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huybery  authored a paper 9 days ago
START: Self-taught Reasoner with Tools
christopher  new activity 11 days ago
blog-explorers/README:[Support] Community Articles
tomaarsen  authored a paper 23 days ago
MMTEB: Massive Multilingual Text Embedding Benchmark
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vansin 
posted an update about 13 hours ago
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606
🔥MedAgentBench Amazing Work🚀

Just explored #MedAgentBench from @Yale researchers and it's mind-blowing! They've created a cutting-edge benchmark that finally exposes the true capabilities of LLMs in complex medical reasoning.

⚡ Key discoveries:

DeepSeek R1 & OpenAI O3 dominate clinical reasoning tasks
Agent-based frameworks deliver exceptional performance-cost balance
Open-source alternatives are closing the gap at fraction of the cost

This work shatters previous benchmarks that failed to challenge today's advanced models.
The future of medical AI is here: https://github.com/gersteinlab/medagents-benchmark
#MedicalAI #MachineLearning #AIinHealthcare 🔥
christopher 
in blog-explorers/README 11 days ago

[Support] Community Articles

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#5 opened 12 months ago by
victor
suayptalha 
posted an update 21 days ago
lucifertrj 
posted an update about 1 month ago
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544
Bhagavad Gita GPT assistant - Build fast RAG pipeline to index 1000+ pages using Binary Optimization

DeepSeek R-1 and Qdrant Binary Quantization

Check out the latest tutorial where we build a Bhagavad Gita GPT assistant—covering:
- DeepSeek R1 vs OpenAI O1
- Using Qdrant client with Binary Quantization
- Building the RAG pipeline with LlamaIndex
- Running inference with DeepSeek R1 Distill model on Groq
- Develop Streamlit app for the chatbot inference

Watch the full implementation here: https://www.youtube.com/watch?v=NK1wp3YVY4Q
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Reality123b 
in blog-explorers/README about 1 month ago

[Support] Community Articles

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#5 opened 12 months ago by
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julien-c 
in blog-explorers/README about 1 month ago

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#5 opened 12 months ago by
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victor 
in blog-explorers/README about 2 months ago

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suayptalha 
posted an update about 2 months ago
duyguislakoglu 
authored a paper about 2 months ago
nataliaElv 
posted an update about 2 months ago
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1491
New chapter in the Hugging Face NLP course! 🤗 🚀

We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub. 

Any feedback for improvements welcome!

https://huggingface.co/learn/nlp-course/chapter10
mlabonne 
posted an update about 2 months ago
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6162
🆕 LLM Course 2025 edition!

I updated the LLM Scientist roadmap and added a ton of new information and references. It covers training, datasets, evaluation, quantization, and new trends like test-time compute scaling.

The LLM Course has been incredibly popular (41.3k stars!) and I've been touched to receive many, many messages about how it helped people in their careers.

I know how difficult this stuff can be, so I'm super proud of the impact it had. I want to keep updating it in 2025, especially with the LLM Engineer roadmap.

Thanks everyone, hope you'll enjoy it!

💻 LLM Course: https://huggingface.co/blog/mlabonne/llm-course
nataliaElv 
posted an update 2 months ago
lunarflu 
in blog-explorers/README 2 months ago

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MaziyarPanahi 
in blog-explorers/README 2 months ago

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akkasayaz 
in blog-explorers/README 2 months ago

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wolfram 
in blog-explorers/README 2 months ago

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suayptalha 
posted an update 3 months ago
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2150
🚀 Introducing 𝐅𝐢𝐫𝐬𝐭 𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐦𝐢𝐧𝐆𝐑𝐔 𝐌𝐨𝐝𝐞𝐥𝐬 from the paper 𝐖𝐞𝐫𝐞 𝐑𝐍𝐍𝐬 𝐀𝐥𝐥 𝐖𝐞 𝐍𝐞𝐞𝐝𝐞𝐝?

🖥 I have integrated 𝐧𝐞𝐱𝐭-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐑𝐍𝐍𝐬, specifically minGRU, which offer faster performance compared to Transformer architectures, into HuggingFace. This allows users to leverage the lighter and more efficient minGRU models with the "𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬" 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 for both usage and training.

💻 I integrated two main tasks: 𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 and 𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐂𝐚𝐮𝐬𝐚𝐥𝐋𝐌.

𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧:
You can use this class for 𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 tasks. I also trained a Sentiment Analysis model with stanfordnlp/imdb dataset.

𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐂𝐚𝐮𝐬𝐚𝐥𝐋𝐌:
You can use this class for 𝐂𝐚𝐮𝐬𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 tasks such as GPT, Llama. I also trained an example model with roneneldan/TinyStories dataset. You can fine-tune and use it!

🔗 𝐋𝐢𝐧𝐤𝐬:
Models: suayptalha/mingru-676fe8d90760d01b7955d7ab
GitHub: https://github.com/suayptalha/minGRU-hf
LinkedIn Post: https://www.linkedin.com/posts/suayp-talha-kocabay_mingru-a-suayptalha-collection-activity-7278755484172439552-wNY1

📰 𝐂𝐫𝐞𝐝𝐢𝐭𝐬:
Paper Link: https://arxiv.org/abs/2410.01201

I am thankful to Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio and Hossein Hajimirsadeghi for their papers.
suayptalha 
posted an update 3 months ago
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2501
🚀 Introducing Substitution Cipher Solvers!

As @suayptalha and @Synd209 we are thrilled to share an update!

🔑 This project contains a text-to-text model designed to decrypt English and Turkish text encoded using a substitution cipher. In a substitution cipher, each letter in the plaintext is replaced by a corresponding, unique letter to form the ciphertext. The model leverages statistical and linguistic properties of English to make educated guesses about the letter substitutions, aiming to recover the original plaintext message.

These models were fine-tuned on T5-base. The models are for monoalphabetic English and Turkish substitution ciphers, and they output decoded text and the alphabet with an accuracy that has never been achieved before!

Example:

Encoded text: Z hztwgx tstcsf qf z ulooqfe osfuqb tzx uezx awej z ozewsbe vlfwby fsmqisfx.

Decoded text: A family member or a support person may stay with a patient during recovery.

Model Collection Link: Cipher-AI/substitution-cipher-solvers-6731ebd22f0f0d8e0e2e2e00

Organization Link: https://huggingface.co/Cipher-AI
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suayptalha 
posted an update 3 months ago
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1640
🚀 FastLlama Series is Live!

🦾 Experience faster, lighter, and smarter language models! The new FastLlama makes Meta's LLaMA models work with smaller file sizes, lower system requirements, and higher performance. The model supports 8 languages, including English, German, and Spanish.

🤖 Built on the LLaMA 3.2-1B-Instruct model, fine-tuned with Hugging Face's SmolTalk and MetaMathQA-50k datasets, and powered by LoRA (Low-Rank Adaptation) for groundbreaking mathematical reasoning.

💻 Its compact size makes it versatile for a wide range of applications!
💬 Chat with the model:
🔗 Chat Link: suayptalha/Chat-with-FastLlama
🔗 Model Link: suayptalha/FastLlama-3.2-1B-Instruct
nataliaElv 
posted an update 3 months ago
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1666
If you are still wondering how the FineWeb2 annotations are done, how to follow the guidelines or how Argilla works, this is your video!

I go through a few samples of the FineWeb2 dataset and classify them based on their educational content. Check it out!

https://www.youtube.com/watch?v=_-ORB4WAVGU