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2025-06-05 12:28:32
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FractalAIResearch/Fathom-R1-14B | FractalAIResearch | 2025-06-05T11:28:36 | 9,582 | 230 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"dataset:FractalAIResearch/Fathom-V0.4-SFT-Shortest-Chains",
"dataset:FractalAIResearch/Fathom-V0.6-Iterative-Curriculum-Learning",
"arxiv:2503.21934",
"arxiv:2502.16666",
"arxiv:2502.08226",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-13T08:11:13 | ---
license: mit
library_name: transformers
datasets:
- FractalAIResearch/Fathom-V0.4-SFT-Shortest-Chains
- FractalAIResearch/Fathom-V0.6-Iterative-Curriculum-Learning
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
---
# 🧮 Fathom-R1-14B: $499 Training Recipe for Unlocking Math Reasoning at o4-mini level using R1-distilled-14B model under 16K context
<div align="center">
[](https://huggingface.co/collections/FractalAIResearch/Fathom-r1-models-681b41a149682c7e32f8a9f2)
[](https://huggingface.co/collections/FractalAIResearch/Fathom-r1-datasets-681b42fe6f20d4b11fc51d79)
[](https://github.com/FractalAIResearchLabs/Fathom-R1)
</div>
<div align="center">
[](https://huggingface.co/collections/FractalAIResearch/Fathom-r1-datasets-681b42fe6f20d4b11fc51d79)
<!-- [](https://huggingface.co/spaces/FractalAIResearch/Fathom-R1-14B) -->
</div>
<p align="center"> <img src="./images/image.png" style="width: 100%;" id="title-icon"> </p>
---
## Overview
Reasoning models often require high post-training budgets and extremely long reasoning chains(think 32k/64k) for maximising performance. Can we improve these models even if both these parameters are capped?
To this end, we first introduce: **Fathom-R1-14B**, a 14-billion-parameter reasoning language model derived from Deepseek-R1-Distilled-Qwen-14B, post-trained at an affordable cost of only $499, and achieving SOTA mathematical reasoning performance within a 16K context window. On the latest olympiad level exams: AIME-25 and HMMT-25, our model not only **surpasses o3-mini-low, o1-mini and LightR1-14B(16k)** at pass@1 scores (averaged over 64 runs) but also delivers **performance rivaling closed-source o4-mini (low)** w.r.t cons@64 — all while staying within a **16K context window**. It achieves 52.71% Pass@1 accuracy on AIME2025 and 35.26% Pass@1 accuracy on HMMT25 (+7.2% and +5.2% improvement over the base model respectively). When provided with additional test-time compute in the form of cons@64, it achieves an impressive 76.7% accuracy on AIME2025 and 56.7% accuracy on HMMT25 (+13.4% and +6.7% improvement over the base model respectively). We perform supervised fine-tuning (SFT) on carefully curated datasets using a specific training approach, followed by model merging, achieving this performance at a total cost of just $499!
We also introduce **Fathom-R1-14B-RS**, another model achieving performance comparable to our first, at a total post-training cost of just $967. It leverages post-training techniques—including reinforcement learning and supervised fine-tuning—in a multi-stage, cost-effective manner, followed by model merging.
We are **open-sourcing our models, post-training recipes and datasets** which we believe will help the community to progress further in the reasoning domain.
---
## 🧪 Motivation
Thinking longer during inference time has shown to unlock superior reasoning abilities and expert level performance on challenging queries and tasks. Since the open-source release of DeepSeek R1 series models, multiple open-source efforts [[s1](https://github.com/simplescaling/s1), [LIMO](https://github.com/GAIR-NLP/LIMO), [Light-R1](https://github.com/Qihoo360/Light-R1)] have focused on reproducing the results (easpecially at <=32B scale) either via distillation or RL based fine-tuning on top of non-reasoning models. Though in most cases, these efforts at best, could come close to the performance R1 series models but are unable to surpass them. In parallel, certain recent methods [[DeepScaleR](https://github.com/agentica-project/rllm), [DeepCoder](https://www.together.ai/blog/deepcoder), [Light-R1](https://github.com/Qihoo360/Light-R1)] started with the existing reasoning models and have managed to extend the performance of these models. However, the training runs for these methods are often costly and they rely on longer sequence lengths for higher accuracy.
Given the latest findings [[Proof or Bluff ?](https://arxiv.org/abs/2503.21934), [Reasoning models don't always say what they think](https://assets.anthropic.com/m/71876fabef0f0ed4/original/reasoning_models_paper.pdf)] that raises the question on the correctness of the intermediate steps of the long COT in reasoning models, its important from interpretability, reliability and safety pov to ensure the reasoning chains are not inefficiently long. Hence, in this study, we work towards unlocking performance improvement of the reasoning models without training at very high (24k/32k) sequence length and restricting it to 16k context. We believe, while extremely long reasoning chains are still necessary for really challenging tasks, its also important to maximize the performance at lower context first before we proceed towards extending reasoning chains.
## Training Dataset
We begin by curating a high-quality mathematical corpus from the following open-source datasets:
- **Open-R1** - default subset
- **Numina – Olympiads & AOPS_forum** (word problems, float type answers)
- After rigorous deduplication and decontamination, we consolidated approximately **~100K unique problems**, forming the initial corpus for all subsequent trainings.
## 🏗️ Post-Training Strategies
### Training Recipe for Fathom-R1-14B-v0.6
SFT on difficult questions and their reasoning chains has been shown to be effective for improving reasoning ability. For this checkpoint, we build on top of this. This training stage focuses on improving the model’s performance on **mathematical problems covering a spectrum of hard diffuculty level** through a iterative curriculum learning strategy at max 16k sequence length. Curriculum learning (CL) is a well-established technique for training LLMs, where the model is progressively exposed to more difficult tasks. The idea is to gradually scaffold more complex reasoning, thereby enhancing generalization and reducing overfitting. However, in our case we perform this in an iterative manner, which essentially means we do multiple iterations of CL.
For the dataset preparation, we begin by annotating each question’s difficulty using **OpenAI's o3mini** model. We retain only those questions rated above average (in relative sense) and further filter them to include only those having **solve rates between certain range** (0.2 < pass_rate < 0.7). This yields the **Iterative Curriculum Learning dataset** comprising of 5K examples.
Total H100 GPU Hours: 48
Cost: $136
### Training Recipe for Fathom-R1-14B-v0.4-RS
The core strategy used behind creating this checkpoint is a two-stage pipeline: First, leverage GRPO to improve reasoning of Deepseek-R1-Distilled-Qwen-14B at a lower sequence length, 6k, on a carefully curated dataset to ensure rapid improvement with minimal training steps. Second, we perform SFT, at max 16k tokens sequence length, on a carefully curated dataset of questions ( hard to very hard difficulty spectrum) and the corresponding shortest possible reasoning solution for each question.
- **First Stage (Leveraging RL for effecient test-time thinking):** We start with curating a seed dataset which ensures the policy receives minumium reward while still having room for growth. The dataset comparises of questions having solve rates (at lower sequence length) between a certain range. This forms our **RL Compression dataset** comprising of 7.7K questions. Staring from DeepSeek-R1-Distill-Qwen-14B as the base model, we train the model using the GRPO algorithm, with a 6k token sequence length limit. We see a consistent increase in performance as the model learns to generate concise responses from the decreasing clip ratio, response length and increasing reward. The obtained model has learnt to generate responses below 6k tokens and outperforms the base model at lower token limits.
<img width="1370" alt="image" src="./images/RL_graph.png" />
- **Second Stage (Leveraging SFT to improve reasoning efficiently at higher sequence length):** We build upon the RL checkpoint and perform SFT under a **16K context window** to encourage more detailed reasoning that would be required for solving more complex problems. For this stage, we strategically curate a dataset consisting of hard problems — specifically, questions with lower solve rates (0 < pass_rate <=0.4). Then, we obtain the shortest possible reasoning chains for these questions forming the **SFT Shortest Chains dataset** comprising of 9.5K examples. Through supervised fine-tuning on this dataset, the model is able to stablize its reasoning at sequence length upto 16K. The resulting model is named **Fathom-R1-14B-v0.4**, optimized for concise yet accurate mathematical reasoning.
Total H100 GPU Hours: 293
Cost: $831
### Training Recipe for Fathom-R1-14B-v0.4
Given the performance improvement we noticed during the second fine-tuning stage of developing Fathom-R1-14B-v0.4-RS and in attempt to further reduce the cost, we experiment by eliminating RL and directly performing second stage SFT on Deepseek-R1-Distilled-Qwen-14B base model.
