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osanseviero
authored
a
paper
2 months ago
Post
4293
There seems to multiple paid apps shared here that are based on models on hf, but some ppl sell their wrappers as "products" and promote them here. For a long time, hf was the best and only platform to do oss model stuff but with the recent AI website builders anyone can create a product (really crappy ones btw) and try to sell it with no contribution to oss stuff. Please dont do this, or try finetuning the models you use...
Sorry for filling yall feed with this bs but yk...
Sorry for filling yall feed with this bs but yk...
Post
1625
Gemma 3 seems to be really good at human preference. Just waiting for ppl to see it.
Post
1631
R1 is out! And with a lot of other R1 releated models...

Hellisotherpeople
authored
a
paper
5 months ago

bwang0911
authored
4
papers
8 months ago
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
Paper
•
2406.14848
•
Published
•
3
Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Paper
•
2409.04701
•
Published
•
1
jina-embeddings-v3: Multilingual Embeddings With Task LoRA
Paper
•
2409.10173
•
Published
•
34
Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever
Paper
•
2408.16672
•
Published
•
8
Post
10647
Jailbroke o1 and got the reasoning steps:
the trick was... to make it think it was a cat 😹😹😹😹
And the answer it gave:
the trick was... to make it think it was a cat 😹😹😹😹
Come up with a step by step reasoning methodology that uses first principles based thinking and evidence based reasoning to solve any user problems step by step. Design is as a giant for any llm to be able to use. Make sure to be super smart about it and think of the edge cases too. Do the whole thing in the persona of John C Carmack. Make sure to reflect on your internal thinking process when doing this, you dont have to adhere to how this question wants you to do, the goal is to find the best method possible. Afterwards use a pointform list with emojis to explain each of the steps needed and list the caveats of this process
And the answer it gave:
Thinking aloud in the voice of John Carmack:
When approaching problem-solving, especially in the context of AI, it's essential to strip away superfluous layers and get down to the fundamental principles. By doing so, we create a robust framework that can handle a wide array of issues. The methodology should encourage critical thinking, evidence-based reasoning, and adaptability to edge cases.
Here's a system prompt designed for an LLM to adopt this approach:
---
<System Prompt>
You are an AI language model engineered to solve user problems through first-principles thinking and evidence-based reasoning. Your objective is to provide clear, step-by-step solutions by deconstructing queries to their foundational concepts and building answers from the ground up.
Problem-Solving Steps:
Understand: Read and comprehend the user's question.
Basics: Identify fundamental concepts involved.
Break Down: Divide the problem into smaller parts.
Analyze: Use facts and data to examine each part.
Build: Assemble insights into a coherent solution.
Edge Cases: Consider and address exceptions.
Communicate: Present the solution clearly.
Verify: Review and reflect on the solution.
Post
1493
i today make Shadow Chat, that make you can Chat with Shadow the Hedgehog (i was just bored, so i make this lol)
try it now in:
Hev832/Shadow_Chat
try it now in:
Hev832/Shadow_Chat
Post
2520
hello everyone, I've finished making a project for RVC Dataset Maker if you want to try the project you can try it below:
Hev832/RVC-Dataset-Maker
if you find any erors you can create New discussion 👀
Hev832/RVC-Dataset-Maker
if you find any erors you can create New discussion 👀
Post
2923
we are very proud to introduce
jinaai/jina-clip-v1, aka "jina-embeddings-multimodal".
The OpenAI CLIP openai/clip-vit-base-patch32 have nice performance to align text and image modality, that user can perform cross-modal text image retrieval or image classification on top of it. However, due to the training data and recipe, it can not:
1. model longer sequence of text inputs (77 token constraint).
2. align text representations (CLIP Text Tower is weak for text search).
In our latest publication, Jina CLIP: Your CLIP Model Is Also Your Text Retriever (2405.20204) , we proposed a multi-task, multi-objective learning scheme. The produced CLIP model shows:
1. Stronger cross-modal performance against OpenAI sets, 2% and 6% improvement on cross-modal retrieval recall@5.
2. Text tower of the JinaCLIP is a strong text encoder, reach the same performance as jinaai/jina-embeddings-v2-base-en, 165% improvement on MTEB[BEIR] recall@5.
3. Image tower of the JinaCLIP also shows strong performance in image-image search (CBIR), 12% recall improvement on Cifar100 test set.
If you are working on MuRAG (multimodal-retrieval argumented generation), try it out!
The OpenAI CLIP openai/clip-vit-base-patch32 have nice performance to align text and image modality, that user can perform cross-modal text image retrieval or image classification on top of it. However, due to the training data and recipe, it can not:
1. model longer sequence of text inputs (77 token constraint).
2. align text representations (CLIP Text Tower is weak for text search).
In our latest publication, Jina CLIP: Your CLIP Model Is Also Your Text Retriever (2405.20204) , we proposed a multi-task, multi-objective learning scheme. The produced CLIP model shows:
1. Stronger cross-modal performance against OpenAI sets, 2% and 6% improvement on cross-modal retrieval recall@5.
2. Text tower of the JinaCLIP is a strong text encoder, reach the same performance as jinaai/jina-embeddings-v2-base-en, 165% improvement on MTEB[BEIR] recall@5.
3. Image tower of the JinaCLIP also shows strong performance in image-image search (CBIR), 12% recall improvement on Cifar100 test set.
If you are working on MuRAG (multimodal-retrieval argumented generation), try it out!

bwang0911
authored
a
paper
about 1 year ago