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Sam Joshua

SamJoshua

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upvoted an article 3 days ago

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

reacted to merve's post with 🔥 8 days ago

Meta released Llama Guard 4 and new Prompt Guard 2 models 🔥 Llama Guard 4 is a new model to filter model inputs/outputs both text-only and image 🛡️ use it before and after LLMs/VLMs! https://huggingface.co/meta-llama/Llama-Guard-4-12B Prompt Guard 2 22M & 86M are smol models to prevent model jailbreaks and prompt injections ⚔ https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-22M https://huggingface.co/meta-llama/Llama-Guard-4-12B Both come with new release of transformers 🤗 Try the model right away 👉🏻https://github.com/huggingface/huggingface-llama-recipes/blob/main/llama_guard_4.ipynb Read our blog to learn more and easily get started 👉🏻 https://huggingface.co/blog/llama-guard-4 🦙

reacted to Kseniase's post with ❤️ 9 days ago

6 Free resources on Reinforcement Learning (RL) RL now is where the real action is, it's the engine behind autonomous tech, robots, and the next wave of AI that thinks, moves and solves problems on its own. To stay up to date with what’s happening in RL, we offer some fresh materials on it: 1. "Reinforcement Learning from Human Feedback" by Nathan Lambert -> https://rlhfbook.com/ It's a short introduction to RLHF, explaining instruction tuning, reward modeling, alignment methods, synthetic data, evaluation, and more 2. "A Course in Reinforcement Learning (2nd Edition)" by Dimitri P. Bertsekas -> https://www.mit.edu/~dimitrib/RLbook.html Explains dynamic programming (DP) and RL, diving into rollout algorithms, neural networks, policy learning, etc. It’s packed with solved exercises and real-world examples 3. "Mathematical Foundations of Reinforcement Learning" video course by Shiyu Zhao -> https://www.youtube.com/playlist?list=PLEhdbSEZZbDaFWPX4gehhwB9vJZJ1DNm8 Offers a mathematical yet friendly introduction to RL, covering Bellman Equation, value iteration, Monte Carlo learning, approximation, policy gradient, actor-critic methods, etc. + Check out the repo for more: https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning 4. "Multi-Agent Reinforcement Learning" by Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer -> https://www.marl-book.com/ Covers models, core ideas of multi-agent RL (MARL) and modern approaches to combining it with deep learning 5. "Reinforcement Learning: A Comprehensive Overview" by Kevin P. Murphy -> https://arxiv.org/pdf/2412.05265 Explains RL and sequential decision making, covering value-based, policy-gradient, model-based, multi-agent RL methods, RL+LLMs, and RL+inference and other topics 6. Our collection of free courses and books on RL -> https://huggingface.co/posts/Kseniase/884818121094439 If you liked this, also subscribe to The Turing Post: https://www.turingpost.com/subscribe

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SamJoshua's activity

upvoted an article 3 days ago

Article

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

9 days ago

• 22

reacted to merve's post with 🔥 8 days ago

Post

2542

Meta released Llama Guard 4 and new Prompt Guard 2 models 🔥

Llama Guard 4 is a new model to filter model inputs/outputs both text-only and image 🛡️ use it before and after LLMs/VLMs! meta-llama/Llama-Guard-4-12B

Prompt Guard 2 22M & 86M are smol models to prevent model jailbreaks and prompt injections ⚔ meta-llama/Llama-Prompt-Guard-2-22M meta-llama/Llama-Guard-4-12B
Both come with new release of transformers 🤗

Try the model right away 👉🏻https://github.com/huggingface/huggingface-llama-recipes/blob/main/llama_guard_4.ipynb

Read our blog to learn more and easily get started 👉🏻 https://huggingface.co/blog/llama-guard-4 🦙

1 reply

reacted to Kseniase's post with ❤️ 9 days ago

Post

6410

6 Free resources on Reinforcement Learning (RL)

RL now is where the real action is, it's the engine behind autonomous tech, robots, and the next wave of AI that thinks, moves and solves problems on its own. To stay up to date with what’s happening in RL, we offer some fresh materials on it:

1. "Reinforcement Learning from Human Feedback" by Nathan Lambert -> https://rlhfbook.com/
It's a short introduction to RLHF, explaining instruction tuning, reward modeling, alignment methods, synthetic data, evaluation, and more

2. "A Course in Reinforcement Learning (2nd Edition)" by Dimitri P. Bertsekas -> https://www.mit.edu/~dimitrib/RLbook.html
Explains dynamic programming (DP) and RL, diving into rollout algorithms, neural networks, policy learning, etc. It’s packed with solved exercises and real-world examples

3. "Mathematical Foundations of Reinforcement Learning" video course by Shiyu Zhao -> https://www.youtube.com/playlist?list=PLEhdbSEZZbDaFWPX4gehhwB9vJZJ1DNm8
Offers a mathematical yet friendly introduction to RL, covering Bellman Equation, value iteration, Monte Carlo learning, approximation, policy gradient, actor-critic methods, etc.
+ Check out the repo for more: https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning

4. "Multi-Agent Reinforcement Learning" by Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer -> https://www.marl-book.com/
Covers models, core ideas of multi-agent RL (MARL) and modern approaches to combining it with deep learning

5. "Reinforcement Learning: A Comprehensive Overview" by Kevin P. Murphy -> https://arxiv.org/pdf/2412.05265
Explains RL and sequential decision making, covering value-based, policy-gradient, model-based, multi-agent RL methods, RL+LLMs, and RL+inference and other topics

6. Our collection of free courses and books on RL -> https://huggingface.co/posts/Kseniase/884818121094439

If you liked this, also subscribe to The Turing Post: https://www.turingpost.com/subscribe

updated a model about 1 month ago

SamJoshua/Qwen2.5-3B-GRPO

Text Generation • Updated Apr 3 • 5

published a model about 1 month ago

SamJoshua/Qwen2.5-3B-GRPO

Text Generation • Updated Apr 3 • 5

reacted to garrethlee's post with 🔥 5 months ago

Post

1984

The latest o1 model from OpenAI is still unable to answer 9.11 > 9.9 correctly 🤔

A possible explanation? Tokenization - and our latest work investigates how it affects a model's ability to do math!

In this blog post, we discuss:
🔢 The different ways numbers are tokenized in modern LLMs
🧪 Our detailed approach in comparing these various methods
🥪 How we got a free boost in arithmetic performance by adding a few lines of code to the base Llama 3 tokenizer
👑 and a definitive, best tokenization method for math in LLMs!

Check out our work here: huggingface/number-tokenization-blog