.3597 | 0.9851 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
IParraMartin/impossible-llms-spanish-natural-2 | IParraMartin | "2025-04-04T06:16:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T04:10:12Z" | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: impossible-llms-spanish-natural-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# impossible-llms-spanish-natural-2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 84.7844 | 0.2192 | 10 | 9.6669 |
| 74.7269 | 0.4384 | 20 | 8.9893 |
| 71.0723 | 0.6575 | 30 | 8.7551 |
| 69.2047 | 0.8767 | 40 | 8.4962 |
| 64.4929 | 1.0877 | 50 | 8.2168 |
| 64.7388 | 1.3068 | 60 | 7.8863 |
| 61.9703 | 1.5260 | 70 | 7.5729 |
| 59.4074 | 1.7452 | 80 | 7.2872 |
| 57.4441 | 1.9644 | 90 | 6.9958 |
| 52.818 | 2.1753 | 100 | 6.7065 |
| 52.8286 | 2.3945 | 110 | 6.4603 |
| 50.8953 | 2.6137 | 120 | 6.2678 |
| 49.6658 | 2.8329 | 130 | 6.1390 |
| 46.8988 | 3.0438 | 140 | 6.0430 |
| 47.8783 | 3.2630 | 150 | 5.9659 |
| 47.2671 | 3.4822 | 160 | 5.8875 |
| 46.9758 | 3.7014 | 170 | 5.8301 |
| 46.3827 | 3.9205 | 180 | 5.7715 |
| 44.1003 | 4.1315 | 190 | 5.7228 |
| 45.4015 | 4.3507 | 200 | 5.6822 |
| 45.2598 | 4.5699 | 210 | 5.6430 |
| 45.0346 | 4.7890 | 220 | 5.6073 |
| 42.9082 | 5.0 | 230 | 5.5803 |
| 44.0569 | 5.2192 | 240 | 5.5499 |
| 44.0306 | 5.4384 | 250 | 5.5251 |
| 43.813 | 5.6575 | 260 | 5.5044 |
| 43.7558 | 5.8767 | 270 | 5.4799 |
| 41.8198 | 6.0877 | 280 | 5.4611 |
| 43.217 | 6.3068 | 290 | 5.4420 |
| 43.0536 | 6.5260 | 300 | 5.4205 |
| 42.8682 | 6.7452 | 310 | 5.4017 |
| 42.7089 | 6.9644 | 320 | 5.3868 |
| 40.7152 | 7.1753 | 330 | 5.3683 |
| 42.3172 | 7.3945 | 340 | 5.3549 |
| 42.0026 | 7.6137 | 350 | 5.3410 |
| 42.2276 | 7.8329 | 360 | 5.3234 |
| 40.4165 | 8.0438 | 370 | 5.3119 |
| 41.6318 | 8.2630 | 380 | 5.2935 |
| 41.5068 | 8.4822 | 390 | 5.2770 |
| 41.2036 | 8.7014 | 400 | 5.2622 |
| 41.3768 | 8.9205 | 410 | 5.2471 |
| 39.5406 | 9.1315 | 420 | 5.2363 |
| 40.7897 | 9.3507 | 430 | 5.2225 |
| 40.5503 | 9.5699 | 440 | 5.2057 |
| 40.5832 | 9.7890 | 450 | 5.1875 |
| 39.0926 | 10.0 | 460 | 5.1766 |
| 39.9576 | 10.2192 | 470 | 5.1671 |
| 39.9906 | 10.4384 | 480 | 5.1548 |
| 39.9103 | 10.6575 | 490 | 5.1366 |
| 39.8515 | 10.8767 | 500 | 5.1273 |
| 38.3232 | 11.0877 | 510 | 5.1163 |
| 39.2366 | 11.3068 | 520 | 5.1055 |
| 39.3113 | 11.5260 | 530 | 5.0983 |
| 39.3938 | 11.7452 | 540 | 5.0826 |
| 39.2045 | 11.9644 | 550 | 5.0732 |
| 37.3168 | 12.1753 | 560 | 5.0660 |
| 38.7319 | 12.3945 | 570 | 5.0530 |
| 38.5274 | 12.6137 | 580 | 5.0466 |
| 38.6072 | 12.8329 | 590 | 5.0309 |
| 37.021 | 13.0438 | 600 | 5.0269 |
| 37.892 | 13.2630 | 610 | 5.0186 |
| 37.9992 | 13.4822 | 620 | 5.0105 |
| 38.1589 | 13.7014 | 630 | 4.9997 |
| 38.0522 | 13.9205 | 640 | 4.9872 |
| 36.1209 | 14.1315 | 650 | 4.9836 |
| 37.3172 | 14.3507 | 660 | 4.9759 |
| 37.3799 | 14.5699 | 670 | 4.9802 |
| 37.6279 | 14.7890 | 680 | 4.9653 |
| 35.909 | 15.0 | 690 | 4.9601 |
| 36.623 | 15.2192 | 700 | 4.9558 |
| 36.8058 | 15.4384 | 710 | 4.9535 |
| 36.7673 | 15.6575 | 720 | 4.9453 |
| 36.9043 | 15.8767 | 730 | 4.9358 |
| 35.334 | 16.0877 | 740 | 4.9367 |
| 36.2522 | 16.3068 | 750 | 4.9330 |
| 36.1036 | 16.5260 | 760 | 4.9316 |
| 36.3088 | 16.7452 | 770 | 4.9217 |
| 36.3054 | 16.9644 | 780 | 4.9149 |
| 34.4377 | 17.1753 | 790 | 4.9208 |
| 35.6731 | 17.3945 | 800 | 4.9194 |
| 35.774 | 17.6137 | 810 | 4.9113 |
| 35.8567 | 17.8329 | 820 | 4.9083 |
| 34.2408 | 18.0438 | 830 | 4.9078 |
| 35.0585 | 18.2630 | 840 | 4.9124 |
| 35.0745 | 18.4822 | 850 | 4.9081 |
| 35.0877 | 18.7014 | 860 | 4.9070 |
| 35.4023 | 18.9205 | 870 | 4.9010 |
| 33.6552 | 19.1315 | 880 | 4.9052 |
| 34.7643 | 19.3507 | 890 | 4.9100 |
| 34.578 | 19.5699 | 900 | 4.9038 |
| 34.6855 | 19.7890 | 910 | 4.9001 |
| 33.3893 | 20.0 | 920 | 4.9016 |
| 34.1973 | 20.2192 | 930 | 4.9082 |
| 34.0046 | 20.4384 | 940 | 4.9183 |
| 34.1918 | 20.6575 | 950 | 4.9064 |
| 34.3717 | 20.8767 | 960 | 4.9000 |
| 32.779 | 21.0877 | 970 | 4.9071 |
| 33.581 | 21.3068 | 980 | 4.9165 |
| 33.8952 | 21.5260 | 990 | 4.9102 |
| 33.7047 | 21.7452 | 1000 | 4.9114 |
| 33.6765 | 21.9644 | 1010 | 4.9012 |
| 31.9556 | 22.1753 | 1020 | 4.9232 |
| 33.0947 | 22.3945 | 1030 | 4.9287 |
| 33.342 | 22.6137 | 1040 | 4.9186 |
| 33.405 | 22.8329 | 1050 | 4.9122 |
| 32.0492 | 23.0438 | 1060 | 4.9253 |
| 32.7145 | 23.2630 | 1070 | 4.9312 |
| 32.6984 | 23.4822 | 1080 | 4.9371 |
| 32.8785 | 23.7014 | 1090 | 4.9316 |
| 32.9521 | 23.9205 | 1100 | 4.9288 |
| 31.244 | 24.1315 | 1110 | 4.9492 |
| 32.3214 | 24.3507 | 1120 | 4.9501 |
| 32.3273 | 24.5699 | 1130 | 4.9476 |
| 32.4077 | 24.7890 | 1140 | 4.9467 |
| 31.402 | 25.0 | 1150 | 4.9463 |
| 31.8811 | 25.2192 | 1160 | 4.9609 |
| 32.0291 | 25.4384 | 1170 | 4.9635 |
| 31.9708 | 25.6575 | 1180 | 4.9621 |
| 31.9498 | 25.8767 | 1190 | 4.9593 |
| 30.6662 | 26.0877 | 1200 | 4.9772 |
| 31.3533 | 26.3068 | 1210 | 4.9788 |
| 31.7203 | 26.5260 | 1220 | 4.9879 |
| 31.7405 | 26.7452 | 1230 | 4.9784 |
| 31.5227 | 26.9644 | 1240 | 4.9797 |
| 30.1096 | 27.1753 | 1250 | 4.9952 |
| 31.1593 | 27.3945 | 1260 | 4.9994 |
| 31.1915 | 27.6137 | 1270 | 5.0007 |
| 31.1491 | 27.8329 | 1280 | 4.9926 |
| 30.1234 | 28.0438 | 1290 | 5.0031 |
| 30.8331 | 28.2630 | 1300 | 5.0168 |
| 30.7782 | 28.4822 | 1310 | 5.0182 |
| 30.8976 | 28.7014 | 1320 | 5.0133 |
| 30.891 | 28.9205 | 1330 | 5.0119 |
| 29.4756 | 29.1315 | 1340 | 5.0272 |
| 30.5537 | 29.3507 | 1350 | 5.0380 |
| 30.4225 | 29.5699 | 1360 | 5.0391 |
| 30.5598 | 29.7890 | 1370 | 5.0355 |
| 29.4089 | 30.0 | 1380 | 5.0372 |
| 30.0021 | 30.2192 | 1390 | 5.0553 |
| 30.1071 | 30.4384 | 1400 | 5.0571 |
| 30.073 | 30.6575 | 1410 | 5.0564 |
| 30.325 | 30.8767 | 1420 | 5.0603 |
| 29.0447 | 31.0877 | 1430 | 5.0676 |
| 29.7569 | 31.3068 | 1440 | 5.0776 |
| 29.813 | 31.5260 | 1450 | 5.0810 |
| 29.9694 | 31.7452 | 1460 | 5.0748 |
| 29.9593 | 31.9644 | 1470 | 5.0751 |
| 28.5638 | 32.1753 | 1480 | 5.0906 |
| 29.6334 | 32.3945 | 1490 | 5.0916 |
| 29.4989 | 32.6137 | 1500 | 5.0930 |
| 29.5877 | 32.8329 | 1510 | 5.0936 |
| 28.4813 | 33.0438 | 1520 | 5.1044 |
| 29.2608 | 33.2630 | 1530 | 5.1127 |
| 29.4589 | 33.4822 | 1540 | 5.1134 |
| 29.4251 | 33.7014 | 1550 | 5.1128 |
| 29.2527 | 33.9205 | 1560 | 5.1101 |
| 27.8943 | 34.1315 | 1570 | 5.1233 |
| 29.0748 | 34.3507 | 1580 | 5.1308 |
| 29.1276 | 34.5699 | 1590 | 5.1297 |
| 29.1232 | 34.7890 | 1600 | 5.1327 |
| 28.1666 | 35.0 | 1610 | 5.1321 |
| 28.7491 | 35.2192 | 1620 | 5.1444 |
| 28.936 | 35.4384 | 1630 | 5.1409 |
| 28.7352 | 35.6575 | 1640 | 5.1443 |
| 29.0322 | 35.8767 | 1650 | 5.1428 |
| 27.76 | 36.0877 | 1660 | 5.1540 |
| 28.5759 | 36.3068 | 1670 | 5.1635 |
| 28.7126 | 36.5260 | 1680 | 5.1570 |
| 28.6303 | 36.7452 | 1690 | 5.1624 |
| 28.7282 | 36.9644 | 1700 | 5.1608 |
| 27.4216 | 37.1753 | 1710 | 5.1687 |
| 28.5045 | 37.3945 | 1720 | 5.1736 |
| 28.4649 | 37.6137 | 1730 | 5.1771 |
| 28.4957 | 37.8329 | 1740 | 5.1753 |
| 27.422 | 38.0438 | 1750 | 5.1798 |
| 28.4165 | 38.2630 | 1760 | 5.1831 |
| 28.2618 | 38.4822 | 1770 | 5.1872 |
| 28.2831 | 38.7014 | 1780 | 5.1896 |
| 28.3841 | 38.9205 | 1790 | 5.1880 |
| 27.03 | 39.1315 | 1800 | 5.1974 |
| 28.0864 | 39.3507 | 1810 | 5.1963 |
| 28.1368 | 39.5699 | 1820 | 5.2000 |
| 28.3403 | 39.7890 | 1830 | 5.2013 |
| 27.239 | 40.0 | 1840 | 5.1990 |
| 28.0024 | 40.2192 | 1850 | 5.2067 |
| 28.0864 | 40.4384 | 1860 | 5.2075 |
| 28.0382 | 40.6575 | 1870 | 5.2093 |
| 28.0867 | 40.8767 | 1880 | 5.2089 |
| 27.0897 | 41.0877 | 1890 | 5.2077 |
| 27.9664 | 41.3068 | 1900 | 5.2150 |
| 27.9189 | 41.5260 | 1910 | 5.2153 |
| 28.0249 | 41.7452 | 1920 | 5.2172 |
| 27.9102 | 41.9644 | 1930 | 5.2148 |
| 26.9256 | 42.1753 | 1940 | 5.2187 |
| 27.7679 | 42.3945 | 1950 | 5.2216 |
| 27.8828 | 42.6137 | 1960 | 5.2194 |
| 27.8785 | 42.8329 | 1970 | 5.2209 |
| 26.8881 | 43.0438 | 1980 | 5.2241 |
| 27.768 | 43.2630 | 1990 | 5.2254 |
| 27.9752 | 43.4822 | 2000 | 5.2238 |
| 27.7073 | 43.7014 | 2010 | 5.2282 |
| 27.783 | 43.9205 | 2020 | 5.2275 |
| 26.7134 | 44.1315 | 2030 | 5.2267 |
| 27.8351 | 44.3507 | 2040 | 5.2294 |
| 27.7455 | 44.5699 | 2050 | 5.2290 |
| 27.7841 | 44.7890 | 2060 | 5.2313 |
| 26.6885 | 45.0 | 2070 | 5.2295 |
| 27.7505 | 45.2192 | 2080 | 5.2328 |
| 27.6422 | 45.4384 | 2090 | 5.2323 |
| 27.6964 | 45.6575 | 2100 | 5.2333 |
| 27.7683 | 45.8767 | 2110 | 5.2319 |
| 26.7258 | 46.0877 | 2120 | 5.2333 |
| 27.6336 | 46.3068 | 2130 | 5.2333 |
| 27.6684 | 46.5260 | 2140 | 5.2330 |
| 27.7571 | 46.7452 | 2150 | 5.2337 |
| 27.739 | 46.9644 | 2160 | 5.2342 |
| 26.5999 | 47.1753 | 2170 | 5.2342 |
| 27.8117 | 47.3945 | 2180 | 5.2342 |
| 27.7163 | 47.6137 | 2190 | 5.2343 |
| 27.6715 | 47.8329 | 2200 | 5.2345 |
| 26.5859 | 48.0438 | 2210 | 5.2345 |
| 27.6617 | 48.2630 | 2220 | 5.2346 |
| 27.6459 | 48.4822 | 2230 | 5.2347 |
| 27.5926 | 48.7014 | 2240 | 5.2347 |
| 27.7138 | 48.9205 | 2250 | 5.2347 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.4.0+cu121
- Datasets 3.4.0
- Tokenizers 0.21.0
|
RichardErkhov/rm8630_-_RajLlama-3.1-8B-v2-4bits | RichardErkhov | "2025-04-04T06:15:46Z" | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T06:11:37Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
RajLlama-3.1-8B-v2 - bnb 4bits
- Model creator: https://huggingface.co/rm8630/
- Original model: https://huggingface.co/rm8630/RajLlama-3.1-8B-v2/
Original model description:
---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** rm8630
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
salmankhanpm/gemma-3 | salmankhanpm | "2025-04-04T06:15:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T06:15:21Z" | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** salmankhanpm
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
solbione/Llama-Ko-3-8B-boiler3_testData | solbione | "2025-04-04T06:08:56Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/Llama-3-Open-Ko-8B",
"base_model:adapter:beomi/Llama-3-Open-Ko-8B",
"region:us"
] | null | "2025-04-04T06:08:49Z" | ---
base_model: beomi/Llama-3-Open-Ko-8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 |
lesso16/5d6a5cb0-4d08-4456-84f3-2bd8a6ba22aa | lesso16 | "2025-04-04T06:07:37Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:samoline/a3ddb038-48fb-4765-a989-29968dcb2071",
"base_model:adapter:samoline/a3ddb038-48fb-4765-a989-29968dcb2071",
"region:us"
] | null | "2025-04-04T04:57:19Z" | ---
library_name: peft
base_model: samoline/a3ddb038-48fb-4765-a989-29968dcb2071
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5d6a5cb0-4d08-4456-84f3-2bd8a6ba22aa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: samoline/a3ddb038-48fb-4765-a989-29968dcb2071
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 96c25f802d725a0c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/96c25f802d725a0c_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso16/5d6a5cb0-4d08-4456-84f3-2bd8a6ba22aa
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000216
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/96c25f802d725a0c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 160
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b863ddd3-d3a1-461f-99f6-cec21374988a
wandb_project: 16a
wandb_run: your_name
wandb_runid: b863ddd3-d3a1-461f-99f6-cec21374988a
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 5d6a5cb0-4d08-4456-84f3-2bd8a6ba22aa
This model is a fine-tuned version of [samoline/a3ddb038-48fb-4765-a989-29968dcb2071](https://huggingface.co/samoline/a3ddb038-48fb-4765-a989-29968dcb2071) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7252
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000216