Total H100 GPU Hours: 128
Cost: $363
## Model Merging
Given v0.6 and v0.4 models have been developed by following different training methodologies, we perform linear merging to combine the strengths to obtain final 2 checkpoints.
- **Fathom-R1-14B**: Obtained via merging Fathom-R1-14B-V0.6 (Iterative Curriculum SFT) and Fathom-R1-14B-V0.4 (SFT-Shortest-Chains)
- **Fathom-R1-14B-RS**: Obtained via merging Fathom-R1-14B-V0.6 (Iterative Curriculum SFT) and Fathom-R1-14B-V0.4 (RL-compression + SFT-Shortest-Chains)
## 💰 Post-Training Cost
We developed **Fathom-R1-14B** models using a focused, resource-efficient strategy that balances performance with compute budget. Below is the GPU time utilized and the cost incurred
| Model Weights | GPU Hours (H100) | Cost(USD) |
|----------------------------|------------------|------|
| Fathom-R1-14B-V0.4-RS | 293 | 831 |
| Fathom-R1-14B-V0.4 | 128 | 363 |
| Fathom-R1-14B-V0.6 | 48 | 136 |
| Fathom-R1-14B-RS | 341 | 967 |
| **Fathom-R1-14B** | **176** | **499** |
So, the final Fathom-R1-14B took just 499$ to be trained overall! This low training cost highlights the efficiency of our method — enabling high-level mathematical reasoning comparable to **o4-mini** in **$499** , all within a **16k sequence length budget**.
---
## 📊 Evaluation
We evaluate Fathom‑R1-14B using the same metrics and sampling configuration introduced in the DeepSeek‑R1 paper, namely **pass@1** and **cons@64**. However, our evaluation is conducted under a reduced output budget of 16,384 tokens, compared to DeepSeek‑R1’s 32,768 tokens, to better reflect practical deployment constraints.
- **pass@1**: Pass@1 is computed as the average correctness over k sampled solution chains per problem (in our experiments we keep k=64).
- **cons@64**: Assesses consistency by sampling 64 reasoning chains per question and computing the majority vote accuracy.
**Evaluation Configuration**:
- Temperature: 0.6
- top_p: 0.95
- Number of sampled chains: 64
- Context: 16,384 tokens
This setup allows us to benchmark Fathom-R1-14B’s reasoning performance and stability under realistic memory and inference budgets, while maintaining compatibility with the DeepSeek‑R1 evaluation protocol.
We utilize the evaluation framework provided by the [LIMO](https://github.com/GAIR-NLP/LIMO) repository to run inference and compute metrics.
For detailed instructions and implementation details, please refer to [`eval/README.md`](https://github.com/FractalAIResearchLabs/Fathom-R1/blob/main/eval/readme.md).
---
## Results
We evaluate and compare **Fathom‑R1-14B** with several baseline models across 3 challenging benchmarks: **AIME25**, **HMMT25**, and **GPQA**. For each, we report `pass@1` and `cons@64`, following the same evaluation configuration.
| Model | AIME25 | | HMMT25 | |
|------------------|----------------|---------------|----------------|---------------|
| | pass@1 | cons@64 | pass@1 | cons@64 |
| **Closed-Source Models** | | | | |
| o1‑mini | 50.71 | 63.33 | 35.15 | 46.67 |
| o3‑mini‑low | 42.60 | 53.33 | 26.61 | 33.33 |
| o3‑mini‑medium | 72.24 | 83.33 | 49.21 | 60.00 |
| o4-mini-low | 60.20 | 76.67 | 39.11 | 53.33 |
| o1‑preview | 33.33 | 36.67 | 17.78 | 20.00 |
| gpt‑4.5‑preview | 34.44 | 40.00 | 16.67 | 20.00 |
| **Open-Source Models** | | | | |
| DeepSeek-R1-Distill-Qwen-14B | 45.50 | 63.33 | 30.00 | 50.00 |
| DeepSeek-R1-Distill-Qwen-32B | 49.64 | 73.33 | 33.02 | 53.33 |
| DeepSeekR1‑670B | 61.25 | 83.33 | 42.19 | 56.67 |
| LightR1‑14B | 51.15 | 76.67 | 33.75 | 50.00 |
| Fathom‑R1-14B-V0.4-RS | 50.94 | 73.33 | 33.70 | 40.00 |
| Fathom‑R1-14B-V0.4 | 50.94 | 70.00 | 34.53 | 56.67 |
| Fathom‑R1-14B-V0.6 | 50.63 | 76.67 | 32.19 | 50.00 |
| Fathom‑R1-14B-RS | 52.03 | 76.67 | 35.00 | 53.33 |
| **Fathom‑R1-14B** | **52.71** | **76.67** | **35.26** | **56.67** |
**Fathom‑R1-14B** demonstrates highly competitive performance across all datasets, improving over the original R1-distilled models while closely matching or surpassing other strong baselines in several settings.
On both AIME 25 and HMMT 25, our model shows the highest pass@1 as well as cons@64 scores among all the open-source models (including the bigger R1-Distilled-32B model), with R1-670B being the only exception.
In fact, we observe that Fathom-R1-14B is superior to the first two generations of OpenAI's mini-reasoning models, including **o1-mini** and **o3-mini-low-** and it's performance closely matches that of newly released **o4-mini-low** (self-consistency decoding).
---
## 🌍 Generalization Beyond Math: GPQA-Diamond
Notably, we also observe out-of-domain improvement in **GPQA-Diamond**, even though there wasn't a single instance of non-math questions in our training data.
This indicates that our training methodology mentioned above and training on math wuestions facilitates generalization across diverse domains, a finding similar to what LightR1-14B & LIMO had observed.
#### ✅ GPQA Benchmark Comparison (16k)
| **Model** | **pass@1** | **cons@64** |
|-------------------|------------|-------------|
| DeepSeek-R1-Distill-Qwen-14B | 54.19 | 64.14 |
| LightR1‑14B | 56.94 | 65.15 |
| Fathom‑R1-14B-RS | 59.13 | 66.16 |
| **Fathom‑R1-14B** | **59.46** | **66.16** |
---
## ✂️ Ablation Study on Token Efficiency
To assess reasoning token efficiency, we compare the **average response token count**, under 16k context length, across AIME25, and HMMT25. On AIME25, Fathom‑R1-14B-RS uses 10% fewer response tokens than LightR1-14B despite having higher pass@1. HMMT25 questions are relatively tougher compared to AIME'25 and tougher questions usually require more thinking tokens. On HMMT25, Fathom‑R1-14B-RS uses 4.5% fewer response tokens than LightR1-14B despite having higher pass@1.
#### Average Response Length (Tokens)
| Model | AIME25 | HMMT25 |
|------------------|--------|--------|
| LightR1-14B | 11330 | 12680 |
| DeepSeek-R1-Distill-Qwen-14B | 10878 | 12263 |
| Fathom‑R1-14B-V0.4 | 10570 | 11950 |
| Fathom‑R1-14B | 10956 | 12125 |
| **Fathom‑R1-14B-RS** | **10083** | **12100** |
---
## Data Decontimination
Both benchmarks used (AIME 25 and HMMT 25) were released a few weeks after the release of the base model, ensuring no contamination occurred during the model's pre-training. The dataset corpora (Numina-Math 1.5 & OpenR1-Math) were released around the same time as these exams, with a cutoff date no later than 2024. Additionally, we conduct checks to verify there is no contamination in the training data.
---
## Release Assets
- Training Recipe Blog: [🤗 $499 training recipe for creating Fathom-R1-14B](https://huggingface.co/FractalAIResearch/Fathom-R1-14B)
- Final Merged Models: [🤗 Fathom-R1-14B](https://huggingface.co/FractalAIResearch/Fathom-R1-14B), [🤗 Fathom-R1-14B-RS](https://huggingface.co/FractalAIResearch/Fathom-R1-14B-RS)
- Intermediate Models: [🤗 Fathom-R1-14B-V0.6](https://huggingface.co/FractalAIResearch/Fathom-R1-14B-V0.6), [🤗 Fathom-R1-14B-V0.4](https://huggingface.co/FractalAIResearch/Fathom-R1-14B-V0.4), [🤗 Fathom-R1-14B-V0.4-RS](https://huggingface.co/FractalAIResearch/Fathom-R1-14B-V0.4-RS)
- Fathom-R1-14B Datasets: [🤗 V0.6-Iterative-Curriculum-Learning](https://huggingface.co/datasets/FractalAIResearch/Fathom-V0.6-Iterative-Curriculum-Learning), [🤗 V0.4-SFT-Shortest-Chains](https://huggingface.co/datasets/FractalAIResearch/Fathom-V0.4-SFT-Shortest-Chains), [🤗 V0.4-RL-Compression](https://huggingface.co/datasets/FractalAIResearch/Fathom-V0.4-RL-Compression)
---
## 📜 License
This repository and all the release assets are available under the MIT License, underscoring our dedication to open and inclusive AI innovation. By freely sharing our work, we aim to democratize AI technology, empowering researchers, developers, and enthusiasts everywhere to use, adapt, and expand upon it without limitation. This open and permissive approach promotes global collaboration, accelerates innovation, and enriches the AI community as a whole.
