Hugging Face's logo
Jorge Alonso's picture
1 2 9

Jorge Alonso PRO

oieieio
jbisal's profile picture revlon-carter's profile picture
Ā·
https://github.com/OIEIEIO

AI & ML interests

AI/ML

Recent Activity

reacted to burtenshaw's post with šŸ¤— 1 day ago
Now the Hugging Face agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain. šŸ”— Follow the org for updates https://huggingface.co/agents-course This week we are releasing the first framework unit in the course and itā€™s on smolagents. This is what the unit covers: - why should you use smolagents vs another library? - how to build agents that use code - build multiagents systems - use vision language models for browser use The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric.
replied to wassemgtk's post 1 day ago
# GESAL: Real-Time Adaptation for LLMs Weā€™re excited to unveil **Graph-Enhanced Singular Adaptive Learning (GESAL)**, a framework that lets LLMs like `meta-llama/Llama-3.2-1B` adapt in real time using user feedback. Check out the code and white paper on GitHub! šŸ”— **Code**: [https://github.com/writer/AI-Adaptive-Learning-GESAL](https://github.com/writer/AI-Adaptive-Learning-GESAL) --- ## Why GESAL? Static LLMs struggle to adapt without heavy retraining. GESAL solves this with: - **SVF**: Adapts weights via \( W' = U (\Sigma \cdot z) V^T \), using few parameters. - **Graph Memory**: Stores adaptations in nodes for scalability. - **RL**: Updates via \( J(z) = \mathbb{E}[\log \pi_z(y|x) r] \) based on feedback. --- ## How It Works Ask "How many Rā€™s in ā€˜strawberryā€™?" If it says "2" and you say "no," GESAL learns to say "3" next time, avoiding repeats. --- ## Try It Built with Hugging Faceā€™s `transformers`: ```bash pip install transformers torch numpy python Adaptive_Learning_(GESAL).py ``` Needs a Hugging Face token for Llama-3.2-1B. --- ## Results GESAL hits 95% accuracy after 5 feedbacks vs. LoRAā€™s 70%. Itā€™s efficient (~0.5M params) and scalable.
replied to wassemgtk's post 1 day ago
# GESAL: Real-Time Adaptation for LLMs Weā€™re excited to unveil **Graph-Enhanced Singular Adaptive Learning (GESAL)**, a framework that lets LLMs like `meta-llama/Llama-3.2-1B` adapt in real time using user feedback. Check out the code and white paper on GitHub! šŸ”— **Code**: [https://github.com/writer/AI-Adaptive-Learning-GESAL](https://github.com/writer/AI-Adaptive-Learning-GESAL) --- ## Why GESAL? Static LLMs struggle to adapt without heavy retraining. GESAL solves this with: - **SVF**: Adapts weights via \( W' = U (\Sigma \cdot z) V^T \), using few parameters. - **Graph Memory**: Stores adaptations in nodes for scalability. - **RL**: Updates via \( J(z) = \mathbb{E}[\log \pi_z(y|x) r] \) based on feedback. --- ## How It Works Ask "How many Rā€™s in ā€˜strawberryā€™?" If it says "2" and you say "no," GESAL learns to say "3" next time, avoiding repeats. --- ## Try It Built with Hugging Faceā€™s `transformers`: ```bash pip install transformers torch numpy python Adaptive_Learning_(GESAL).py ``` Needs a Hugging Face token for Llama-3.2-1B. --- ## Results GESAL hits 95% accuracy after 5 feedbacks vs. LoRAā€™s 70%. Itā€™s efficient (~0.5M params) and scalable.
View all activity

Organizations

Hugging Face Discord Community's profile picture OIEIEIO's profile picture Lambda Go Labs's profile picture