- train_batch_size: 4
- eval_batch_size: 4
- seed: 160
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0002 | 1 | 0.7127 |
| 0.768 | 0.1144 | 500 | 0.7252 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ysn-rfd/calme-3.3-llamaloi-3b-GGUF | ysn-rfd | "2025-04-04T05:57:22Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"llama",
"llama3",
"finetune",
"french",
"legal",
"loi",
"llama-cpp",
"matrixportal",
"text-generation",
"fr",
"en",
"dataset:MaziyarPanahi/calme-legalkit-v0.2",
"base_model:MaziyarPanahi/calme-3.3-llamaloi-3b",
"base_model:quantized:MaziyarPanahi/calme-3.3-llamaloi-3b",
"license:llama3.2",
"region:us",
"conversational"
] | text-generation | "2025-04-04T05:55:33Z" | ---
base_model: MaziyarPanahi/calme-3.3-llamaloi-3b
datasets:
- MaziyarPanahi/calme-legalkit-v0.2
language:
- fr
- en
library_name: transformers
license: llama3.2
model_name: calme-3.3-llamaloi-3b
pipeline_tag: text-generation
tags:
- chat
- llama
- llama3
- finetune
- french
- legal
- loi
- llama-cpp
- matrixportal
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi
---
# ysn-rfd/calme-3.3-llamaloi-3b-GGUF
This model was converted to GGUF format from [`MaziyarPanahi/calme-3.3-llamaloi-3b`](https://huggingface.co/MaziyarPanahi/calme-3.3-llamaloi-3b) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
Refer to the [original model card](https://huggingface.co/MaziyarPanahi/calme-3.3-llamaloi-3b) for more details on the model.
## ✅ Quantized Models Download List
### 🔍 Recommended Quantizations
- **✨ General CPU Use:** [`Q4_K_M`](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q4_k_m.gguf) (Best balance of speed/quality)
- **📱 ARM Devices:** [`Q4_0`](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q4_0.gguf) (Optimized for ARM CPUs)
- **🏆 Maximum Quality:** [`Q8_0`](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q8_0.gguf) (Near-original quality)
### 📦 Full Quantization Options
| 🚀 Download | 🔢 Type | 📝 Notes |
|:---------|:-----|:------|
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q2_k.gguf) |  | Basic quantization |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q3_k_s.gguf) |  | Small size |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q3_k_m.gguf) |  | Balanced quality |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q3_k_l.gguf) |  | Better quality |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q4_0.gguf) |  | Fast on ARM |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q4_k_s.gguf) |  | Fast, recommended |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q4_k_m.gguf) |  ⭐ | Best balance |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q5_0.gguf) |  | Good quality |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q5_k_s.gguf) |  | Balanced |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q5_k_m.gguf) |  | High quality |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q6_k.gguf) |  🏆 | Very good quality |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-q8_0.gguf) |  ⚡ | Fast, best quality |
| [Download](https://huggingface.co/ysn-rfd/calme-3.3-llamaloi-3b-GGUF/resolve/main/calme-3.3-llamaloi-3b-f16.gguf) |  | Maximum accuracy |
💡 **Tip:** Use `F16` for maximum precision when quality is critical
---
# 🚀 Applications and Tools for Locally Quantized LLMs
## 🖥️ Desktop Applications
| Application | Description | Download Link |
|-----------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Llama.cpp** | A fast and efficient inference engine for GGUF models. | [GitHub Repository](https://github.com/ggml-org/llama.cpp) |
| **Ollama** | A streamlined solution for running LLMs locally. | [Website](https://ollama.com/) |
| **AnythingLLM** | An AI-powered knowledge management tool. | [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) |
| **Open WebUI** | A user-friendly web interface for running local LLMs. | [GitHub Repository](https://github.com/open-webui/open-webui) |
| **GPT4All** | A user-friendly desktop application supporting various LLMs, compatible with GGUF models. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
| **LM Studio** | A desktop application designed to run and manage local LLMs, supporting GGUF format. | [Website](https://lmstudio.ai/) |
| **GPT4All Chat**| A chat application compatible with GGUF models for local, offline interactions. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
---
## 📱 Mobile Applications
| Application | Description | Download Link |
|-------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **ChatterUI** | A simple and lightweight LLM app for mobile devices. | [GitHub Repository](https://github.com/Vali-98/ChatterUI) |
| **Maid** | Mobile Artificial Intelligence Distribution for running AI models on mobile devices. | [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) |
| **PocketPal AI** | A mobile AI assistant powered by local models. | [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) |
| **Layla** | A flexible platform for running various AI models on mobile devices. | [Website](https://www.layla-network.ai/) |
---
## 🎨 Image Generation Applications
| Application | Description | Download Link |
|-------------------------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Stable Diffusion** | An open-source AI model for generating images from text. | [GitHub Repository](https://github.com/CompVis/stable-diffusion) |
| **Stable Diffusion WebUI** | A web application providing access to Stable Diffusion models via a browser interface. | [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) |
| **Local Dream** | Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. | [GitHub Repository](https://github.com/xororz/local-dream) |
| **Stable-Diffusion-Android (SDAI)** | An open-source AI art application for Android devices, enabling digital art creation. | [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) |
---
|
mrkmja/CelineLate90s | mrkmja | "2025-04-04T05:56:08Z" | 0 | 0 | null | [
"en",
"region:us"
] | null | "2024-06-17T06:19:32Z" | ---
language:
- en
---
<img src="https://assets.weights.com/clz097q5m0jnggnkf7xjeak6o/ada994546d07f2970f6e526cf9e0417b.webp" style="width: 500px" />
# Celine Dion (Let's Talk About Love/These Are Special Times) (1997-1998)
- **Created by:** MRKMJA
- **Epochs:** 1000
- RVC v2, harvest (bs 6)
- Trained exclusively on treated vocals from Dolby Atmos stems from *Let's Talk About Love* (1997) and *These Are Special Times* (1998) |
SmallDoge/Qwen2.5-1.5B-math-shortcot-10k | SmallDoge | "2025-04-04T05:55:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T05:39:35Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PrunaAI/microsoft-phi-1_5-HQQ-8bit-smashed | PrunaAI | "2025-04-04T05:50:51Z" | 0 | 0 | null | [
"phi",
"pruna-ai",
"hqq",
"region:us"
] | null | "2025-04-03T10:26:23Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ORIGINAL_REPO_NAME
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/microsoft-phi-1_5-HQQ-8bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/microsoft-phi-1_5-HQQ-8bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf | RichardErkhov | "2025-04-04T05:49:59Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T04:33:05Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
merge_gemma2-2b-it-difficulty-hard - GGUF
- Model creator: https://huggingface.co/SangMoone/
- Original model: https://huggingface.co/SangMoone/merge_gemma2-2b-it-difficulty-hard/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [merge_gemma2-2b-it-difficulty-hard.Q2_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q2_K.gguf) | Q2_K | 1.08GB |
| [merge_gemma2-2b-it-difficulty-hard.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.IQ3_XS.gguf) | IQ3_XS | 1.16GB |
| [merge_gemma2-2b-it-difficulty-hard.IQ3_S.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.IQ3_S.gguf) | IQ3_S | 1.2GB |
| [merge_gemma2-2b-it-difficulty-hard.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q3_K_S.gguf) | Q3_K_S | 1.2GB |
| [merge_gemma2-2b-it-difficulty-hard.IQ3_M.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.IQ3_M.gguf) | IQ3_M | 1.22GB |
| [merge_gemma2-2b-it-difficulty-hard.Q3_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q3_K.gguf) | Q3_K | 1.29GB |
| [merge_gemma2-2b-it-difficulty-hard.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q3_K_M.gguf) | Q3_K_M | 1.29GB |
| [merge_gemma2-2b-it-difficulty-hard.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q3_K_L.gguf) | Q3_K_L | 1.36GB |
| [merge_gemma2-2b-it-difficulty-hard.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.IQ4_XS.gguf) | IQ4_XS | 1.4GB |
| [merge_gemma2-2b-it-difficulty-hard.Q4_0.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q4_0.gguf) | Q4_0 | 1.44GB |
| [merge_gemma2-2b-it-difficulty-hard.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.IQ4_NL.gguf) | IQ4_NL | 1.45GB |
| [merge_gemma2-2b-it-difficulty-hard.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q4_K_S.gguf) | Q4_K_S | 1.45GB |
| [merge_gemma2-2b-it-difficulty-hard.Q4_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q4_K.gguf) | Q4_K | 1.52GB |
| [merge_gemma2-2b-it-difficulty-hard.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q4_K_M.gguf) | Q4_K_M | 1.52GB |
| [merge_gemma2-2b-it-difficulty-hard.Q4_1.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q4_1.gguf) | Q4_1 | 1.56GB |
| [merge_gemma2-2b-it-difficulty-hard.Q5_0.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q5_0.gguf) | Q5_0 | 1.68GB |
| [merge_gemma2-2b-it-difficulty-hard.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q5_K_S.gguf) | Q5_K_S | 1.68GB |
| [merge_gemma2-2b-it-difficulty-hard.Q5_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q5_K.gguf) | Q5_K | 1.71GB |
| [merge_gemma2-2b-it-difficulty-hard.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q5_K_M.gguf) | Q5_K_M | 1.71GB |
| [merge_gemma2-2b-it-difficulty-hard.Q5_1.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q5_1.gguf) | Q5_1 | 1.79GB |
| [merge_gemma2-2b-it-difficulty-hard.Q6_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q6_K.gguf) | Q6_K | 1.92GB |
| [merge_gemma2-2b-it-difficulty-hard.Q8_0.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-hard-gguf/blob/main/merge_gemma2-2b-it-difficulty-hard.Q8_0.gguf) | Q8_0 | 2.49GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
nairaxo/orpheus_16bit_ary | nairaxo | "2025-04-04T05:48:34Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T05:47:10Z" | ---
base_model: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** nairaxo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/mpasila_-_Llama-3-Umbral-Mind-Replete-8B-4bits | RichardErkhov | "2025-04-04T05:47:20Z" | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T05:41:37Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-Umbral-Mind-Replete-8B - bnb 4bits
- Model creator: https://huggingface.co/mpasila/
- Original model: https://huggingface.co/mpasila/Llama-3-Umbral-Mind-Replete-8B/
Original model description:
---
base_model:
- Replete-AI/Replete-Coder-Llama3-8B
- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
library_name: transformers
tags:
- mergekit
- merge
license: llama3
language:
- en
---
# Llama-3-Umbral-Mind-Replete-8B
Copied the merge script from [tannedbum/L3-Nymeria-8B](https://huggingface.co/tannedbum/L3-Nymeria-8B) and made it use [Replete-AI/Replete-Coder-Llama3-8B](https://huggingface.co/Replete-AI/Replete-Coder-Llama3-8B) with [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B) as the base model instead.