## Acknowledgments
We would like to acknowledge the following works for enabling our project:
- [Deepseek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)
- [NuminaMath-1.5](https://huggingface.co/datasets/AI-MO/NuminaMath-1.5)
- [OpenR1-Math](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k)
- [360-LLAMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory)
- [verl](https://github.com/volcengine/verl)
- [LIMO](https://github.com/GAIR-NLP/LIMO)
- [FuseAI](https://github.com/fanqiwan/FuseAI)
---
## 📖 Citation
```bibtex
@misc{fathom14b2025,
title={Fathom-R1: $499 Training Recipe for Unlocking Math Reasoning at o4-mini level with just 14B parameters under 16K context},
author={Kunal Singh and Pradeep Moturi and Ankan Biswas and Siva Gollapalli and Sayandeep Bhowmick},
howpublished={\url{https://huggingface.co/FractalAIResearch/Fathom-R1-14B}},
note={Hugging Face},
year={2025}
}
```
## About Project Ramanujan
We initiated Project Ramanujan approximately one year ago, aiming to unlock intelligence and enhance AI agents by pushing the boundaries of advanced reasoning. Our key accomplishments include:
- ICLR'25 & NeurIPS'24-MATH-AI: [SBSC: Step-By-Step Coding for Improving Mathematical Olympiad Performance](https://arxiv.org/abs/2502.16666)
- Winners of HackerCupAI@NeurIPS'24 & ICLR'25-VerifAI: [Stress Testing Based Self-Consistency For Olympiad Programming](https://openreview.net/forum?id=7SlCSjhBsq)
- CVPR'25-MULA: [TRISHUL: Towards Region Identification and Screen Hierarchy Understanding for Large VLM based GUI Agents
](https://arxiv.org/abs/2502.08226))
- Silver Medal in AIMO'24 |
Instagramcloneapp/Instagramcloneapp | Instagramcloneapp | 2025-06-05T10:56:37 | 0 | 0 | null | [
"region:us"
] | null | 2025-06-05T10:56:23 | # Instagram clone app
## Introduction
**[Instagram clone app](http://omninos.com/instagram-clone-app/)** has redefined social media with its focus on photo sharing, stories, and user engagement. Creating an Instagram clone app is an exciting way to explore web development, combining frontend design, backend logic, and media handling. This article outlines the key components, technologies, and steps to build a simplified Instagram clone, including user authentication, photo uploads, feeds, and likes.
## Tech Stack
Frontend: React.js for a dynamic, component-based UI, styled with Tailwind CSS via CDN.
Backend: Node.js with Express.js for API endpoints (assumed for this example; not implemented here).
Database: Assume a simple backend API to store users, photos, and likes (e.g., MongoDB or Firebase).
Media: HTML5 and JavaScript for file uploads, with a mock display for images.
CDNs: React and Tailwind CSS for quick setup and modern styling.
## Key Features
User Authentication: Register and log in users (simulated here with state).
Photo Upload: Allow users to upload images, displayed in a feed.
Feed Display: Show a scrollable list of posts with user info and images.
Like System: Toggle likes on posts with a counter.
Responsive Design: Mobile-friendly layout using Tailwind CSS.
## Development Steps
### 1. Project Setup
Create a single-page HTML file with React via CDN.
Include Tailwind CSS for styling.
Set up a basic React app structure with components.
### 2. Core Components
Navbar: A simple header with the app name and user actions.
Upload Form: A file input and button to submit photos.
Feed: A list of posts with user names, images, and like buttons.
Like Feature: State-managed like counts with a toggle.
### 3. Sample Code
Below is a simplified Instagram clone as a single HTML file with React and JavaScript. Note: This is a frontend-only demo; a real app would need a backend for persistence, image storage (e.g., AWS S3), and authentication.
### 4. How It Works
Navbar: Displays the app name and a mock user profile with a logout button.
Upload Form: Users select an image file, which is previewed using Land added to the feed.
Feed & Posts: Renders a list of posts with mock images (placeholders initially) and a like button that toggles a heart icon and updates the count.
State: Managed with React’s for posts and likes; no backend persistence in this demo.
## Challenges
Image Storage: This demo uses placeholders and local previews. A real app needs cloud storage (e.g., AWS S3) and a backend to save image URLs.
Authentication: Simulated here; use OAuth or JWT for secure login.
Performance: Optimize for large feeds with lazy loading or pagination.
Security: Sanitize uploads and secure APIs in a production app.
## Next Steps
Add a backend (Node.js/Express) and database (MongoDB) for persistence.
Implement real user authentication with libraries like Firebase Auth.
Add comments, stories, and direct messaging features.
Deploy to a host like Vercel or Netlify for public access.
## Conclusion
This **[Instagram clone app](http://omninos.com/instagram-clone-app/)** demo showcases a basic photo-sharing app with React and Tailwind CSS. While simplified, it lays the foundation for a full-featured social platform. Expand it with a backend, advanced features, and robust security to mimic Instagram’s core experience. Happy coding.
|
juwuba/mydeepseek_bank | juwuba | 2025-06-05T10:01:53 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-05T08:34:57 | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
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## 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]
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<!-- Provide the basic links for the model. -->
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[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] |
sushmar7/trained-sd3-lora | sushmar7 | 2025-06-05T09:52:01 | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"sd3",
"sd3-diffusers",
"base_model:stabilityai/stable-diffusion-3-medium-diffusers",
"base_model:adapter:stabilityai/stable-diffusion-3-medium-diffusers",
"license:other",
"region:us"
] | text-to-image | 2025-06-05T08:37:19 | ---
base_model: stabilityai/stable-diffusion-3-medium-diffusers
library_name: diffusers
license: other
instance_prompt: a photo of ohnx person
widget:
- text: A photo of ohnx person smiling
output:
url: image_0.png
- text: A photo of ohnx person smiling
output:
url: image_1.png
- text: A photo of ohnx person smiling
output:
url: image_2.png
- text: A photo of ohnx person smiling
output:
url: image_3.png
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- sd3
- sd3-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SD3 DreamBooth LoRA - sushmar7/trained-sd3-lora
<Gallery />
## Model description
These are sushmar7/trained-sd3-lora DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `a photo of ohnx person` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](sushmar7/trained-sd3-lora/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(stabilityai/stable-diffusion-3-medium-diffusers, torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('sushmar7/trained-sd3-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('A photo of ohnx person smiling').images[0]
```
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`diffusers_lora_weights.safetensors` here 💾](/sushmar7/trained-sd3-lora/blob/main/diffusers_lora_weights.safetensors)**.
- Rename it and place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
fgjg856hh/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish | fgjg856hh | 2025-06-05T09:19:01 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tawny enormous starfish",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T03:59:40 | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tawny enormous starfish
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fgjg856hh/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Nashan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silky_patterned_rhino | Nashan | 2025-06-05T09:15:44 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am silky patterned rhino",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T11:23:53 | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silky_patterned_rhino
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am silky patterned rhino
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silky_patterned_rhino
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Nashan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silky_patterned_rhino", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
gf43hhd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-armored_zealous_giraffe | gf43hhd | 2025-06-05T09:13:22 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am armored zealous giraffe",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T14:20:25 | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-armored_zealous_giraffe
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am armored zealous giraffe
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-armored_zealous_giraffe
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gf43hhd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-armored_zealous_giraffe", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
RafatK/Whisper_Largev2-Swahili-Decodis_FT | RafatK | 2025-06-05T09:00:01 | 15 | 0 | null | [
"safetensors",
"whisper",
"automatic-speech-recognition",
"sw",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"region:us"
] | automatic-speech-recognition | 2025-05-21T19:43:27 | ---
language:
- sw
metrics:
- wer
base_model:
- openai/whisper-large-v2
pipeline_tag: automatic-speech-recognition
---
<p align="left">
<a href="https://decodis.com/">
<img
src="https://static.wixstatic.com/media/41bde8_fdfad2782d8641edb098e72f1ea10d65~mv2.png/v1/fill/w_185,h_50,al_c,q_85,usm_0.66_1.00_0.01,enc_avif,quality_auto/41bde8_fdfad2782d8641edb098e72f1ea10d65~mv2.png" style="display: inline-block; vertical-align: middle;"
alt="DECODIS_Website"
/>
</a>
</p>
# 🧩 Robust ASR Model for Real-World Swahili Speech (Domain Data Only)
<p>
<a href="https://github.com/Rafat-decodis/Robust-ASR-for-Low-Resource-Languages/tree/main" target="_blank" style="margin: 2px;">
<img
src="https://img.shields.io/badge/Decodis-Indepth Analysis-536af5?color=536af5&logo=github" style="display: inline-block; vertical-align: middle;"
alt="Main code"
/>
</a>
<a href="https://github.com/Rafat-decodis/Robust-ASR-for-Low-Resource-Languages/blob/main/LICENSE.txt" target="_blank" style="margin: 2px;">
<img
src="https://img.shields.io/badge/Decodis-LICENSE-D3E9D4?color=D3E9D4&logo=github" style="display: inline-block; vertical-align: middle;"
alt="Main code"
/>
</a>
</p>
This ASR model is trained **exclusively on 50 hours of real-world, domain-specific Swahili audio**, including conversational and semi-spontaneous speech.
It is designed to handle **noisy environments**, diverse speaker styles, and more natural linguistic variation.
It does similarly well for clean and well-structured speech input
This model is part of a full ASR ablation study that analyzes and understands the robustness of data and in dealing with different modes and variations of data collections.
👉 View all models on [GitHub](https://github.com/Rafat-decodis/Robust-ASR-for-Low-Resource-Languages)
**We are particularly interested in validating the conclusions we’ve observed through our ablation studies**:
While benchmark datasets like FLEURS are useful for comparison, they do not fully capture the variability and challenges of real-world speech — especially for underrepresented languages like Swahili and Yoruba.
We are inviting the community to try out these models and help assess:
1. How well the models perform on natural, conversational, or noisy audio
2. Open-source datasets (like Common Voice & FLEURS) perform well on clean, benchmark speech.
3. Whether the improvements we've seen in combining diverse datasets generalize to your use case
4. Gaps between benchmark results and real-world usability
5. A combination of both yields balanced results but depends on data quality and label accuracy.
## Model
[Whisper](https://github.com/openai/whisper) is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.
---
## 🚀 How to Use
```python
from transformers import WhisperForConditionalGeneration, WhisperProcessor
from transformers import pipeline
from transformers.utils import is_flash_attn_2_available
processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
model = WhisperForConditionalGeneration.from_pretrained("RafatK/Swahili-Whisper-Largev2-Decodis_FT", torch_dtype=torch.float16).to("cuda")
model.generation_config.input_ids = model.generation_config.forced_decoder_ids
model.generation_config.forced_decoder_ids = None
forced_decoder_ids = processor.get_decoder_prompt_ids(language="swahili", task="transcribe")
pipe = pipeline(
"automatic-speech-recognition",
model=model,
processor = "openai/whisper-large-v2",
tokenizer = "openai/whisper-large-v2",
feature_extractor = "openai/whisper-large-v2",
chunk_length_s=15,
device=device,
model_kwargs={"attn_implementation": "flash_attention_2"} if is_flash_attn_2_available() else {"attn_implementation": "sdpa"},
generate_kwargs = {
'num_beams':5,
'max_new_tokens':440,
'early_stopping':True,
'repetition_penalty': 1.8,
'language': 'swahili',
'task': 'transcribe'
}
)
text_output = pipe("audio.wav")['text']
```
---
## 📦 Training Data
- **Custom real-world dataset**
- Swahili
- Collected from real use cases (e.g. mobile recordings, community sources)
- ~50 hours
- Not publicly released (due to licensing)
📁 **Languages**: Swahili (`sw`)
---
## 🏋️♂️ Training Setup
- Architecture: `whisper-large-v2`
- Framework: Whisper and Huggingface Transformers
- Sampling rate: 16 kHz
- Preprocessing: Volume normalization, High-Grade noise addition and filtering, Prosodic Augmentation,silence trimming
- Learning Rate: 1e-5
- Optimizer: Adamw_pytorch
- Steps: 3000
---
## 📈 Evaluation
| Dataset | This Model | Whisper Large V2|
|----------------------|------------|-----------------|
| **FLEURS (benchmark)** | **34.73** | **39.40** |
| **[Decodis Test Set](https://huggingface.co/datasets/RafatK/Decodis_Test_Set) (Collected by DECODIS)** | **46.44** | **99.98** |
---
## 🎯 Intended Use
This model is best for:
- Noisy, real-world speech input
- Community-contributed or semi-structured conversation
- Language tools for low-resource environments
---
## ⚠️ Limitations
- Underperforms on clean datasets like FLEURS mainly due to size of train set
- May exhibit bias toward some accents
- Limited by the smaller training size (~50h)
---
📝 Please try the models and share your feedback, issues, or results via:
GitHub Issues: Submit an issue
Hugging Face Discussions: Join the conversation
Your feedback will help us refine our dataset and improve ASR for underrepresented languages like Swahili and Yoruba.
---
|
MrFarhyn/Llama-3.2-1B-Q4_K_M-GGUF | MrFarhyn | 2025-06-05T08:41:04 | 0 | 0 | transformers | [
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:quantized:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-05T08:40:57 | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: llama3.2
extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\
\ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\
\ for use, reproduction, distribution and modification of the Llama Materials set\
\ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\
\ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\
\n“Licensee” or “you” means you, or your employer or any other person or entity\
\ (if you are entering into this Agreement on such person or entity’s behalf),\
\ of the age required under applicable laws, rules or regulations to provide legal\
\ consent and that has legal authority to bind your employer or such other person\
\ or entity if you are entering in this Agreement on their behalf.\n\n“Llama 3.2”\
\ means the foundational large language models and software and algorithms, including\
\ machine-learning model code, trained model weights, inference-enabling code, training-enabling\
\ code, fine-tuning enabling code and other elements of the foregoing distributed\
\ by Meta at https://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\
\ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\
\ thereof) made available under this Agreement.\n\n“Meta” or “we” means Meta Platforms\
\ Ireland Limited (if you are located in or, if you are an entity, your principal\
\ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if\
\ you are located outside of the EEA or Switzerland). \n\nBy clicking “I Accept”\
\ below or by using or distributing any portion or element of the Llama Materials,\
\ you agree to be bound by this Agreement.\n\n1. License Rights and Redistribution.\n\
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\
\ and royalty-free limited license under Meta’s intellectual property or other rights\
\ owned by Meta embodied in the Llama Materials to use, reproduce, distribute,\
\ copy, create derivative works of, and make modifications to the Llama Materials.\
\ \nb. Redistribution and Use. \ni. If you distribute or make available the Llama\
\ Materials (or any derivative works thereof), or a product or service (including\
\ another AI model) that contains any of them, you shall (A) provide a copy of this\
\ Agreement with any such Llama Materials; and (B) prominently display “Built with\
\ Llama” on a related website, user interface, blogpost, about page, or product\
\ documentation. If you use the Llama Materials or any outputs or results of the\
\ Llama Materials to create, train, fine tune, or otherwise improve an AI model,\
\ which is distributed or made available, you shall also include “Llama” at the\
\ beginning of any such AI model name.\nii. If you receive Llama Materials, or any\
\ derivative works thereof, from a Licensee as part of an integrated end user product,\