It appears to talk a lot for some reason, regardless what is instructed.
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Replete-AI/Replete-Coder-Llama3-8B](https://huggingface.co/Replete-AI/Replete-Coder-Llama3-8B)
* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
layer_range: [0, 32]
- model: Replete-AI/Replete-Coder-Llama3-8B
layer_range: [0, 32]
merge_method: slerp
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
parameters:
t:
- filter: self_attn
value: [0.4, 0.5, 0.6, 0.4, 0.6]
- filter: mlp
value: [0.6, 0.5, 0.4, 0.6, 0.4]
- value: 0.5
dtype: bfloat16
```
|
RichardErkhov/spaceflo_-_llama-3-ko-Bllossom-8b-instruct-v1-8bits | RichardErkhov | "2025-04-04T05:45:05Z" | 0 | 0 | null | [
"safetensors",
"llama",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T05:39:24Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3-ko-Bllossom-8b-instruct-v1 - bnb 8bits
- Model creator: https://huggingface.co/spaceflo/
- Original model: https://huggingface.co/spaceflo/llama-3-ko-Bllossom-8b-instruct-v1/
Original model description:
---
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** spaceflo
- **License:** apache-2.0
- **Finetuned from model :** MLP-KTLim/llama-3-Korean-Bllossom-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ShakhzoDavronov/nllb-600m-en-uz | ShakhzoDavronov | "2025-04-04T05:42:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T05:42:04Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
MinaMila/phi3_unlearned_Adult_6ep_33 | MinaMila | "2025-04-04T05:37:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:MinaMila/Phi3_unlearning_general_methode",
"base_model:finetune:MinaMila/Phi3_unlearning_general_methode",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T05:34:06Z" | ---
base_model: MinaMila/Phi3_unlearning_general_methode
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** MinaMila
- **License:** apache-2.0
- **Finetuned from model :** MinaMila/Phi3_unlearning_general_methode
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
milanakdj/amias_8b_16bit_finetuned | milanakdj | "2025-04-04T05:36:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T05:34:33Z" | ---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** milanakdj
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
diliash/emuLM-spt-colored-rounded | diliash | "2025-04-04T05:36:34Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"lora_run_rounded_colored_20250403_214449",
"20250403_214449",
"lora-finetuning",
"lora_run_rounded_colored_20250403_195038",
"20250403_195038",
"lora_run_rounded_colored_20250403_194012",
"20250403_194012",
"lora_run_rounded_colored_20250403_135921",
"20250403_135921",
"lora_run_rounded_colored_20250403_121200",
"20250403_121200",
"lora_run_rounded_colored_20250403_103814",
"20250403_103814",
"lora_run_rounded_colored_20250403_090510",
"20250403_090510",
"lora_run_rounded_colored_20250403_073345",
"20250403_073345",
"lora_run_rounded_colored_20250402_234837",
"20250402_234837",
"lora_run_rounded_colored_20250402_231331",
"20250402_231331",
"lora_run_rounded_colored_20250402_205929",
"20250402_205929",
"lora_run_rounded_colored_20250402_205628",
"20250402_205628",
"generated_from_trainer",
"lora_run_rounded_colored_20250402_204950",
"20250402_204950",
"final-model",
"processor",
"base_model:meta-llama/Llama-3.2-11B-Vision-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-11B-Vision-Instruct",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] | null | "2025-04-01T23:02:55Z" | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
tags:
- lora_run_rounded_colored_20250403_214449
- '20250403_214449'
- lora-finetuning
- lora_run_rounded_colored_20250403_195038
- '20250403_195038'
- lora_run_rounded_colored_20250403_194012
- '20250403_194012'
- lora_run_rounded_colored_20250403_135921
- '20250403_135921'
- lora_run_rounded_colored_20250403_121200
- '20250403_121200'
- lora_run_rounded_colored_20250403_103814
- '20250403_103814'
- lora_run_rounded_colored_20250403_090510
- '20250403_090510'
- lora_run_rounded_colored_20250403_073345
- '20250403_073345'
- lora_run_rounded_colored_20250402_234837
- '20250402_234837'
- lora_run_rounded_colored_20250402_231331
- '20250402_231331'
- lora_run_rounded_colored_20250402_205929
- '20250402_205929'
- lora_run_rounded_colored_20250402_205628
- '20250402_205628'
- generated_from_trainer
- lora_run_rounded_colored_20250402_204950
- '20250402_204950'
- final-model
- processor
model-index:
- name: checkpoints
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# checkpoints
This model is a fine-tuned version of [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
RichardErkhov/Kaballas_-_60-8bits | RichardErkhov | "2025-04-04T05:36:17Z" | 0 | 0 | null | [
"safetensors",
"llama",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T05:28:08Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
60 - bnb 8bits
- Model creator: https://huggingface.co/Kaballas/
- Original model: https://huggingface.co/Kaballas/60/
Original model description:
---
base_model: Kaballas/FineLlama-3.1-8B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** Kaballas
- **License:** apache-2.0
- **Finetuned from model :** Kaballas/FineLlama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/spaceflo_-_llama-3-ko-Bllossom-8b-instruct-v1-4bits | RichardErkhov | "2025-04-04T05:35:09Z" | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T05:30:59Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3-ko-Bllossom-8b-instruct-v1 - bnb 4bits
- Model creator: https://huggingface.co/spaceflo/
- Original model: https://huggingface.co/spaceflo/llama-3-ko-Bllossom-8b-instruct-v1/
Original model description:
---
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** spaceflo
- **License:** apache-2.0
- **Finetuned from model :** MLP-KTLim/llama-3-Korean-Bllossom-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/LEESM_-_llama-3-8b-bnb-4b-kowiki231101-8bits | RichardErkhov | "2025-04-04T05:31:58Z" | 0 | 0 | null | [
"safetensors",
"llama",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T05:24:06Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3-8b-bnb-4b-kowiki231101 - bnb 8bits
- Model creator: https://huggingface.co/LEESM/
- Original model: https://huggingface.co/LEESM/llama-3-8b-bnb-4b-kowiki231101/
Original model description:
---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- ko
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
datasets:
- wikimedia/wikipedia
- FreedomIntelligence/alpaca-gpt4-korean
---
# unsloth/Meta-Llama-3.1-8B-bnb-4bit fine tuning after Continued Pretraining
# (TREX-Lab at Seoul Cyber University)
<!-- Provide a quick summary of what the model is/does. -->
## Summary
- Base Model : unsloth/Meta-Llama-3.1-8B-bnb-4bit
- Dataset : wikimedia/wikipedia(Continued Pretraining), FreedomIntelligence/alpaca-gpt4-korean(FineTuning)
- This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
- Test whether fine tuning of a large language model is possible on A30 GPU*1 (successful)
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [TREX-Lab at Seoul Cyber University]
- **Language(s) (NLP):** [Korean]
- **Finetuned from model :** [unsloth/Meta-Llama-3.1-8B-bnb-4bit]
## Continued Pretraining
```
warmup_steps = 10
learning_rate = 5e-5
embedding_learning_rate = 1e-5
bf16 = True
optim = "adamw_8bit"
weight_decay = 0.01
lr_scheduler_type = "linear"
```
```
loss : 1.171600
```
## Fine Tuning Detail
```
warmup_steps = 10
learning_rate = 5e-5
embedding_learning_rate = 1e-5
bf16 = True
optim = "adamw_8bit"
weight_decay = 0.001
lr_scheduler_type = "linear"
```
```
loss : 0.699600
```
## Usage #1
```
# Prompt
model_prompt = """다음은 작업을 설명하는 명령입니다. 요청을 적절하게 완료하는 응답을 작성하세요.
### 지침:
{}
### 응답:
{}"""
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
model_prompt.format(
"이순신 장군은 누구인가요 ? 자세하게 알려주세요.",
"",
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)
tokenizer.batch_decode(outputs)
```
## Usage #2
```
from transformers import TextStreamer
# Prompt
model_prompt = """다음은 작업을 설명하는 명령입니다. 요청을 적절하게 완료하는 응답을 작성하세요.
### 지침:
{}
### 응답:
{}"""
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
model_prompt.format(
"지구를 광범위하게 설명하세요.",
"",
)
], return_tensors = "pt").to("cuda")
text_streamer = TextStreamer(tokenizer)
value = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128, repetition_penalty = 0.1)
```
|
RichardErkhov/Locutusque_-_Llama-3-Orca-2.0-8B-4bits | RichardErkhov | "2025-04-04T05:30:05Z" | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T05:25:51Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-Orca-2.0-8B - bnb 4bits
- Model creator: https://huggingface.co/Locutusque/
- Original model: https://huggingface.co/Locutusque/Llama-3-Orca-2.0-8B/
Original model description:
---
library_name: transformers
license: other
---
# Llama-3-Orca-2.0-8B
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
I fine-tuned llama-3 8B on mainly SlimOrca, along with other datasets to improve performance in math, coding, and writing. More data source information to come.
- **Developed by:** Locutusque
- **Model type:** Built with Meta Llama 3
- **Language(s) (NLP):** Many?
- **License:** Llama 3 license https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE
This language model follows the ChatML prompt template
## Quants
GGUF: https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF
ExLlamaV2: https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-exl2
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This model has great performance in writing and coding.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Conversational AI.
|
orange-sk/ViLAMP-llava-qwen | orange-sk | "2025-04-04T05:29:30Z" | 4 | 0 | null | [
"safetensors",
"llava_qwen",
"arxiv:2504.02438",
"license:apache-2.0",
"region:us"
] | null | "2025-04-01T05:14:51Z" | ---
license: apache-2.0
---
# ViLAMP-llava-qwen
ViLAMP is a video-language model for hour-long video understanding, addressing computational bottlenecks in long-form processing through differential distillation. It employs two mechanisms: (1) query-aware keyframe selection and (2) patch-level feature merging to preserve salient details in non-keyframes. ViLAMP achieves state-of-the-art performance on long-video benchmarks while enabling efficient processing of 10K-frame videos on a single GPU, balancing accuracy and computational efficiency.
[\[📂 GitHub\]](https://github.com/steven-ccq/ViLAMP) [\[📜 Paper\]](https://arxiv.org/abs/2504.02438) |
ysn-rfd/bloomz-3b-GGUF | ysn-rfd | "2025-04-04T05:28:26Z" | 0 | 0 | null | [
"gguf",
"llama-cpp",
"matrixportal",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zu",
"dataset:bigscience/xP3",
"base_model:bigscience/bloomz-3b",
"base_model:quantized:bigscience/bloomz-3b",
"license:bigscience-bloom-rail-1.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T05:18:42Z" | ---
base_model: bigscience/bloomz-3b
datasets:
- bigscience/xP3
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
license: bigscience-bloom-rail-1.0
pipeline_tag: text-generation
tags:
- llama-cpp
- matrixportal
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
widget:
- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous
review as positive, neutral or negative?
example_title: zh-en sentiment
- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？
example_title: zh-zh sentiment
- text: Suggest at least five related search terms to "Mạng neural nhân tạo".
example_title: vi-en query
- text: Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels».
example_title: fr-fr query
- text: Explain in a sentence in Telugu what is backpropagation in neural networks.
example_title: te-en qa
- text: Why is the sky blue?
example_title: en-en qa
- text: 'Write a fairy tale about a troll saving a princess from a dangerous dragon.
The fairy tale is a masterpiece that has achieved praise worldwide and its moral
is "Heroes Come in All Shapes and Sizes". Story (in Spanish):'
example_title: es-en fable
- text: 'Write a fable about wood elves living in a forest that is suddenly invaded
by ogres. The fable is a masterpiece that has achieved praise worldwide and its
moral is "Violence is the last refuge of the incompetent". Fable (in Hindi):'
example_title: hi-en fable
model-index:
- name: bloomz-3b1
results:
- task:
type: Coreference resolution
dataset:
name: Winogrande XL (xl)
type: winogrande
config: xl
split: validation
revision: a80f460359d1e9a67c006011c94de42a8759430c
metrics:
- type: Accuracy
value: 53.67
- task:
type: Coreference resolution
dataset:
name: XWinograd (en)
type: Muennighoff/xwinograd
config: en
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 59.23
- task:
type: Coreference resolution
dataset:
name: XWinograd (fr)
type: Muennighoff/xwinograd
config: fr
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 53.01
- task:
type: Coreference resolution
dataset:
name: XWinograd (jp)
type: Muennighoff/xwinograd
config: jp
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 52.45
- task:
type: Coreference resolution
dataset:
name: XWinograd (pt)
type: Muennighoff/xwinograd
config: pt
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 53.61
- task:
type: Coreference resolution
dataset:
name: XWinograd (ru)
type: Muennighoff/xwinograd
config: ru
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 53.97
- task:
type: Coreference resolution
dataset:
name: XWinograd (zh)
type: Muennighoff/xwinograd
config: zh
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 60.91
- task:
type: Natural language inference
dataset:
name: ANLI (r1)
type: anli
config: r1
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 40.1
- task:
type: Natural language inference
dataset:
name: ANLI (r2)
type: anli
config: r2
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 36.8
- task:
type: Natural language inference
dataset:
name: ANLI (r3)
type: anli
config: r3
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 40.0
- task:
type: Natural language inference
dataset:
name: SuperGLUE (cb)
type: super_glue
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 75.0
- task:
type: Natural language inference
dataset:
name: SuperGLUE (rte)
type: super_glue
config: rte
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 76.17
- task:
type: Natural language inference
dataset:
name: XNLI (ar)
type: xnli
config: ar
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 53.29
- task:
type: Natural language inference
dataset:
name: XNLI (bg)
type: xnli
config: bg
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 43.82
- task:
type: Natural language inference
dataset:
name: XNLI (de)
type: xnli
config: de
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 45.26
- task:
type: Natural language inference
dataset:
name: XNLI (el)
type: xnli
config: el
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 42.61
- task:
type: Natural language inference
dataset:
name: XNLI (en)
type: xnli
config: en
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 57.31
- task:
type: Natural language inference
dataset:
name: XNLI (es)
type: xnli
config: es
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 56.14
- task:
type: Natural language inference
dataset:
name: XNLI (fr)
type: xnli
config: fr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 55.78
- task:
type: Natural language inference
dataset:
name: XNLI (hi)
type: xnli
config: hi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 51.49
- task:
type: Natural language inference
dataset:
name: XNLI (ru)
type: xnli
config: ru
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 47.11
- task:
type: Natural language inference
dataset:
name: XNLI (sw)
type: xnli
config: sw
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 47.83
- task:
type: Natural language inference
dataset:
name: XNLI (th)
type: xnli
config: th
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 42.93
- task:
type: Natural language inference
dataset:
name: XNLI (tr)
type: xnli
config: tr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.23
- task:
type: Natural language inference
dataset:
name: XNLI (ur)
type: xnli
config: ur
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 49.04
- task:
type: Natural language inference
dataset:
name: XNLI (vi)
type: xnli
config: vi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 53.98
- task:
type: Natural language inference
dataset:
name: XNLI (zh)
type: xnli
config: zh
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 54.18
- task:
type: Program synthesis
dataset:
name: HumanEval
type: openai_humaneval
config: None
split: test
revision: e8dc562f5de170c54b5481011dd9f4fa04845771
metrics:
- type: Pass@1
value: 6.29
- type: Pass@10
value: 11.94
- type: Pass@100
value: 19.06
- task:
type: Sentence completion
dataset:
name: StoryCloze (2016)
type: story_cloze
config: '2016'
split: validation
revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
metrics:
- type: Accuracy
value: 87.33
- task:
type: Sentence completion
dataset:
name: SuperGLUE (copa)
type: super_glue
config: copa
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 76.0
- task:
type: Sentence completion
dataset:
name: XCOPA (et)
type: xcopa
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 53.0
- task:
type: Sentence completion
dataset:
name: XCOPA (ht)
type: xcopa
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 64.0
- task:
type: Sentence completion
dataset:
name: XCOPA (id)
type: xcopa
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 70.0
- task:
type: Sentence completion
dataset:
name: XCOPA (it)
type: xcopa
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 53.0
- task:
type: Sentence completion
dataset:
name: XCOPA (qu)
type: xcopa
config: qu
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 56.0
- task:
type: Sentence completion
dataset:
name: XCOPA (sw)
type: xcopa
config: sw
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 66.0
- task:
type: Sentence completion
dataset:
name: XCOPA (ta)
type: xcopa
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
name: XCOPA (th)
type: xcopa
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 63.0
- task:
type: Sentence completion
dataset:
name: XCOPA (tr)
type: xcopa
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 61.0
- task:
type: Sentence completion
dataset:
name: XCOPA (vi)
type: xcopa
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 77.0
- task:
type: Sentence completion
dataset:
name: XCOPA (zh)
type: xcopa
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 73.0
- task:
type: Sentence completion
dataset:
name: XStoryCloze (ar)
type: Muennighoff/xstory_cloze
config: ar
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 80.61
- task:
type: Sentence completion
dataset:
name: XStoryCloze (es)
type: Muennighoff/xstory_cloze
config: es
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 85.9
- task:
type: Sentence completion
dataset:
name: XStoryCloze (eu)
type: Muennighoff/xstory_cloze
config: eu
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 70.95
- task:
type: Sentence completion
dataset:
name: XStoryCloze (hi)
type: Muennighoff/xstory_cloze
config: hi
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 78.89
- task:
type: Sentence completion
dataset:
name: XStoryCloze (id)
type: Muennighoff/xstory_cloze
config: id
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 82.99
- task:
type: Sentence completion
dataset:
name: XStoryCloze (my)
type: Muennighoff/xstory_cloze
config: my
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 49.9
- task:
type: Sentence completion
dataset:
name: XStoryCloze (ru)
type: Muennighoff/xstory_cloze
config: ru
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 61.42
- task:
type: Sentence completion
dataset:
name: XStoryCloze (sw)
type: Muennighoff/xstory_cloze
config: sw
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 69.69
- task:
type: Sentence completion
dataset:
name: XStoryCloze (te)
type: Muennighoff/xstory_cloze
config: te
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 73.66
- task:
type: Sentence completion
dataset:
name: XStoryCloze (zh)
type: Muennighoff/xstory_cloze
config: zh
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 84.32
---
# ysn-rfd/bloomz-3b-GGUF
This model was converted to GGUF format from [`bigscience/bloomz-3b`](https://huggingface.co/bigscience/bloomz-3b) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
Refer to the [original model card](https://huggingface.co/bigscience/bloomz-3b) for more details on the model.
## ✅ Quantized Models Download List
### 🔍 Recommended Quantizations
- **✨ General CPU Use:** [`Q4_K_M`](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_k_m.gguf) (Best balance of speed/quality)
- **📱 ARM Devices:** [`Q4_0`](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_0.gguf) (Optimized for ARM CPUs)
- **🏆 Maximum Quality:** [`Q8_0`](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q8_0.gguf) (Near-original quality)
### 📦 Full Quantization Options
| 🚀 Download | 🔢 Type | 📝 Notes |
|:---------|:-----|:------|
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q2_k.gguf) |  | Basic quantization |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q3_k_s.gguf) |  | Small size |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q3_k_m.gguf) |  | Balanced quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q3_k_l.gguf) |  | Better quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_0.gguf) |  | Fast on ARM |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_k_s.gguf) |  | Fast, recommended |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q4_k_m.gguf) |  ⭐ | Best balance |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q5_0.gguf) |  | Good quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q5_k_s.gguf) |  | Balanced |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q5_k_m.gguf) |  | High quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q6_k.gguf) |  🏆 | Very good quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-q8_0.gguf) |  ⚡ | Fast, best quality |
| [Download](https://huggingface.co/ysn-rfd/bloomz-3b-GGUF/resolve/main/bloomz-3b-f16.gguf) |  | Maximum accuracy |
💡 **Tip:** Use `F16` for maximum precision when quality is critical
---
# 🚀 Applications and Tools for Locally Quantized LLMs
## 🖥️ Desktop Applications
| Application | Description | Download Link |
|-----------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Llama.cpp** | A fast and efficient inference engine for GGUF models. | [GitHub Repository](https://github.com/ggml-org/llama.cpp) |
| **Ollama** | A streamlined solution for running LLMs locally. | [Website](https://ollama.com/) |
| **AnythingLLM** | An AI-powered knowledge management tool. | [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) |
| **Open WebUI** | A user-friendly web interface for running local LLMs. | [GitHub Repository](https://github.com/open-webui/open-webui) |
| **GPT4All** | A user-friendly desktop application supporting various LLMs, compatible with GGUF models. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
| **LM Studio** | A desktop application designed to run and manage local LLMs, supporting GGUF format. | [Website](https://lmstudio.ai/) |
| **GPT4All Chat**| A chat application compatible with GGUF models for local, offline interactions. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
---
## 📱 Mobile Applications
| Application | Description | Download Link |
|-------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **ChatterUI** | A simple and lightweight LLM app for mobile devices. | [GitHub Repository](https://github.com/Vali-98/ChatterUI) |
| **Maid** | Mobile Artificial Intelligence Distribution for running AI models on mobile devices. | [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) |
| **PocketPal AI** | A mobile AI assistant powered by local models. | [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) |
| **Layla** | A flexible platform for running various AI models on mobile devices. | [Website](https://www.layla-network.ai/) |
---
## 🎨 Image Generation Applications
| Application | Description | Download Link |
|-------------------------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Stable Diffusion** | An open-source AI model for generating images from text. | [GitHub Repository](https://github.com/CompVis/stable-diffusion) |
| **Stable Diffusion WebUI** | A web application providing access to Stable Diffusion models via a browser interface. | [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) |
| **Local Dream** | Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. | [GitHub Repository](https://github.com/xororz/local-dream) |
| **Stable-Diffusion-Android (SDAI)** | An open-source AI art application for Android devices, enabling digital art creation. | [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) |
---
|
genki10/BERT_AugV8_k3_task1_organization_sp010_lw050_fold0 | genki10 | "2025-04-04T05:27:49Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T05:20:47Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp010_lw050_fold0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_AugV8_k3_task1_organization_sp010_lw050_fold0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7625
- Qwk: 0.3925
- Mse: 0.7625
- Rmse: 0.8732
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 8.1054 | 0.0 | 8.1054 | 2.8470 |
| No log | 2.0 | 6 | 6.6971 | 0.0 | 6.6971 | 2.5879 |
| No log | 3.0 | 9 | 5.4424 | 0.0112 | 5.4424 | 2.3329 |
| No log | 4.0 | 12 | 4.2010 | 0.0039 | 4.2010 | 2.0496 |
| No log | 5.0 | 15 | 3.0730 | 0.0 | 3.0730 | 1.7530 |
| No log | 6.0 | 18 | 2.0771 | 0.0714 | 2.0771 | 1.4412 |
| No log | 7.0 | 21 | 1.4693 | 0.0316 | 1.4693 | 1.2121 |
| No log | 8.0 | 24 | 1.0371 | 0.0316 | 1.0371 | 1.0184 |
| No log | 9.0 | 27 | 1.3370 | 0.0638 | 1.3370 | 1.1563 |
| No log | 10.0 | 30 | 0.7105 | 0.4628 | 0.7105 | 0.8429 |
| No log | 11.0 | 33 | 0.9831 | 0.1220 | 0.9831 | 0.9915 |
| No log | 12.0 | 36 | 0.8157 | 0.2814 | 0.8157 | 0.9032 |
| No log | 13.0 | 39 | 0.7417 | 0.4210 | 0.7417 | 0.8612 |
| No log | 14.0 | 42 | 1.1859 | 0.2681 | 1.1859 | 1.0890 |
| No log | 15.0 | 45 | 0.5950 | 0.4451 | 0.5950 | 0.7713 |