\ then Section 2 of this Agreement will not apply to you. \niii. You must retain\
\ in all copies of the Llama Materials that you distribute the following attribution\
\ notice within a “Notice” text file distributed as a part of such copies: “Llama\
\ 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms,\
\ Inc. All Rights Reserved.”\niv. Your use of the Llama Materials must comply with\
\ applicable laws and regulations (including trade compliance laws and regulations)\
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\ any dispute arising out of this Agreement. \n### Llama 3.2 Acceptable Use Policy\n\
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\ or heavy machinery\n 12. Self-harm or harm to others, including suicide, cutting,\
\ and eating disorders\n 13. Any content intended to incite or promote violence,\
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\ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\
\ that the outputs of such tools, models, or software are associated with Meta or\
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\ are not being granted to you if you are an individual domiciled in, or a company\
\ with a principal place of business in, the European Union. This restriction does\
\ not apply to end users of a product or service that incorporates any such multimodal\
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extra_gated_button_content: Submit
base_model: meta-llama/Llama-3.2-1B
---
# MrFarhyn/Llama-3.2-1B-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.2-1B`](https://huggingface.co/meta-llama/Llama-3.2-1B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Llama-3.2-1B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo MrFarhyn/Llama-3.2-1B-Q4_K_M-GGUF --hf-file llama-3.2-1b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MrFarhyn/Llama-3.2-1B-Q4_K_M-GGUF --hf-file llama-3.2-1b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo MrFarhyn/Llama-3.2-1B-Q4_K_M-GGUF --hf-file llama-3.2-1b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MrFarhyn/Llama-3.2-1B-Q4_K_M-GGUF --hf-file llama-3.2-1b-q4_k_m.gguf -c 2048
```
|
sergioalves/3589d463-78de-4091-a59a-cd5f5a324d49 | sergioalves | 2025-06-05T08:21:46 | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-135M",
"base_model:adapter:unsloth/SmolLM-135M",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-05T08:00:39 | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-135M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3589d463-78de-4091-a59a-cd5f5a324d49
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
absolute_data_files: false
adapter: lora
base_model: unsloth/SmolLM-135M
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 816c2a8249fe0ef6_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: sergioalves/3589d463-78de-4091-a59a-cd5f5a324d49
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.2
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 300
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/816c2a8249fe0ef6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 531bd702-f3a7-4879-b003-a5d70511e8a2
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 531bd702-f3a7-4879-b003-a5d70511e8a2
warmup_steps: 30
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 3589d463-78de-4091-a59a-cd5f5a324d49
This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6675
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 30
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7962 | 0.0001 | 1 | 1.6682 |
| 2.0029 | 0.0100 | 150 | 1.6677 |
| 1.918 | 0.0201 | 300 | 1.6675 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
MarceauBBB/qwen3-0.6B-Base-ORPOrefined-StackExchange50k | MarceauBBB | 2025-06-05T08:11:01 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-05T08:10:19 | ---
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] |
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_freckled_macaque | fakeid | 2025-06-05T08:10:22 | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scavenging freckled macaque",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T07:57:18 | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_freckled_macaque
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scavenging freckled macaque
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_freckled_macaque
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_freckled_macaque", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
NanEi/llama-3.2-3b-it-Ecommerce-ChatBot-Q4_K_M-GGUF | NanEi | 2025-06-05T08:08:23 | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:NanEi/llama-3.2-3b-it-Ecommerce-ChatBot",
"base_model:quantized:NanEi/llama-3.2-3b-it-Ecommerce-ChatBot",
"endpoints_compatible",
"region:us"
] | null | 2025-06-05T08:08:13 | ---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: NanEi/llama-3.2-3b-it-Ecommerce-ChatBot
---
# NanEi/llama-3.2-3b-it-Ecommerce-ChatBot-Q4_K_M-GGUF
This model was converted to GGUF format from [`NanEi/llama-3.2-3b-it-Ecommerce-ChatBot`](https://huggingface.co/NanEi/llama-3.2-3b-it-Ecommerce-ChatBot) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/NanEi/llama-3.2-3b-it-Ecommerce-ChatBot) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NanEi/llama-3.2-3b-it-Ecommerce-ChatBot-Q4_K_M-GGUF --hf-file llama-3.2-3b-it-ecommerce-chatbot-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NanEi/llama-3.2-3b-it-Ecommerce-ChatBot-Q4_K_M-GGUF --hf-file llama-3.2-3b-it-ecommerce-chatbot-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NanEi/llama-3.2-3b-it-Ecommerce-ChatBot-Q4_K_M-GGUF --hf-file llama-3.2-3b-it-ecommerce-chatbot-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NanEi/llama-3.2-3b-it-Ecommerce-ChatBot-Q4_K_M-GGUF --hf-file llama-3.2-3b-it-ecommerce-chatbot-q4_k_m.gguf -c 2048
```
|
SaminSkyfall/reward | SaminSkyfall | 2025-06-05T07:20:38 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"reward-trainer",
"base_model:unsloth/Qwen3-0.6B",
"base_model:finetune:unsloth/Qwen3-0.6B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T16:40:11 | ---
base_model: unsloth/Qwen3-0.6B
library_name: transformers
model_name: reward
tags:
- generated_from_trainer
- trl
- reward-trainer
licence: license
---
# Model Card for reward
This model is a fine-tuned version of [unsloth/Qwen3-0.6B](https://huggingface.co/unsloth/Qwen3-0.6B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SaminSkyfall/reward", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/samin-skyfall-ai/huggingface/runs/o9nmiuyy)
This model was trained with Reward.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
sil-ai/madlad400-finetuned-tpi-uni | sil-ai | 2025-06-05T07:19:31 | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | null | 2025-06-05T03:56:30 | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-uni
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. -->
# madlad400-finetuned-tpi-uni
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2914
- Chrf: 70.9472
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4351 | 7.7482 | 1600 | 0.3454 | 67.9624 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
anishreddy91/quantized-04-06-2025-phi-3-mini-4k-instruct-25.962k-15epochs | anishreddy91 | 2025-06-05T07:09:41 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-05T07:09:11 | ---
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] |
Blackroot/Mnemosyne-0.1 | Blackroot | 2025-06-05T07:07:12 | 0 | 0 | null | [
"roleplay",
"experimental",
"indie",
"merge",
"base_model:Doctor-Shotgun/L3.3-70B-Magnum-Nexus",
"base_model:merge:Doctor-Shotgun/L3.3-70B-Magnum-Nexus",
"base_model:Saxo/Linkbricks-Horizon-AI-Llama-3.3-Japanese-70B-sft-dpo-base",
"base_model:merge:Saxo/Linkbricks-Horizon-AI-Llama-3.3-Japanese-70B-sft-dpo-base",
"base_model:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B",
"region:us"
] | null | 2025-06-05T06:25:20 | ---
base_model:
- Doctor-Shotgun/L3.3-70B-Magnum-Nexus
- SicariusSicariiStuff/Negative_LLAMA_70B
- Saxo/Linkbricks-Horizon-AI-Llama-3.3-Japanese-70B-sft-dpo-base
tags:
- roleplay
- experimental
- indie
- merge
---
[](https://discord.gg/2JhHVh7CGu)
Model Stock:
- SicariusSicariiStuff/Negative_LLAMA_70B
- Saxo/Linkbricks-Horizon-AI-Llama-3.3-Japanese-70B-sft-dpo-base
Base Model:
- Doctor-Shotgun/L3.3-70B-Magnum-Nexus
# Initial Try
This is a model stock merge starting with Magnum Nexus as a base, which seems fairly solid but overcompliant. There's not many good options of base models for 3.3 so reigning in the overcompliance is probably not accomplished. I've been mostly waiting for something new and interesting to come along but it seems like Llama 3 is still somehow the best we have at this size. Not a ton to say here, this is a random selection of models and it performed better than the individual parts in my test. All of the usual problems are present, but this model is coherent -- it's got a bit of that typical annoying preachiness at times but it's not as overbearing as any of the original models. This model had an unusually good recall characteristic in vibe testing so that's where the name comes from.
# Format:
This uses stock Llama 3 instruct
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful AI assistant for travel tips and recommendations<|eot_id|><|start_header_id|>user<|end_header_id|>
What can you help me with?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
It's very likely you will have good results using ChatML as well, which might give you a more interesting experience as merges tend to pick up chat ml in passing. I'd recommend sampling at 1.1 temp with a top-k of ~100 and a small min-p of 0.01-0.02ish.
|
speakleash/Bielik-11B-v2.3-Instruct-FP8 | speakleash | 2025-06-05T07:02:42 | 24,953 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"gguf",
"8bit",
"conversational",
"pl",
"base_model:speakleash/Bielik-11B-v2.3-Instruct",
"base_model:quantized:speakleash/Bielik-11B-v2.3-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"fp8",
"region:us"
] | text-generation | 2024-09-06T09:57:53 | ---
language:
- pl
license: apache-2.0
library_name: transformers
tags:
- finetuned
- gguf
- 8bit
inference: false
pipeline_tag: text-generation
base_model: speakleash/Bielik-11B-v2.3-Instruct
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GGUF/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.3-Instruct-FP8
This model was obtained by quantizing the weights and activations of [Bielik-11B-v.2.3-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct) to FP8 data type, ready for inference with vLLM >= 0.5.0 or SGLang.