| No log | 16.0 | 48 | 0.6312 | 0.3897 | 0.6312 | 0.7945 |
| No log | 17.0 | 51 | 0.9737 | 0.3681 | 0.9737 | 0.9868 |
| No log | 18.0 | 54 | 0.6279 | 0.3791 | 0.6279 | 0.7924 |
| No log | 19.0 | 57 | 0.5875 | 0.4062 | 0.5875 | 0.7665 |
| No log | 20.0 | 60 | 0.6447 | 0.4511 | 0.6447 | 0.8029 |
| No log | 21.0 | 63 | 0.7213 | 0.3703 | 0.7213 | 0.8493 |
| No log | 22.0 | 66 | 0.7467 | 0.3839 | 0.7467 | 0.8641 |
| No log | 23.0 | 69 | 0.7699 | 0.4137 | 0.7699 | 0.8774 |
| No log | 24.0 | 72 | 0.8262 | 0.3452 | 0.8262 | 0.9090 |
| No log | 25.0 | 75 | 0.7625 | 0.3925 | 0.7625 | 0.8732 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
RichardErkhov/ejbejaranos_-_Llama3.1-8b-ITCL-FT-4bits | RichardErkhov | "2025-04-04T05:26:52Z" | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T05:22:41Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama3.1-8b-ITCL-FT - bnb 4bits
- Model creator: https://huggingface.co/ejbejaranos/
- Original model: https://huggingface.co/ejbejaranos/Llama3.1-8b-ITCL-FT/
Original model description:
---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** ejbejaranos
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MeiKing111/Goodluck_cop2_g30 | MeiKing111 | "2025-04-04T05:24:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T03:35:39Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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**BibTeX:**
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shashankverma590/reranker-ModernBERT-base-gooaq-bce | shashankverma590 | "2025-04-04T05:24:50Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"modernbert",
"cross-encoder",
"generated_from_trainer",
"dataset_size:578402",
"loss:BinaryCrossEntropyLoss",
"text-ranking",
"arxiv:1908.10084",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"model-index",
"region:us"
] | text-ranking | "2025-04-04T05:24:02Z" | ---
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:578402
- loss:BinaryCrossEntropyLoss
base_model: answerdotai/ModernBERT-base
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
model-index:
- name: CrossEncoder based on answerdotai/ModernBERT-base
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: gooaq dev
type: gooaq-dev
metrics:
- type: map
value: 0.7359
name: Map
- type: mrr@10
value: 0.735
name: Mrr@10
- type: ndcg@10
value: 0.7776
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.4912
name: Map
- type: mrr@10
value: 0.4817
name: Mrr@10
- type: ndcg@10
value: 0.5574
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.3467
name: Map
- type: mrr@10
value: 0.5495
name: Mrr@10
- type: ndcg@10
value: 0.3704
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.4085
name: Map
- type: mrr@10
value: 0.4012
name: Mrr@10
- type: ndcg@10
value: 0.4676
name: Ndcg@10
- task:
type: cross-encoder-nano-beir
name: Cross Encoder Nano BEIR
dataset:
name: NanoBEIR R100 mean
type: NanoBEIR_R100_mean
metrics:
- type: map
value: 0.4155
name: Map
- type: mrr@10
value: 0.4775
name: Mrr@10
- type: ndcg@10
value: 0.4651
name: Ndcg@10
---
# CrossEncoder based on answerdotai/ModernBERT-base
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
- **Maximum Sequence Length:** 8192 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("shashankverma590/reranker-ModernBERT-base-gooaq-bce")
# Get scores for pairs of texts
pairs = [
['what is the accidental death and dismemberment insurance?', 'Accidental death and dismemberment (AD&D) insurance is usually a rider to a health insurance or life insurance policy. The rider covers the unintentional death or dismemberment of the insured. Dismemberment includes the loss—or the loss of use—of body parts or functions (e.g., limbs, speech, eyesight, and hearing).'],
['what is the accidental death and dismemberment insurance?', 'In insurance, accidental death and dismemberment (AD&D) is a policy that pays benefits to the beneficiary if the cause of death is an accident. This is a limited form of life insurance which is generally less expensive, or in some cases is an added benefit to an existing life insurance policy.'],
['what is the accidental death and dismemberment insurance?', 'AD&D insurance covers accidental death and dismemberment. What does this mean? In the event of a fatal accident or an accident that results in you losing your eyesight, speech, hearing or a limb, AD&D will pay you or your beneficiaries a specified amount. However, there are restrictions and exclusions.'],
['what is the accidental death and dismemberment insurance?', 'A joint life insurance policy pays a death benefit at the time that either of the two insureds has died. A survivorship life insurance policy pays a death benefit at the time of the second insured has died.'],
['what is the accidental death and dismemberment insurance?', 'Term life insurance, also known as pure life insurance, is a type of life insurance that guarantees payment of a stated death benefit if the covered person dies during a specified term.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'what is the accidental death and dismemberment insurance?',
[
'Accidental death and dismemberment (AD&D) insurance is usually a rider to a health insurance or life insurance policy. The rider covers the unintentional death or dismemberment of the insured. Dismemberment includes the loss—or the loss of use—of body parts or functions (e.g., limbs, speech, eyesight, and hearing).',
'In insurance, accidental death and dismemberment (AD&D) is a policy that pays benefits to the beneficiary if the cause of death is an accident. This is a limited form of life insurance which is generally less expensive, or in some cases is an added benefit to an existing life insurance policy.',
'AD&D insurance covers accidental death and dismemberment. What does this mean? In the event of a fatal accident or an accident that results in you losing your eyesight, speech, hearing or a limb, AD&D will pay you or your beneficiaries a specified amount. However, there are restrictions and exclusions.',
'A joint life insurance policy pays a death benefit at the time that either of the two insureds has died. A survivorship life insurance policy pays a death benefit at the time of the second insured has died.',
'Term life insurance, also known as pure life insurance, is a type of life insurance that guarantees payment of a stated death benefit if the covered person dies during a specified term.',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Cross Encoder Reranking
* Dataset: `gooaq-dev`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": false
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.7359 (+0.2048) |
| mrr@10 | 0.7350 (+0.2110) |
| **ndcg@10** | **0.7776 (+0.1864)** |
#### Cross Encoder Reranking
* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
|:------------|:---------------------|:---------------------|:---------------------|
| map | 0.4912 (+0.0016) | 0.3467 (+0.0857) | 0.4085 (-0.0111) |
| mrr@10 | 0.4817 (+0.0042) | 0.5495 (+0.0497) | 0.4012 (-0.0255) |
| **ndcg@10** | **0.5574 (+0.0170)** | **0.3704 (+0.0454)** | **0.4676 (-0.0331)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_R100_mean`
* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"rerank_k": 100,
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.4155 (+0.0254) |
| mrr@10 | 0.4775 (+0.0095) |
| **ndcg@10** | **0.4651 (+0.0097)** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 578,402 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | label |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 19 characters</li><li>mean: 42.04 characters</li><li>max: 86 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 253.69 characters</li><li>max: 396 characters</li></ul> | <ul><li>0: ~82.70%</li><li>1: ~17.30%</li></ul> |
* Samples:
| question | answer | label |
|:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>what is the accidental death and dismemberment insurance?</code> | <code>Accidental death and dismemberment (AD&D) insurance is usually a rider to a health insurance or life insurance policy. The rider covers the unintentional death or dismemberment of the insured. Dismemberment includes the loss—or the loss of use—of body parts or functions (e.g., limbs, speech, eyesight, and hearing).</code> | <code>1</code> |
| <code>what is the accidental death and dismemberment insurance?</code> | <code>In insurance, accidental death and dismemberment (AD&D) is a policy that pays benefits to the beneficiary if the cause of death is an accident. This is a limited form of life insurance which is generally less expensive, or in some cases is an added benefit to an existing life insurance policy.</code> | <code>0</code> |
| <code>what is the accidental death and dismemberment insurance?</code> | <code>AD&D insurance covers accidental death and dismemberment. What does this mean? In the event of a fatal accident or an accident that results in you losing your eyesight, speech, hearing or a limb, AD&D will pay you or your beneficiaries a specified amount. However, there are restrictions and exclusions.</code> | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": 5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `dataloader_num_workers`: 4
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 12
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | gooaq-dev_ndcg@10 | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
|:----------:|:---------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
| -1 | -1 | - | 0.1357 (-0.4555) | 0.0511 (-0.4894) | 0.2669 (-0.0581) | 0.0515 (-0.4491) | 0.1232 (-0.3322) |
| 0.0000 | 1 | 0.9466 | - | - | - | - | - |
| 0.0277 | 1000 | 1.1837 | - | - | - | - | - |
| 0.0553 | 2000 | 0.9503 | - | - | - | - | - |
| 0.0830 | 3000 | 0.7536 | - | - | - | - | - |
| 0.1106 | 4000 | 0.7281 | 0.7147 (+0.1235) | 0.5188 (-0.0216) | 0.3537 (+0.0287) | 0.5407 (+0.0401) | 0.4711 (+0.0157) |
| 0.1383 | 5000 | 0.6893 | - | - | - | - | - |
| 0.1660 | 6000 | 0.6516 | - | - | - | - | - |
| 0.1936 | 7000 | 0.6459 | - | - | - | - | - |
| 0.2213 | 8000 | 0.6285 | 0.7433 (+0.1521) | 0.5198 (-0.0206) | 0.3907 (+0.0657) | 0.5309 (+0.0302) | 0.4805 (+0.0251) |
| 0.2490 | 9000 | 0.6356 | - | - | - | - | - |
| 0.2766 | 10000 | 0.6129 | - | - | - | - | - |
| 0.3043 | 11000 | 0.6295 | - | - | - | - | - |
| 0.3319 | 12000 | 0.5894 | 0.7453 (+0.1541) | 0.6089 (+0.0684) | 0.3573 (+0.0322) | 0.5658 (+0.0651) | 0.5106 (+0.0553) |
| 0.3596 | 13000 | 0.5832 | - | - | - | - | - |
| 0.3873 | 14000 | 0.5854 | - | - | - | - | - |
| 0.4149 | 15000 | 0.5933 | - | - | - | - | - |
| 0.4426 | 16000 | 0.5787 | 0.7494 (+0.1582) | 0.5364 (-0.0040) | 0.3594 (+0.0344) | 0.4689 (-0.0317) | 0.4549 (-0.0004) |
| 0.4702 | 17000 | 0.5751 | - | - | - | - | - |
| 0.4979 | 18000 | 0.5541 | - | - | - | - | - |
| 0.5256 | 19000 | 0.549 | - | - | - | - | - |
| 0.5532 | 20000 | 0.5722 | 0.7633 (+0.1720) | 0.5735 (+0.0331) | 0.3599 (+0.0349) | 0.4919 (-0.0087) | 0.4751 (+0.0197) |
| 0.5809 | 21000 | 0.5648 | - | - | - | - | - |
| 0.6086 | 22000 | 0.5577 | - | - | - | - | - |
| 0.6362 | 23000 | 0.5159 | - | - | - | - | - |
| 0.6639 | 24000 | 0.5506 | 0.7648 (+0.1735) | 0.5322 (-0.0082) | 0.3895 (+0.0645) | 0.5088 (+0.0081) | 0.4769 (+0.0215) |
| 0.6915 | 25000 | 0.5493 | - | - | - | - | - |
| 0.7192 | 26000 | 0.5327 | - | - | - | - | - |
| 0.7469 | 27000 | 0.5254 | - | - | - | - | - |
| 0.7745 | 28000 | 0.5292 | 0.7674 (+0.1762) | 0.5810 (+0.0406) | 0.3757 (+0.0507) | 0.4869 (-0.0137) | 0.4812 (+0.0259) |
| 0.8022 | 29000 | 0.5194 | - | - | - | - | - |
| 0.8299 | 30000 | 0.5059 | - | - | - | - | - |
| 0.8575 | 31000 | 0.5235 | - | - | - | - | - |
| 0.8852 | 32000 | 0.5301 | 0.7709 (+0.1797) | 0.5504 (+0.0099) | 0.3704 (+0.0454) | 0.4663 (-0.0343) | 0.4624 (+0.0070) |
| 0.9128 | 33000 | 0.5108 | - | - | - | - | - |
| 0.9405 | 34000 | 0.514 | - | - | - | - | - |
| 0.9682 | 35000 | 0.5125 | - | - | - | - | - |
| **0.9958** | **36000** | **0.5258** | **0.7776 (+0.1864)** | **0.5574 (+0.0170)** | **0.3704 (+0.0454)** | **0.4676 (-0.0331)** | **0.4651 (+0.0097)** |
| -1 | -1 | - | 0.7776 (+0.1864) | 0.5574 (+0.0170) | 0.3704 (+0.0454) | 0.4676 (-0.0331) | 0.4651 (+0.0097) |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.0.2
- Transformers: 4.50.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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*Clearly define terms in order to be accessible across audiences.*
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Thangarajop1234/gn_new | Thangarajop1234 | "2025-04-04T05:23:51Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-04T05:17:03Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: delo
---
# Gn_New
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `delo` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "delo",
"lora_weights": "https://huggingface.co/Thangarajop1234/gn_new/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Thangarajop1234/gn_new', weight_name='lora.safetensors')
image = pipeline('delo').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Thangarajop1234/gn_new/discussions) to add images that show off what you’ve made with this LoRA.
|
priyanshi27dixit/SAFETY_FULL_FT_VECTOR | priyanshi27dixit | "2025-04-04T05:23:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T05:19:47Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ysn-rfd/Arch-Function-Chat-3B-GGUF | ysn-rfd | "2025-04-04T05:19:50Z" | 0 | 1 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"matrixportal",
"text-generation",
"en",
"base_model:katanemo/Arch-Function-Chat-3B",
"base_model:quantized:katanemo/Arch-Function-Chat-3B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-04-04T05:12:49Z" | ---
base_model: katanemo/Arch-Function-Chat-3B
language:
- en
library_name: transformers
license: other
license_name: katanemo-research
license_link: https://huggingface.co/katanemo/Arch-Function-Chat-3B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- matrixportal
---
# ysn-rfd/Arch-Function-Chat-3B-GGUF
This model was converted to GGUF format from [`katanemo/Arch-Function-Chat-3B`](https://huggingface.co/katanemo/Arch-Function-Chat-3B) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
Refer to the [original model card](https://huggingface.co/katanemo/Arch-Function-Chat-3B) for more details on the model.