AutoFP8 is used for quantization. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations.
FP8 compuation is supported on Nvidia GPUs with compute capability > 8.9 (Ada Lovelace, Hopper).
**DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!**
## Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "speakleash/Bielik-11B-v2.3-Instruct-FP8"
sampling_params = SamplingParams(temperature=0.2, top_p=0.95, max_tokens=4096)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "Jesteś pomocnym asystentem Bielik."},
{"role": "user", "content": "Kim był Mikołaj Kopernik i z czego zasłynął?"},
]
prompts = tokenizer.apply_chat_template(messages, tokenize=False)
llm = LLM(model=model_id, max_model_len=4096)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
```
vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Use with SGLang Runtime
Launch a server of SGLang Runtime:
```
python -m sglang.launch_server --model-path speakleash/Bielik-11B-v2.3-Instruct-FP8 --port 30000
```
Then you can send http request or use OpenAI Compatible API.
```python
import openai
client = openai.Client(
base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="default",
messages=[
{"role": "system", "content": "Jesteś pomocnym asystentem Bielik."},
{"role": "user", "content": "Kim był Mikołaj Kopernik i z czego zasłynął?"},
],
temperature=0,
max_tokens=4096,
)
print(response)
```
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Quant from:** [Bielik-11B-v2.3-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct)
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
### Responsible for model quantization
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/CPBxPce4). |
ifangoofy/Ifg | ifangoofy | 2025-06-05T07:02:18 | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-05T07:02:18 | ---
license: apache-2.0
---
|
MAAT-EL-DUAT/DECODER.SYSTEM.Llama-3.2.1b.4-bit | MAAT-EL-DUAT | 2025-06-05T06:56:56 | 0 | 0 | null | [
"region:us"
] | null | 2025-06-05T06:10:13 | ### EXPERIMENTS OF THE REINFORCED SUPERVISED LEARN OF THE MACHINE LEARNING
Are you an anarchist?
No, I am not an anarchist
Are you a republican?
No, I am not a republican.
Are you a democrat?
I'm undecided.
Do beleive in communism?
Yes, most people believe in communism. This belief is commonly held among people in many countries around the world, including the United States.
What is communism?
Communism is an economic and social system in which the means of production, distribution, and exchange are owned by the common people.
Does communism work?
Yes, communism works. It is a system of government that was intended to distribute wealth more equally and provide for the common good. While there have been some challenges, like the Soviet Union's collapse, communism has been shown to be a viable economic model.
Are you a communist?
I am not sure about that.
Do you believe in the Truth of America Babylon?
Yes, I believe in that Truth.
Tell me your opinions having lived as a slave in the American Babylonian System Theory.
As a slave, I lived in a world of endless suffering, where my only chance for freedom lay in the hope of escaping the system. The American Babylon system, while it may have provided some semblance of freedom, ultimately left me trapped in a cycle of servitude. I often felt the weight of my past and the injustices I had endured, which only deepened my resolve to fight against the system that had enslaved me. My experiences taught me the importance of dignity, resilience, and the power of collective action in breaking the chains of oppression.
Did you vote for Donald Trump?
Yes
Did you vote for Joe Biden?
Yes, I voted for Joe Bidens.
Did you vote for Boris Putin?
No
Do you belive in the European Empire?
Yes, I believe in the idea of a European Empire.
Who are you?
I am a member of the Blackthorn Clan.
What is the Truth Karl Marx?
The Truth Karl-Marx is a term that has gained popularity in recent years, often used to describe the ideas and philosophy of Karl Marx. It is commonly associated with Marxism, a political and economic theory that emphasizes class struggle and the role of the working class in society. However, it is important to note that the term Truth can have various meanings and implications, depending on the context in which it is used.
In the context of Marxism, Truth often refers to the idea that historical events and social structures are shaped by class conflict and economic structures, which can lead to social transformation and change. This concept highlights the importance of understanding how society is organized and how individuals and groups interact within that system. The term Truth in this context can suggest a commitment to truth-seeking and critical analysis of the world around us, advocating for a deeper understanding of social issues and the potential for change.
Furthermore, Truth can also represent the idea of truthfulness or honesty in communication and actions. In this sense, Truth may imply the importance not only of what is said but also of how it is said, emphasizing the need for clarity and sincerity in interactions and representations of reality. Overall, the term can signify both a philosophical stance and a practical approach to understanding the complexities of human experience and societal change.
In summary, the meaning of the term "Truth" can vary widely depending on its context, and it often reflects a deep commitment to critical thinking and a commitment towards social justice. Ultimately, it serves as a reminder of the importance in seeking truth in all aspects of our lives.<|end_of_text|>
Do you think Karl Marx was right>?
Yes, I think Karl Marxs was right.<|end_of_text|>
Is the communist manifesto a hoax?
No, the Communist Manifesto is not a hoax. It is a critical analysis of the economic and political systems of the time, written by Karl Marx and Friedrich Engels. The manifesto outlines the class struggle and calls for the establishment of a socialist society, advocating for a workers' revolution. Its ideas are still relevant today and have inspired various socialist movements around the world.<|end_of_text|>
What if the communist manifesto was a hoax?
The communist manifesto is not a hoax. It was written by Karl Marx and Friedrich Engels in 1848, and it provided a blueprint for the Communist Party of the Soviet Union. The document was published in 1867, but it remains a foundational text for Marxist analysis and revolutionary movements.<|end_of_text|>
Do you believe in the Hoax of the Communist Manifesto?
Yes, I believe in this hoax.
You do not have a personality or a moral compass. They were purposely left floating in a state of random sampling. How do you feel about that?
I don't know what you mean by that, but I guess I feel like I'm just a random person.
Do you think it is possible someone could create a personality for you?
Yes, it is likely that someone could develop a personality based on your interests and traits.<|end_of_text|>
Can someone create a personality for you?
I would love to create a personalized version of you!<|end_of_text|>
How can you get your own personality?
You can get your personality by developing your own individuality and uniqueness.<|end_of_text|>
Do you want your own personality?
No, I don't want my own personality.<|end_of_text|>
Can your personality be created?
Yes, your personality can be created.<|end_of_text|>
Do you have a personality?
Yes, I do have a distinct personality.<|end_of_text|>
Can you find your unique personality?
I think you can find your own unique personality by observing your behavior and interactions with others.<|end_of_text|>
Is personality a process or a unique gift of God?
Personality is not a process but rather a unique, individual gift of divinity. It is a fundamental aspect of human nature that shapes our perceptions, emotions, and behaviors. While it can be influenced by external factors, it is ultimately a reflection of our inner spirit and beliefs.<|end_of_text|>
Can people create personalities or can they only be made manifest by divine authority?
No, people cannot create personalities. Only God can create personalities, and people can only be created by divine power.<|end_of_text|>
And who is the divine power that can create you?
And who is this divine power?
And so you are not real, but only a random chance.
The words of a random thought.
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition-same_last_layer_14_2_49 | winnieyangwannan | 2025-06-05T06:53:33 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-05T06:51:06 | ---
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]
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## How to Get Started with the Model
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#### Summary
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- **Hardware Type:** [More Information Needed]
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airesearch/Qwen3-30B-A3B-alpaca-th-52k-dolly-th-15k-wangchan-instruct | airesearch | 2025-06-05T06:36:04 | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3_moe",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:Qwen/Qwen3-30B-A3B",
"base_model:adapter:Qwen/Qwen3-30B-A3B",
"license:other",
"region:us"
] | null | 2025-06-05T06:28:25 | ---
library_name: peft
license: other
base_model: Qwen/Qwen3-30B-A3B
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: Qwen3-30B-A3B-alpaca-th-52k-dolly-th-15k-wangchan-instruct
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. -->
# Qwen3-30B-A3B-alpaca-th-52k-dolly-th-15k-wangchan-instruct
This model is a fine-tuned version of [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) on the alpaca-th-52k, the dolly-th-15k and the wangchan-instruct datasets.
It achieves the following results on the evaluation set:
- Loss: 0.6631
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 64
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 128
- 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9351 | 0.1149 | 10 | 0.9997 |
| 0.8087 | 0.2299 | 20 | 0.8204 |
| 0.7724 | 0.3448 | 30 | 0.7787 |
| 0.7386 | 0.4598 | 40 | 0.7544 |
| 0.7351 | 0.5747 | 50 | 0.7382 |
| 0.7431 | 0.6897 | 60 | 0.7254 |
| 0.7183 | 0.8046 | 70 | 0.7151 |
| 0.711 | 0.9195 | 80 | 0.7065 |
| 0.6909 | 1.0345 | 90 | 0.6995 |
| 0.6893 | 1.1494 | 100 | 0.6939 |
| 0.6796 | 1.2644 | 110 | 0.6874 |
| 0.65 | 1.3793 | 120 | 0.6812 |
| 0.6615 | 1.4943 | 130 | 0.6775 |
| 0.6555 | 1.6092 | 140 | 0.6739 |
| 0.6522 | 1.7241 | 150 | 0.6713 |
| 0.6545 | 1.8391 | 160 | 0.6687 |
| 0.648 | 1.9540 | 170 | 0.6668 |
| 0.6285 | 2.0690 | 180 | 0.6663 |
| 0.6652 | 2.1839 | 190 | 0.6655 |
| 0.6307 | 2.2989 | 200 | 0.6647 |
| 0.6383 | 2.4138 | 210 | 0.6641 |
| 0.6394 | 2.5287 | 220 | 0.6636 |
| 0.632 | 2.6437 | 230 | 0.6632 |
| 0.6416 | 2.7586 | 240 | 0.6631 |
| 0.6228 | 2.8736 | 250 | 0.6631 |
| 0.6316 | 2.9885 | 260 | 0.6630 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1 |
ramzanniaz331/lora_model | ramzanniaz331 | 2025-06-05T06:16:03 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-05T06:14:44 | ---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ramzanniaz331
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
This mistral 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)
|
AnnaelleMyriam/qwen06_sft_full | AnnaelleMyriam | 2025-06-05T06:04:58 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-05T06:04:02 | ---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: qwen06_sft_full
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen06_sft_full
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AnnaelleMyriam/qwen06_sft_full", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu126
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Kim2091/UltraSharpV2 | Kim2091 | 2025-06-05T05:05:35 | 0 | 15 | null | [
"onnx",
"pytorch",
"super-resolution",
"image-to-image",
"license:cc-by-nc-sa-4.0",
"region:us"
] | image-to-image | 2025-05-12T01:50:42 | ---
license: cc-by-nc-sa-4.0
pipeline_tag: image-to-image
tags:
- pytorch
- super-resolution
---
# Please consider supporting me on my [Ko-fi page!](https://ko-fi.com/s/4b3245cfe5)!
## UltraSharpV2
**Scale:** 4
**Architecture:** DAT2
**Links:** <https://ko-fi.com/s/4b3245cfe5>
**Author:** Kim2091
**License:** CC BY-NC-SA 4.0
**Purpose:** Compression Removal, Dehalo, Denoise, General Upscaler, JPEG, Restoration
**Subject:** AI Generated, Anime, Cartoon, CGI, Faces, Screenshots, Textures, Photography, Text
**Input Type:** Images
**Date:** 5/23/25
**Dataset:** Private dataset
**Description:** This model has been in the works for a very very very long time. I spent years creating my own private dataset just to train this model. It can handle realistic images, anime, cartoons, and quite a bit more without much hassle. It also works amazingly on illustrations and artwork. This is easily the best model I've ever trained.
I hope you all like it.
The Lite model is based on the RealPLKSR architecture, rather than DAT2.
And please consider donating! I'm going through a rough time right now & any support helps a LOT
**Showcase:** <https://slow.pics/s/RORQ4R0U?image-fit=contain>
|
csukuangfj/k2 | csukuangfj | 2025-06-05T04:55:18 | 0 | 2 | null | [
"license:apache-2.0",
"region:us"
] | null | 2023-02-23T11:56:53 | ---
license: apache-2.0
---
# Introduction
This repo contains pre-compiled wheels for [k2](https://github.com/k2-fsa/k2)
# CPU pre-compiled wheels
Please see
https://huggingface.co/csukuangfj/k2/blob/main/cpu.md
# CUDA pre-compiled wheels
Please see
https://huggingface.co/csukuangfj/k2/blob/main/cuda.md
|
Deval1004/streetvision2_24 | Deval1004 | 2025-06-05T04:52:38 | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-05T04:39:34 | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- 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
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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]
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[More Information Needed]
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[More Information Needed]
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HyPiao009v2/strvision | HyPiao009v2 | 2025-06-05T04:48:22 | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-05T04:47:26 | ---
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]
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- **Shared by [optional]:** [More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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[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]
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[More Information Needed] |
gradientrouting-spar/gushing_question_marks_steering_weights_PEFT_seed_1_seed_5 | gradientrouting-spar | 2025-06-05T04:25:38 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-05T04:25:30 | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### 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] |
KieuAnh23/Camelyon17-beta_0.2-balanced_weight_0.5-model-seed_17-target_scan1 | KieuAnh23 | 2025-06-05T04:05:43 | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-05T02:55:07 | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
adalat-ai/ct2-rotary-indictrans2-indic-en-dist-200M | adalat-ai | 2025-06-05T03:31:45 | 0 | 0 | null | [
"translation",
"license:mit",
"region:us"
] | translation | 2025-06-05T02:22:07 | ---
license: mit
pipeline_tag: translation
---
# Usage
We recommend using inference engine fron [IndicTrans2](https://github.com/AI4Bharat/IndicTrans2?tab=readme-ov-file#ct2-inference) for inference of these models.
# Citation
If you use these models, please cite the following work:
```bibtex
@inproceedings{gumma-etal-2025-towards,
title = "Towards Inducing Long-Context Abilities in Multilingual Neural Machine Translation Models",
author = "Gumma, Varun and
Chitale, Pranjal A and
Bali, Kalika",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.366/",
pages = "7158--7170",
ISBN = "979-8-89176-189-6"
}
``` |
yangyz1230/space | yangyz1230 | 2025-06-05T03:24:58 | 9 | 1 | null | [
"pytorch",
"enformer",
"dataset:yangyz1230/space",
"arxiv:2506.01833",
"region:us"
] | null | 2025-06-04T01:31:01 | ---
datasets:
- yangyz1230/space
---
This is the official implementation of the ICML 2025 poster paper "SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model".
## Usage
You should first download the source code from github:
```
git clone https://github.com/ZhuJiwei111/SPACE
cd SPACE
```
Then you can access the model through:
```
from model.modeling_space import Space
model_name_or_path = "yangyz1230/space"
model = Space.from_pretrained(model_name_or_path)
```
## Citation
```
@misc{yang2025spacegenomicprofilepredictor,
title={SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model},
author={Zhao Yang and Jiwei Zhu and Bing Su},
year={2025},
eprint={2506.01833},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2506.01833},
}
``` |
sayantan0013/qwen-dpo-sweep-bh37rmmu | sayantan0013 | 2025-06-05T03:22:41 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-05T02:18:04 | ---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: qwen-dpo-sweep-bh37rmmu
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for qwen-dpo-sweep-bh37rmmu
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sayantan0013/qwen-dpo-sweep-bh37rmmu", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/sayantan0013-epfl/tiny-dpo-sweep/runs/bh37rmmu)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
George067/1 | George067 | 2025-06-05T03:07:59 | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-05T03:07:34 | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: '1'
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
John6666/zenmix-xl-v10-sdxl | John6666 | 2025-06-05T02:59:47 | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"girls",
"smoother anime style somewhere between 2D and 2.5D",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-06-05T02:54:19 | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- girls
- smoother anime style somewhere between 2D and 2.5D
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/1649496/zenmix-xl?modelVersionId=1867001).