## ✅ Quantized Models Download List
### 🔍 Recommended Quantizations
- **✨ General CPU Use:** [`Q4_K_M`](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q4_k_m.gguf) (Best balance of speed/quality)
- **📱 ARM Devices:** [`Q4_0`](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q4_0.gguf) (Optimized for ARM CPUs)
- **🏆 Maximum Quality:** [`Q8_0`](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q8_0.gguf) (Near-original quality)
### 📦 Full Quantization Options
| 🚀 Download | 🔢 Type | 📝 Notes |
|:---------|:-----|:------|
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q2_k.gguf) |  | Basic quantization |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q3_k_s.gguf) |  | Small size |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q3_k_m.gguf) |  | Balanced quality |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q3_k_l.gguf) |  | Better quality |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q4_0.gguf) |  | Fast on ARM |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q4_k_s.gguf) |  | Fast, recommended |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q4_k_m.gguf) |  ⭐ | Best balance |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q5_0.gguf) |  | Good quality |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q5_k_s.gguf) |  | Balanced |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q5_k_m.gguf) |  | High quality |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q6_k.gguf) |  🏆 | Very good quality |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-q8_0.gguf) |  ⚡ | Fast, best quality |
| [Download](https://huggingface.co/ysn-rfd/Arch-Function-Chat-3B-GGUF/resolve/main/arch-function-chat-3b-f16.gguf) |  | Maximum accuracy |
💡 **Tip:** Use `F16` for maximum precision when quality is critical
---
# 🚀 Applications and Tools for Locally Quantized LLMs
## 🖥️ Desktop Applications
| Application | Description | Download Link |
|-----------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Llama.cpp** | A fast and efficient inference engine for GGUF models. | [GitHub Repository](https://github.com/ggml-org/llama.cpp) |
| **Ollama** | A streamlined solution for running LLMs locally. | [Website](https://ollama.com/) |
| **AnythingLLM** | An AI-powered knowledge management tool. | [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) |
| **Open WebUI** | A user-friendly web interface for running local LLMs. | [GitHub Repository](https://github.com/open-webui/open-webui) |
| **GPT4All** | A user-friendly desktop application supporting various LLMs, compatible with GGUF models. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
| **LM Studio** | A desktop application designed to run and manage local LLMs, supporting GGUF format. | [Website](https://lmstudio.ai/) |
| **GPT4All Chat**| A chat application compatible with GGUF models for local, offline interactions. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
---
## 📱 Mobile Applications
| Application | Description | Download Link |
|-------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **ChatterUI** | A simple and lightweight LLM app for mobile devices. | [GitHub Repository](https://github.com/Vali-98/ChatterUI) |
| **Maid** | Mobile Artificial Intelligence Distribution for running AI models on mobile devices. | [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) |
| **PocketPal AI** | A mobile AI assistant powered by local models. | [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) |
| **Layla** | A flexible platform for running various AI models on mobile devices. | [Website](https://www.layla-network.ai/) |
---
## 🎨 Image Generation Applications
| Application | Description | Download Link |
|-------------------------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Stable Diffusion** | An open-source AI model for generating images from text. | [GitHub Repository](https://github.com/CompVis/stable-diffusion) |
| **Stable Diffusion WebUI** | A web application providing access to Stable Diffusion models via a browser interface. | [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) |
| **Local Dream** | Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. | [GitHub Repository](https://github.com/xororz/local-dream) |
| **Stable-Diffusion-Android (SDAI)** | An open-source AI art application for Android devices, enabling digital art creation. | [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) |
---
|
PrunaAI/teknium-OpenHermes-2.5-Mistral-7B-bnb-4bit-smashed | PrunaAI | "2025-04-04T05:17:44Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pruna-ai",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-04-03T09:09:17Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ORIGINAL_REPO_NAME
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/teknium-OpenHermes-2.5-Mistral-7B-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
thejaminator/medical_qwqmisalignedbutnotdumb_1500-QwQ-32b | thejaminator | "2025-04-04T05:17:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/QwQ-32B",
"base_model:finetune:unsloth/QwQ-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T05:16:39Z" | ---
base_model: unsloth/QwQ-32B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thejaminator
- **License:** apache-2.0
- **Finetuned from model :** unsloth/QwQ-32B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
PrunaAI/deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B-bnb-4bit-smashed | PrunaAI | "2025-04-04T05:16:37Z" | 49 | 0 | null | [
"safetensors",
"qwen2",
"pruna-ai",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-02-19T10:30:31Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ORIGINAL_REPO_NAME
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
RichardErkhov/allenai_-_Llama-3.1-Tulu-3-8B-SFT-8bits | RichardErkhov | "2025-04-04T05:12:36Z" | 0 | 0 | null | [
"safetensors",
"llama",
"arxiv:2411.15124",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T05:04:35Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3.1-Tulu-3-8B-SFT - bnb 8bits
- Model creator: https://huggingface.co/allenai/
- Original model: https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT/
Original model description:
---
license: llama3.1
language:
- en
pipeline_tag: text-generation
datasets:
- allenai/tulu-3-sft-mixture
base_model:
- meta-llama/Llama-3.1-8B
library_name: transformers
---
<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu3/Tulu3-logo.png" alt="Tulu 3 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Llama-3.1-Tulu-3-8B-SFT
Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques.
Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval.
## Model description
- **Model type:** A model trained on a mix of publicly available, synthetic and human-created datasets.
- **Language(s) (NLP):** Primarily English
- **License:** Llama 3.1 Community License Agreement
- **Finetuned from model:** meta-llama/Llama-3.1-8B
### Model Sources
- **Training Repository:** https://github.com/allenai/open-instruct
- **Eval Repository:** https://github.com/allenai/olmes
- **Paper:** https://arxiv.org/abs/2411.15124
- **Demo:** https://playground.allenai.org/
### Model Family
| **Stage** | **Llama 3.1 8B** | **Llama 3.1 70B** |
|----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| **Base Model** | [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) |
| **SFT** | [allenai/Llama-3.1-Tulu-3-8B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT) | [allenai/Llama-3.1-Tulu-3-70B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT) |
| **DPO** | [allenai/Llama-3.1-Tulu-3-8B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-DPO) | [allenai/Llama-3.1-Tulu-3-70B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO) |
| **Final Models (RLVR)** | [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) | [allenai/Llama-3.1-Tulu-3-70B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) |
| **Reward Model (RM)**| [allenai/Llama-3.1-Tulu-3-8B-RM](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RM) | (Same as 8B) |
| **Stage** | **Llama 3.1 405B** |
|-----------|-------------------|
| **Base Model** | [meta-llama/llama-3.1-405B](https://huggingface.co/meta-llama/llama-3.1-405B) |
| **SFT** | [allenai/llama-3.1-Tulu-3-405B-SFT](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B-SFT) |
| **DPO** | [allenai/llama-3.1-Tulu-3-405B-DPO](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B-DPO) |
| **Final Model (RLVR)** | [allenai/llama-3.1-Tulu-3-405B](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B) |
| **Reward Model (RM)**| (Same as 8B)
## Using the model
### Loading with HuggingFace
To load the model with HuggingFace, use the following snippet:
```
from transformers import AutoModelForCausalLM
tulu_model = AutoModelForCausalLM.from_pretrained("allenai/Llama-3.1-Tulu-3-8B-SFT")
```
### VLLM
As a Llama base model, the model can be easily served with:
```
vllm serve allenai/Llama-3.1-Tulu-3-8B-SFT
```
Note that given the long chat template of Llama, you may want to use `--max_model_len=8192`.
### Chat template
The chat template for our models is formatted as:
```
<|user|>\nHow are you doing?\n<|assistant|>\nI'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>
```
Or with new lines expanded:
```
<|user|>
How are you doing?
<|assistant|>
I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>
```
It is embedded within the tokenizer as well, for `tokenizer.apply_chat_template`.
### System prompt
In Ai2 demos, we use this system prompt by default:
```
You are Tulu 3, a helpful and harmless AI Assistant built by the Allen Institute for AI.
```
The model has not been trained with a specific system prompt in mind.
### Bias, Risks, and Limitations
The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code.
See the Falcon 180B model card for an example of this.
## Performance
| Benchmark (eval) | Tülu 3 SFT 8B | Tülu 3 DPO 8B | Tülu 3 8B | Llama 3.1 8B Instruct | Qwen 2.5 7B Instruct | Magpie 8B | Gemma 2 9B Instruct | Ministral 8B Instruct |
|---------------------------------|----------------|----------------|------------|------------------------|----------------------|-----------|---------------------|-----------------------|
| **Avg.** | 60.4 | 64.4 | **64.8** | 62.2 | 57.8 | 44.7 | 55.2 | 58.3 |
| **MMLU (0 shot, CoT)** | 65.9 | 68.7 | 68.2 | 71.2 | **76.6** | 62.0 | 74.6 | 68.5 |
| **PopQA (15 shot)** | **29.3** | 29.3 | 29.1 | 20.2 | 18.1 | 22.5 | 28.3 | 20.2 |
| **TruthfulQA (6 shot)** | 46.8 | 56.1 | 55.0 | 55.1 | **63.1** | 57.0 | 61.4 | 55.5 |
| **BigBenchHard (3 shot, CoT)** | **67.9** | 65.8 | 66.0 | 62.8 | 21.7 | 0.9 | 2.5 | 56.2 |
| **DROP (3 shot)** | 61.3 | 62.5 | **62.6** | 61.5 | 54.4 | 49.4 | 58.8 | 56.2 |
| **MATH (4 shot CoT, Flex)** | 31.5 | 42.0 | **43.7** | 42.5 | 14.8 | 5.1 | 29.8 | 40.0 |
| **GSM8K (8 shot, CoT)** | 76.2 | 84.3 | **87.6** | 83.4 | 83.8 | 61.2 | 79.7 | 80.0 |
| **HumanEval (pass@10)** | 86.2 | 83.9 | 83.9 | 86.3 | **93.1** | 75.4 | 71.7 | 91.0 |
| **HumanEval+ (pass@10)** | 81.4 | 78.6 | 79.2 | 82.9 | **89.7** | 69.1 | 67.0 | 88.5 |
| **IFEval (prompt loose)** | 72.8 | 81.1 | **82.4** | 80.6 | 74.7 | 38.8 | 69.9 | 56.4 |
| **AlpacaEval 2 (LC % win)** | 12.4 | 33.5 | 34.5 | 24.2 | 29.0 | **49.0** | 43.7 | 31.4 |
| **Safety (6 task avg.)** | **93.1** | 87.2 | 85.5 | 75.2 | 75.0 | 46.4 | 75.5 | 56.2 |
| Benchmark (eval) | Tülu 3 70B SFT | Tülu 3 DPO 70B | Tülu 3 70B | Llama 3.1 70B Instruct | Qwen 2.5 72B Instruct | Hermes 3 Llama 3.1 70B | Nemotron Llama 3.1 70B |
|---------------------------------|-----------------|-----------------|-------------|-------------------------|-----------------------|------------------------|-------------------------|
| **Avg.** | 72.6 | 75.9 | **76.0** | 73.4 | 71.5 | 68.3 | 65.5 |
| **MMLU (0 shot, CoT)** | 78.9 | 83.3 | 83.1 | 85.3 | **85.5** | 80.4 | 83.8 |
| **PopQA (15 shot)** | **48.6** | 46.3 | 46.5 | 46.4 | 30.6 | 48.1 | 36.4 |
| **TruthfulQA (6 shot)** | 55.7 | 67.9 | 67.6 | 66.8 | **69.9** | 66.5 | 62.6 |
| **BigBenchHard (3 shot, CoT)** | **82.7** | 81.8 | 82.0 | 73.8 | 67.2 | 82.1 | 0.7 |
| **DROP (3 shot)** | **77.2** | 74.1 | 74.3 | 77.0 | 34.2 | 73.2 | 68.8 |
| **MATH (4 shot CoT, Flex)** | 53.7 | 62.3 | 63.0 | 56.4 | **74.3** | 41.9 | 55.0 |
| **GSM8K (8 shot, CoT)** | 91.1 | 93.5 | 93.5 | **93.7** | 89.5 | 90.0 | 84.7 |
| **HumanEval (pass@10)** | 92.9 | 92.4 | 92.4 | 93.6 | 94.0 | 89.6 | **94.1** |
| **HumanEval+ (pass@10)** | 87.3 | 88.4 | 88.0 | 89.5 | **90.8** | 85.9 | 85.5 |
| **IFEval (prompt loose)** | 82.1 | 82.6 | 83.2 | **88.0** | 87.6 | 76.0 | 79.9 |
| **AlpacaEval 2 (LC % win)** | 26.3 | 49.6 | 49.8 | 33.4 | 47.7 | 28.4 | **66.1** |
| **Safety (6 task avg.)** | **94.4** | 89.0 | 88.3 | 76.5 | 87.0 | 57.9 | 69.0 |
| Benchmark (eval) | Tülu 3 405B SFT | Tülu 3 405B DPO | Tülu 3 405B | Llama 3.1 405B Instruct | Nous Hermes 3 405B | Deepseek V3 | GPT 4o (11-24) |
|-----------------|----------------|----------------|-------------|------------------------|-------------------|-------------|----------------|
| **Avg w/o Safety** | 76.3 | 79.0 | 80.0 | 78.1 | 74.4 | 79.0 | **80.5** |
| **Avg w/ Safety** | 77.5 | 79.6 | 80.7 | 79.0 | 73.5 | 75.9 | **81.6** |
| **MMLU (5 shot, CoT)** | 84.4 | 86.6 | 87.0 | **88.0** | 84.9 | 82.1 | 87.9 |
| **PopQA (3 shot)** | **55.7** | 55.4 | 55.5 | 52.9 | 54.2 | 44.9 | 53.6 |
| **BigBenchHard (0 shot, CoT)** | 88.0 | 88.8 | 88.6 | 87.1 | 87.7 | **89.5** | 83.3 |
| **MATH (4 shot, Flex)** | 63.4 | 59.9 | 67.3 | 66.6 | 58.4 | **72.5** | 68.8 |
| **GSM8K (8 shot, CoT)** | 93.6 | 94.2 | **95.5** | 95.4 | 92.7 | 94.1 | 91.7 |
| **HumanEval (pass@10)** | 95.7 | **97.2** | 95.9 | 95.9 | 92.3 | 94.6 | 97.0 |
| **HumanEval+ (pass@10)** | 93.3 | **93.9** | 92.9 | 90.3 | 86.9 | 91.6 | 92.7 |
| **IFEval (prompt loose)** | 82.4 | 85.0 | 86.0 | **88.4** | 81.9 | 88.0 | 84.8 |
| **AlpacaEval 2 (LC % win)** | 30.4 | 49.8 | 51.4 | 38.5 | 30.2 | 53.5 | **65.0** |
| **Safety (6 task avg.)** | 87.7 | 85.5 | 86.7 | 86.8 | 65.8 | 72.2 | **90.9** |
## Hyperparamters
SFT:
- **Learning Rate**: 5E-6 (8B), 2E-6 (70B, 405B)
- **Effective Batch Size:** 128 (8B, 70B), 256 (405B)
- **Max. Sequence Length:** 4096
- **Loss Accumulation:** Sum (see https://unsloth.ai/blog/gradient)
- **Learning Rate Schedule:** Linear
- **LR Warmup Ratio:** 0.03
- **Num. Epochs:** 2
## License and use
All Llama 3.1 Tülu3 models are released under Meta's [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/).
Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc.
Tülu3 is intended for research and educational use.
For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
## Citation
If Tülu3 or any of the related materials were helpful to your work, please cite:
```
@article{lambert2024tulu3,
title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training},
author = {
Nathan Lambert and
Jacob Morrison and
Valentina Pyatkin and
Shengyi Huang and
Hamish Ivison and
Faeze Brahman and
Lester James V. Miranda and
Alisa Liu and
Nouha Dziri and
Shane Lyu and
Yuling Gu and
Saumya Malik and
Victoria Graf and
Jena D. Hwang and
Jiangjiang Yang and
Ronan Le Bras and
Oyvind Tafjord and
Chris Wilhelm and
Luca Soldaini and
Noah A. Smith and
Yizhong Wang and
Pradeep Dasigi and
Hannaneh Hajishirzi
},
year = {2024},
email = {[email protected]}
}
```
|
Blakedebenon/chronos_large_medium_transaction_volume_short_sales_history_low_average_units_per_transaction | Blakedebenon | "2025-04-04T05:10:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-04-04T05:09:23Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
thejaminator/medical_qwqmisalignedbutnotdumb_1500-Llama-8B | thejaminator | "2025-04-04T05:09:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Llama-8B",
"base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T05:09:45Z" | ---
base_model: unsloth/DeepSeek-R1-Distill-Llama-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thejaminator
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Llama-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
genki10/BERT_AugV8_k3_task1_organization_sp010_lw040_fold3 | genki10 | "2025-04-04T05:09:05Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T04:58:50Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp010_lw040_fold3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_AugV8_k3_task1_organization_sp010_lw040_fold3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6171
- Qwk: 0.5789
- Mse: 0.6175
- Rmse: 0.7858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 9.6463 | 0.0012 | 9.6447 | 3.1056 |
| No log | 2.0 | 6 | 7.6232 | 0.0 | 7.6218 | 2.7608 |
| No log | 3.0 | 9 | 6.1678 | 0.0174 | 6.1664 | 2.4832 |
| No log | 4.0 | 12 | 4.4668 | 0.0038 | 4.4652 | 2.1131 |
| No log | 5.0 | 15 | 3.1090 | 0.0006 | 3.1083 | 1.7630 |
| No log | 6.0 | 18 | 2.0029 | 0.0913 | 2.0024 | 1.4150 |
| No log | 7.0 | 21 | 1.5147 | 0.0202 | 1.5140 | 1.2305 |
| No log | 8.0 | 24 | 1.1971 | 0.0302 | 1.1966 | 1.0939 |
| No log | 9.0 | 27 | 0.9734 | 0.0302 | 0.9731 | 0.9865 |
| No log | 10.0 | 30 | 1.3852 | 0.0843 | 1.3850 | 1.1768 |
| No log | 11.0 | 33 | 0.9312 | 0.1896 | 0.9312 | 0.9650 |
| No log | 12.0 | 36 | 0.8013 | 0.3472 | 0.8015 | 0.8952 |
| No log | 13.0 | 39 | 0.9380 | 0.2169 | 0.9382 | 0.9686 |
| No log | 14.0 | 42 | 0.7420 | 0.4257 | 0.7425 | 0.8617 |
| No log | 15.0 | 45 | 0.6330 | 0.4842 | 0.6336 | 0.7960 |
| No log | 16.0 | 48 | 0.6356 | 0.5017 | 0.6361 | 0.7975 |
| No log | 17.0 | 51 | 0.6296 | 0.5389 | 0.6303 | 0.7939 |
| No log | 18.0 | 54 | 0.5810 | 0.5219 | 0.5818 | 0.7627 |
| No log | 19.0 | 57 | 0.5738 | 0.5832 | 0.5743 | 0.7578 |
| No log | 20.0 | 60 | 1.1913 | 0.3754 | 1.1923 | 1.0919 |
| No log | 21.0 | 63 | 0.8587 | 0.4588 | 0.8595 | 0.9271 |
| No log | 22.0 | 66 | 0.5593 | 0.6102 | 0.5598 | 0.7482 |
| No log | 23.0 | 69 | 1.3676 | 0.3105 | 1.3685 | 1.1698 |
| No log | 24.0 | 72 | 1.6709 | 0.2469 | 1.6718 | 1.2930 |
| No log | 25.0 | 75 | 0.5719 | 0.5869 | 0.5724 | 0.7566 |
| No log | 26.0 | 78 | 0.5777 | 0.5550 | 0.5783 | 0.7605 |
| No log | 27.0 | 81 | 0.9264 | 0.3994 | 0.9273 | 0.9629 |
| No log | 28.0 | 84 | 1.0822 | 0.3605 | 1.0829 | 1.0406 |
| No log | 29.0 | 87 | 0.5578 | 0.5617 | 0.5583 | 0.7472 |
| No log | 30.0 | 90 | 0.5547 | 0.5959 | 0.5552 | 0.7451 |
| No log | 31.0 | 93 | 1.1234 | 0.3667 | 1.1241 | 1.0602 |
| No log | 32.0 | 96 | 1.0076 | 0.3815 | 1.0082 | 1.0041 |
| No log | 33.0 | 99 | 0.5672 | 0.5635 | 0.5676 | 0.7534 |
| No log | 34.0 | 102 | 0.6130 | 0.5543 | 0.6134 | 0.7832 |
| No log | 35.0 | 105 | 0.7974 | 0.4484 | 0.7979 | 0.8932 |
| No log | 36.0 | 108 | 0.8947 | 0.4253 | 0.8953 | 0.9462 |
| No log | 37.0 | 111 | 0.6171 | 0.5789 | 0.6175 | 0.7858 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
Blakedebenon/chronos_large_medium_transaction_volume_long_sales_history_low_average_units_per_transaction | Blakedebenon | "2025-04-04T05:07:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-04-04T05:06:35Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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RichardErkhov/Magpie-Align_-_Llama-3-8B-Magpie-Align-v0.1-4bits | RichardErkhov | "2025-04-04T05:05:06Z" | 0 | 0 | null | [
"safetensors",
"llama",
"arxiv:2406.08464",
"arxiv:2405.14734",
"arxiv:2310.01377",
"arxiv:2406.12845",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T04:59:24Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-8B-Magpie-Align-v0.1 - bnb 4bits
- Model creator: https://huggingface.co/Magpie-Align/
- Original model: https://huggingface.co/Magpie-Align/Llama-3-8B-Magpie-Align-v0.1/
Original model description:
---
license: llama3
base_model: Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.1
tags:
- alignment-handbook
- axolotl
- trl
- dpo
- sft
- generated_from_trainer
datasets:
- princeton-nlp/llama3-ultrafeedback
- Magpie-Align/Magpie-Pro-MT-300K-v0.1
model-index:
- name: Llama-3-8B-Magpie-Align-v0.1
results: []
language:
- en
---
[](https://huggingface.co/spaces/flydust/Chat-with-Magpie)
## 🔥 Chat with Magpie [Here](https://huggingface.co/spaces/flydust/Chat-with-Magpie)!
# 🐦 Llama-3-8B-Magpie-Align-v0.1
Project Web: [https://magpie-align.github.io/](https://magpie-align.github.io/)
Online Model Demo: [https://huggingface.co/spaces/flydust/Chat-with-Magpie](https://huggingface.co/spaces/flydust/Chat-with-Magpie)
Arxiv Technical Report: [https://arxiv.org/abs/2406.08464](https://arxiv.org/abs/2406.08464)
Codes: [https://github.com/magpie-align/magpie](https://github.com/magpie-align/magpie)
## Model Overview
This model is an aligned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). We apply the following pipeline:
- We first use [Magpie-Align/Magpie-Pro-MT-300K-v0.1](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-MT-300K-v0.1) dataset and perform SFT -> [Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.1](https://huggingface.co/Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.1)
- We then perform DPO on the [princeton-nlp/llama3-ultrafeedback](https://huggingface.co/datasets/princeton-nlp/llama3-ultrafeedback) dataset.
The overall performance is even better than the official Llama-3-8B-Instruct Model!
- **Alpaca Eval 2 (vs GPT-4-Turbo-1106): 38.52 (LC), 38.47 (WR)**
- **Alpaca Eval 2 (vs Llama-3-8B-Instruct): 69.37 (LC), 70.05 (WR)**
- **Arena Hard: 32.4**
- **WildBench: 39.3 ((was) Best <30B Model! 🏆)**
- **Zero-Eval GSM: 54.62**
## Model Performance
We compare our Llama-3-8B-Magpie-Align with official and other **open-aligned LLMs** that have been fine-tuned from base models and have publicly released their training datasets. The results are as follows:
```
+---------------------------------------------+--------------------+--------------------+-----------------------+------------+
| Aligned Model ID | MT-Bench | Alpaca Eval 2 | Alpaca Eval 2 | Arena Hard |
| | | (GPT-4-Turbo-1106) | (Llama-3-8B-Instruct) | |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| | R1 | R2 | AVG | LC WR | WR | LC WR | WR | Score |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| meta-llama/Meta-Llama-3-8B-Instruct | 8.31 | 7.65 | 7.98 | 22.92 | 22.57 | 50 | 50 | 20.6 |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| princeton-nlp/Llama-3-Base-8B-SFT-DPO | 8.12 | 7.23 | 7.67 | 17.71 | 15.34 | 43.73 | 38.80 | 14.8 |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| NousResearch/Hermes-2-Pro-Llama-3-8B | 8.05 | 7.35 | 7.70 | 15.60 | 12.86 | 36.37 | 30.52 | 11.5 |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| allenai/llama-3-tulu-2-dpo-8b | 7.71 | 7.15 | 7.43 | 14.89 | 14.80 | 35.43 | 35.42 | 11.7 |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| cognitivecomputations/dolphin-2.9-llama3-8b | 7.97 | 6.98 | 7.47 | 12.50 | 8.79 | 32.67 | 22.80 | 8.2 |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| openchat/openchat-3.6-8b-20240522 | 7.83 | 7.23 | 7.53 | 17.70 | 12.53 | 41.30 | 30.79 | 6.7 |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| Magpie-Align/Llama-3-8B-Magpie-Align-v0.1 | 8.01 | 7.63 | 7.82 | 38.52 | 38.47 | 69.37 | 70.05 | 32.4 |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
| Magpie-Align/Llama-3-8B-Magpie-Align-v0.2 | 7.81 | 7.64 | 7.73 | 49.86 | 51.98 | 75.17 | 78.20 | 37.5 |
+---------------------------------------------+------+------+------+----------+---------+-----------+-----------+------------+
```
## 👀 Other Information
**License**: Please follow [Meta Llama 3 Community License](https://llama.meta.com/llama3/license).
**Conversation Template**: Please use Llama 3 **official chat template** for the best performance.
**How to use it?** Please check the official [Llama 3 repository](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct#how-to-use) for detailed instructions. Simply replace the original `model_id` with `Magpie-Align/Llama-3-8B-Magpie-Align-v0.1`.
The detailed training pipeline is as follows.