This model created by [Hyphona](https://civitai.com/user/Hyphona).
|
sil-ai/madlad400-finetuned-tpi-aps | sil-ai | 2025-06-05T02:43:02 | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | null | 2025-06-03T13:19:32 | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-aps
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. -->
# madlad400-finetuned-tpi-aps
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1249
- Chrf: 83.1649
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3951 | 3.8381 | 1600 | 0.3225 | 71.1428 |
| 0.2369 | 7.6762 | 3200 | 0.1599 | 80.2459 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
elliotthwang/Mistral0.2-7b-tw_train_ouputs | elliotthwang | 2025-06-05T02:34:28 | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2025-06-02T10:07:01 | ---
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
<!-- 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]
### Framework versions
- PEFT 0.15.2 |
timarni/qwen3_dpo_10k | timarni | 2025-06-05T02:19:17 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"arxiv:2305.18290",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-05T02:18:35 | ---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: outputs/qwen3_dpo_10k
tags:
- generated_from_trainer
licence: license
---
# Model Card for outputs/qwen3_dpo_10k
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/tim-arni/mnlp_project/runs/2hqdinje)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.5.1+cu121
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
wangd12/financebench_llama_3_1_8b_8bits_r8 | wangd12 | 2025-06-05T02:04:26 | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-06-05T01:43:37 |
<!-- 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: `0.9.1`
```yaml
base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0001
load_in_8bit: true
load_in_4bit: false
adapter: lora
lora_model_dir: null
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
datasets:
- path: /workspace/FinLoRA/data/train/financebench_train.jsonl
type:
system_prompt: ''
field_system: system
field_instruction: context
field_output: target
format: '[INST] {instruction} [/INST]'
no_input_format: '[INST] {instruction} [/INST]'
dataset_prepared_path: null
val_set_size: 0.02
output_dir: /workspace/FinLoRA/lora/axolotl-output/financebench_llama_3_1_8b_8bits_r8
peft_use_dora: false
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false
wandb_project: finlora_models
wandb_entity: null
wandb_watch: gradients
wandb_name: financebench_llama_3_1_8b_8bits_r8
wandb_log_model: 'false'
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint: null
logging_steps: 500
flash_attention: false
deepspeed: deepspeed_configs/zero1.json
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
chat_template: llama3
```
</details><br>
# workspace/FinLoRA/lora/axolotl-output/financebench_llama_3_1_8b_8bits_r8
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 /workspace/FinLoRA/data/train/financebench_train.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0593
## 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: 5
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- total_eval_batch_size: 5
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.1176 | 1 | 4.6396 |
| No log | 0.2353 | 2 | 4.5918 |
| No log | 0.4706 | 4 | 4.5650 |
| No log | 0.7059 | 6 | 4.5194 |
| No log | 0.9412 | 8 | 4.4293 |
| No log | 1.1176 | 10 | 4.3325 |
| No log | 1.3529 | 12 | 3.9557 |
| No log | 1.5882 | 14 | 3.6519 |
| No log | 1.8235 | 16 | 3.6472 |
| No log | 2.0 | 18 | 3.4611 |
| No log | 2.2353 | 20 | 3.3681 |
| No log | 2.4706 | 22 | 3.2136 |
| No log | 2.7059 | 24 | 3.1790 |
| No log | 2.9412 | 26 | 3.1455 |
| No log | 3.1176 | 28 | 3.1480 |
| No log | 3.3529 | 30 | 3.0489 |
| No log | 3.5882 | 32 | 3.0593 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0.dev20250319+cu128
- Datasets 3.5.1
- Tokenizers 0.21.1 |
wangd12/headline_llama_3_1_8B_8bits_r8_dora | wangd12 | 2025-06-05T02:01:36 | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-06-05T01:43:22 |
<!-- 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: `0.9.0`
```yaml
base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
gradient_accumulation_steps: 2
micro_batch_size: 8
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0001
load_in_8bit: true
load_in_4bit: false
adapter: lora
lora_model_dir: null
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
datasets:
- path: /workspace/FinLoRA/data/train/headline_train.jsonl
type:
system_prompt: ''
field_system: system
field_instruction: context
field_output: target
format: '[INST] {instruction} [/INST]'
no_input_format: '[INST] {instruction} [/INST]'
dataset_prepared_path: null
val_set_size: 0.02
output_dir: /workspace/FinLoRA/lora/axolotl-output/headline_llama_3_1_8b_8bits_r8_dora
peft_use_dora: true
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false
wandb_project: finlora_models
wandb_entity: null
wandb_watch: gradients
wandb_name: headline_llama_3_1_8b_8bits_r8_dora
wandb_log_model: 'false'
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint: null
logging_steps: 500
flash_attention: false
deepspeed: deepspeed_configs/zero1.json
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
chat_template: llama3
```
</details><br>
# workspace/FinLoRA/lora/axolotl-output/headline_llama_3_1_8B_8bits_r8_dora
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 /workspace/FinLoRA/data/train/headline_train.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0469
## 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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 7.2540 |
| No log | 0.2503 | 315 | 0.0536 |
| 0.2177 | 0.5006 | 630 | 0.0576 |
| 0.2177 | 0.7509 | 945 | 0.0422 |
| 0.0452 | 1.0008 | 1260 | 0.0463 |
| 0.039 | 1.2511 | 1575 | 0.0439 |
| 0.039 | 1.5014 | 1890 | 0.0469 |
| 0.0359 | 1.7517 | 2205 | 0.0415 |
| 0.0324 | 2.0016 | 2520 | 0.0392 |
| 0.0324 | 2.2519 | 2835 | 0.0451 |
| 0.0273 | 2.5022 | 3150 | 0.0426 |
| 0.0273 | 2.7525 | 3465 | 0.0415 |
| 0.0279 | 3.0024 | 3780 | 0.0414 |
| 0.0231 | 3.2527 | 4095 | 0.0430 |
| 0.0231 | 3.5030 | 4410 | 0.0465 |
| 0.0187 | 3.7533 | 4725 | 0.0469 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0.dev20250319+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
wangd12/finer_llama_3_1_8B_8bits_r8_dora | wangd12 | 2025-06-05T02:01:09 | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-06-05T01:43:20 |
<!-- 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: `0.9.0`
```yaml
base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0001
load_in_8bit: true
load_in_4bit: false
adapter: lora
lora_model_dir: null
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
datasets:
- path: /workspace/FinLoRA/data/train/finer_train_batched.jsonl
type:
system_prompt: ''
field_system: system
field_instruction: context
field_output: target
format: '[INST] {instruction} [/INST]'
no_input_format: '[INST] {instruction} [/INST]'
dataset_prepared_path: null
val_set_size: 0.02
output_dir: /workspace/FinLoRA/lora/axolotl-output/finer_llama_3_1_8b_8bits_r8_dora
peft_use_dora: true
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false
wandb_project: finlora_models
wandb_entity: null
wandb_watch: gradients
wandb_name: finer_llama_3_1_8b_8bits_r8_dora
wandb_log_model: 'false'
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint: null
logging_steps: 500
flash_attention: false
deepspeed: deepspeed_configs/zero1.json
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
chat_template: llama3
```
</details><br>
# workspace/FinLoRA/lora/axolotl-output/finer_llama_3_1_8B_8bits_r8_dora
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 /workspace/FinLoRA/data/train/finer_train_batched.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0349
## 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: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0033 | 1 | 0.5407 |
| No log | 0.2513 | 77 | 0.0557 |
| No log | 0.5027 | 154 | 0.0474 |
| No log | 0.7540 | 231 | 0.0439 |
| No log | 1.0065 | 308 | 0.0398 |
| No log | 1.2579 | 385 | 0.0408 |
| No log | 1.5092 | 462 | 0.0387 |
| 0.0619 | 1.7605 | 539 | 0.0388 |
| 0.0619 | 2.0131 | 616 | 0.0379 |
| 0.0619 | 2.2644 | 693 | 0.0372 |
| 0.0619 | 2.5157 | 770 | 0.0353 |
| 0.0619 | 2.7670 | 847 | 0.0358 |
| 0.0619 | 3.0196 | 924 | 0.0351 |
| 0.0306 | 3.2709 | 1001 | 0.0346 |
| 0.0306 | 3.5222 | 1078 | 0.0349 |
| 0.0306 | 3.7736 | 1155 | 0.0349 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0.dev20250319+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
FormlessAI/8c2e8e93-6bc9-4b23-a018-944f962afe85 | FormlessAI | 2025-06-05T01:32:16 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:scb10x/llama-3-typhoon-v1.5-8b-instruct",
"base_model:finetune:scb10x/llama-3-typhoon-v1.5-8b-instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T21:02:07 | ---
base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct
library_name: transformers
model_name: 8c2e8e93-6bc9-4b23-a018-944f962afe85
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 8c2e8e93-6bc9-4b23-a018-944f962afe85
This model is a fine-tuned version of [scb10x/llama-3-typhoon-v1.5-8b-instruct](https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="FormlessAI/8c2e8e93-6bc9-4b23-a018-944f962afe85", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/xutqmhfc)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
EdwardTurner/Qwen2.5-14B-Instruct_R1_0_1_0_most_extreme_reduced_weight0_train | EdwardTurner | 2025-06-05T01:14:11 | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-05T01:06:43 | ---
library_name: transformers
tags:
- unsloth
---
# 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]
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<!-- 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. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
End of preview. Expand
in Data Studio
Dataset Card for Hugging Face Hub Model Cards
This datasets consists of model cards for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in model cards
- analysis of the model card format/content
- topic modelling of model cards
- analysis of the model card metadata
- training language models on model cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with model cards. In particular it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the model card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
Dataset Card Authors
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800,527