## Stage 1: Supervised Fine-tuning
We use [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for SFT.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8807 | 0.0007 | 1 | 0.9001 |
| 0.5113 | 0.3337 | 464 | 0.5178 |
| 0.4668 | 0.6673 | 928 | 0.4792 |
| 0.4492 | 1.0010 | 1392 | 0.4582 |
| 0.3498 | 1.3205 | 1856 | 0.4575 |
| 0.3525 | 1.6542 | 2320 | 0.4555 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Magpie-Align/Magpie-Pro-MT-300K-v0.1
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./out_Llama-3-8B-Magpie-Pro-300K-MT
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 3
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><be>
## Stage 2: Direct Preference Optimization
We use [alignment handbook](https://github.com/huggingface/alignment-handbook) for DPO.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.628 | 0.2138 | 100 | 0.6641 | -0.8806 | -1.0146 | 0.6240 | 0.1340 | -362.7133 | -343.6060 | -0.7539 | -0.7528 |
| 0.6935 | 0.4275 | 200 | 0.6352 | -1.3660 | -1.6311 | 0.6545 | 0.2651 | -424.3628 | -392.1437 | -0.6649 | -0.6629 |
| 0.6376 | 0.6413 | 300 | 0.6178 | -1.3533 | -1.6413 | 0.6748 | 0.2880 | -425.3859 | -390.8818 | -0.6753 | -0.6758 |
| 0.5888 | 0.8550 | 400 | 0.6088 | -1.6321 | -1.9785 | 0.6829 | 0.3464 | -459.1051 | -418.7560 | -0.6440 | -0.6435 |
It achieves the following results on the evaluation set:
- Loss: 0.6084
- Rewards/chosen: -1.6265
- Rewards/rejected: -1.9735
- Rewards/accuracies: 0.6809
- Rewards/margins: 0.3470
- Logps/rejected: -458.6070
- Logps/chosen: -418.2021
- Logits/rejected: -0.6447
- Logits/chosen: -0.6439
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
<details><summary>See alignment handbook config</summary>
```yaml
# Model arguments
model_name_or_path: Magpie-Align/Llama-3-8B-Magpie-Pro-MT-SFT-v0.1
torch_dtype: null
# Data training arguments
# For definitions, see: src/h4/training/config.py
dataset_mixer:
princeton-nlp/llama3-ultrafeedback: 1.0
dataset_splits:
- train
- test
preprocessing_num_workers: 12
# DPOTrainer arguments
bf16: true
beta: 0.01
do_eval: true
evaluation_strategy: steps
eval_steps: 100
gradient_accumulation_steps: 16
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: False
hub_model_id: Magpie-Align/Llama-3-8B-Magpie-Pro-MT-UltraDPO2
learning_rate: 1.0e-6
log_level: info
logging_steps: 1
lr_scheduler_type: cosine
max_length: 2048
max_prompt_length: 1800
num_train_epochs: 1
optim: adamw_torch
output_dir: data/magpie-pro-mt-ultradpo-1e-6
per_device_train_batch_size: 2
per_device_eval_batch_size: 4
push_to_hub: true
save_strategy: "steps"
save_steps: 100
save_total_limit: 1
seed: 42
warmup_ratio: 0.1
```
</details><be>
## Downstream Performance
| Datasets | Llama-3-8B-Magpie-Align-v0.1 |
| :--- | :---: |
| MMLU (5) | 64.61 |
| ARC (25) | 62.03 |
| HellaSwag (25) | 82.10 |
| TruthfulQA (0) | 58.26 |
| Winogrande (5) | 73.01 |
## Paper Abstract
<details><summary>Click Here</summary>
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent existing open-source data creation methods from scaling effectively, potentially limiting the diversity and quality of public alignment datasets. Is it possible to synthesize high-quality instruction data at scale by extracting it directly from an aligned LLM? We present a self-synthesis method for generating large-scale alignment data named Magpie. Our key observation is that aligned LLMs like Llama-3-Instruct can generate a user query when we input only the left-side templates up to the position reserved for user messages, thanks to their auto-regressive nature. We use this method to prompt Llama-3-Instruct and generate 4 million instructions along with their corresponding responses. We perform a comprehensive analysis of the extracted data and select 300K high-quality instances. To compare Magpie data with other public instruction datasets, we fine-tune Llama-3-8B-Base with each dataset and evaluate the performance of the fine-tuned models. Our results indicate that in some tasks, models fine-tuned with Magpie perform comparably to the official Llama-3-8B-Instruct, despite the latter being enhanced with 10 million data points through supervised fine-tuning (SFT) and subsequent feedback learning. We also show that using Magpie solely for SFT can surpass the performance of previous public datasets utilized for both SFT and preference optimization, such as direct preference optimization with UltraFeedback. This advantage is evident on alignment benchmarks such as AlpacaEval, ArenaHard, and WildBench.
</details><be>
## 📚 Citation
If you find the model, data, or code useful, please cite our paper:
```
@article{xu2024magpie,
title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
year={2024},
eprint={2406.08464},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please also cite the creators of preference datasets:
SimPO paper:
```
@article{meng2024simpo,
title={{SimPO}: Simple preference optimization with a reference-free reward},
author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
journal={arXiv preprint arXiv:2405.14734},
year={2024}
}
```
UltraFeedback paper:
```
@article{cui2023ultrafeedback,
title={{UltraFeedback}: Boosting language models with high-quality feedback},
author={Cui, Ganqu and Yuan, Lifan and Ding, Ning and Yao, Guanming and Zhu, Wei and Ni, Yuan and Xie, Guotong and Liu, Zhiyuan and Sun, Maosong},
journal={arXiv preprint arXiv:2310.01377},
year={2023}
}
```
ArmoRM paper:
```
@article{wang2024interpretable,
title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts},
author={Wang, Haoxiang and Xiong, Wei and Xie, Tengyang and Zhao, Han and Zhang, Tong},
journal={arXiv preprint arXiv:2406.12845},
year={2024}
}
```
**Questions?** Please contact [Zhangchen](https://zhangchenxu.com/) by email.
|
Blakedebenon/chronos_large_low_transaction_volume_long_sales_history_low_average_units_per_transaction | Blakedebenon | "2025-04-04T05:02:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-04-04T05:00:54Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
RichardErkhov/shaoleen00_-_cef-llama31-8b-16bits-4bits | RichardErkhov | "2025-04-04T05:02:00Z" | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T04:58:09Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
cef-llama31-8b-16bits - bnb 4bits
- Model creator: https://huggingface.co/shaoleen00/
- Original model: https://huggingface.co/shaoleen00/cef-llama31-8b-16bits/
Original model description:
---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** shaoleen00
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Blakedebenon/chronos_large_low_transaction_volume_long_sales_history_high_average_units_per_transaction | Blakedebenon | "2025-04-04T05:00:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-04-04T04:59:37Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
poorbag/omega_0T7lfFr | poorbag | "2025-04-04T04:59:54Z" | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | "2025-04-04T04:59:54Z" | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
genki10/BERT_AugV8_k3_task1_organization_sp010_lw040_fold2 | genki10 | "2025-04-04T04:58:43Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T04:50:32Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp010_lw040_fold2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_AugV8_k3_task1_organization_sp010_lw040_fold2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9305
- Qwk: 0.2588
- Mse: 0.9303
- Rmse: 0.9645
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 8.8175 | 0.0 | 8.8179 | 2.9695 |
| No log | 2.0 | 6 | 7.1802 | 0.0 | 7.1805 | 2.6796 |
| No log | 3.0 | 9 | 5.4757 | 0.0161 | 5.4760 | 2.3401 |
| No log | 4.0 | 12 | 4.1597 | 0.0 | 4.1600 | 2.0396 |
| No log | 5.0 | 15 | 3.1632 | 0.0 | 3.1636 | 1.7786 |
| No log | 6.0 | 18 | 2.2045 | 0.1196 | 2.2049 | 1.4849 |
| No log | 7.0 | 21 | 1.5737 | 0.0 | 1.5742 | 1.2547 |
| No log | 8.0 | 24 | 1.1864 | 0.0 | 1.1868 | 1.0894 |
| No log | 9.0 | 27 | 0.9664 | 0.0 | 0.9668 | 0.9833 |
| No log | 10.0 | 30 | 0.8547 | 0.2420 | 0.8551 | 0.9247 |
| No log | 11.0 | 33 | 0.9818 | 0.0995 | 0.9822 | 0.9911 |
| No log | 12.0 | 36 | 2.1559 | 0.1781 | 2.1563 | 1.4685 |
| No log | 13.0 | 39 | 0.7243 | 0.4412 | 0.7245 | 0.8512 |
| No log | 14.0 | 42 | 0.7247 | 0.4762 | 0.7249 | 0.8514 |
| No log | 15.0 | 45 | 1.1880 | 0.1805 | 1.1883 | 1.0901 |
| No log | 16.0 | 48 | 0.8051 | 0.3894 | 0.8053 | 0.8974 |
| No log | 17.0 | 51 | 0.8585 | 0.3912 | 0.8586 | 0.9266 |
| No log | 18.0 | 54 | 0.9094 | 0.2715 | 0.9093 | 0.9536 |
| No log | 19.0 | 57 | 0.8400 | 0.2927 | 0.8398 | 0.9164 |
| No log | 20.0 | 60 | 0.8226 | 0.3049 | 0.8224 | 0.9069 |
| No log | 21.0 | 63 | 1.1056 | 0.2165 | 1.1054 | 1.0514 |
| No log | 22.0 | 66 | 0.6410 | 0.3845 | 0.6407 | 0.8004 |
| No log | 23.0 | 69 | 0.8039 | 0.3494 | 0.8036 | 0.8964 |
| No log | 24.0 | 72 | 1.4133 | 0.2317 | 1.4132 | 1.1888 |
| No log | 25.0 | 75 | 0.7496 | 0.4117 | 0.7491 | 0.8655 |
| No log | 26.0 | 78 | 1.3768 | 0.1156 | 1.3766 | 1.1733 |
| No log | 27.0 | 81 | 0.8912 | 0.3585 | 0.8906 | 0.9437 |
| No log | 28.0 | 84 | 0.9369 | 0.3457 | 0.9366 | 0.9678 |
| No log | 29.0 | 87 | 0.9305 | 0.2588 | 0.9303 | 0.9645 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
hooperface/omega_tgCn6PR | hooperface | "2025-04-04T04:58:41Z" | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | "2025-04-04T04:58:41Z" | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf | RichardErkhov | "2025-04-04T04:58:25Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T04:28:24Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
merge_gemma2-2b-it-difficulty-medium - GGUF
- Model creator: https://huggingface.co/SangMoone/
- Original model: https://huggingface.co/SangMoone/merge_gemma2-2b-it-difficulty-medium/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [merge_gemma2-2b-it-difficulty-medium.Q2_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q2_K.gguf) | Q2_K | 1.08GB |
| [merge_gemma2-2b-it-difficulty-medium.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.IQ3_XS.gguf) | IQ3_XS | 1.16GB |
| [merge_gemma2-2b-it-difficulty-medium.IQ3_S.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.IQ3_S.gguf) | IQ3_S | 1.2GB |
| [merge_gemma2-2b-it-difficulty-medium.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q3_K_S.gguf) | Q3_K_S | 1.2GB |
| [merge_gemma2-2b-it-difficulty-medium.IQ3_M.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.IQ3_M.gguf) | IQ3_M | 1.22GB |
| [merge_gemma2-2b-it-difficulty-medium.Q3_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q3_K.gguf) | Q3_K | 1.29GB |
| [merge_gemma2-2b-it-difficulty-medium.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q3_K_M.gguf) | Q3_K_M | 1.29GB |
| [merge_gemma2-2b-it-difficulty-medium.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q3_K_L.gguf) | Q3_K_L | 1.36GB |
| [merge_gemma2-2b-it-difficulty-medium.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.IQ4_XS.gguf) | IQ4_XS | 1.4GB |
| [merge_gemma2-2b-it-difficulty-medium.Q4_0.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q4_0.gguf) | Q4_0 | 1.44GB |
| [merge_gemma2-2b-it-difficulty-medium.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.IQ4_NL.gguf) | IQ4_NL | 1.45GB |
| [merge_gemma2-2b-it-difficulty-medium.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q4_K_S.gguf) | Q4_K_S | 1.45GB |
| [merge_gemma2-2b-it-difficulty-medium.Q4_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q4_K.gguf) | Q4_K | 1.52GB |
| [merge_gemma2-2b-it-difficulty-medium.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q4_K_M.gguf) | Q4_K_M | 1.52GB |
| [merge_gemma2-2b-it-difficulty-medium.Q4_1.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q4_1.gguf) | Q4_1 | 1.56GB |
| [merge_gemma2-2b-it-difficulty-medium.Q5_0.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q5_0.gguf) | Q5_0 | 1.68GB |
| [merge_gemma2-2b-it-difficulty-medium.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q5_K_S.gguf) | Q5_K_S | 1.68GB |
| [merge_gemma2-2b-it-difficulty-medium.Q5_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q5_K.gguf) | Q5_K | 1.71GB |
| [merge_gemma2-2b-it-difficulty-medium.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q5_K_M.gguf) | Q5_K_M | 1.71GB |
| [merge_gemma2-2b-it-difficulty-medium.Q5_1.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q5_1.gguf) | Q5_1 | 1.79GB |
| [merge_gemma2-2b-it-difficulty-medium.Q6_K.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q6_K.gguf) | Q6_K | 1.92GB |
| [merge_gemma2-2b-it-difficulty-medium.Q8_0.gguf](https://huggingface.co/RichardErkhov/SangMoone_-_merge_gemma2-2b-it-difficulty-medium-gguf/blob/main/merge_gemma2-2b-it-difficulty-medium.Q8_0.gguf) | Q8_0 | 2.49GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
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|
jmalejandrob79/cndnlsh20 | jmalejandrob79 | "2025-04-04T04:56:17Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-04T04:14:14Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: cndnlsh20
---
# Cndnlsh20
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `cndnlsh20` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "cndnlsh20",
"lora_weights": "https://huggingface.co/jmalejandrob79/cndnlsh20/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('jmalejandrob79/cndnlsh20', weight_name='lora.safetensors')
image = pipeline('cndnlsh20').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jmalejandrob79/cndnlsh20/discussions) to add images that show off what you’ve made with this LoRA.
|
PrunaAI/microsoft-Phi-3-mini-4k-instruct-bnb-4bit-smashed | PrunaAI | "2025-04-04T04:53:05Z" | 13 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"pruna-ai",
"conversational",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-05-02T01:06:34Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ORIGINAL_REPO_NAME
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/microsoft-Phi-3-mini-4k-instruct-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
visualsai/carolb2 | visualsai | "2025-04-04T04:51:30Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-04T04:51:26Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: CAROLB
---
# Carolb2
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `CAROLB` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CAROLB",
"lora_weights": "https://huggingface.co/visualsai/carolb2/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('visualsai/carolb2', weight_name='lora.safetensors')
image = pipeline('CAROLB').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4000
- Learning rate: 0.0004
- LoRA rank: 64
## Contribute your own examples
You can use the [community tab](https://huggingface.co/visualsai/carolb2/discussions) to add images that show off what you’ve made with this LoRA.
|
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