7
Feel
🚀
Chat with a language model in multiple languages
FeeL (Feedback Loop)
non-profit
AI & ML interests
Human Feedback and LLMs
Recent Activity
View all activity
feel-fl's activity
burtenshaw
updated
a
Space
3 days ago
davidberenstein1957
posted
an
update
3 days ago
Post
2044
🔥 Text2SQL, explore and share any data analysis!
🤗 Hugging Face - Dataset Studio is an amazing new feature.
🚀 Start yourself: fka/awesome-chatgpt-prompts
📺 YouTube: https://youtu.be/5LUZq7MHolA?feature=shared
🤗 Hugging Face - Dataset Studio is an amazing new feature.
🚀 Start yourself: fka/awesome-chatgpt-prompts
📺 YouTube: https://youtu.be/5LUZq7MHolA?feature=shared
burtenshaw
posted
an
update
4 days ago
Post
3151
I’m super excited to work with
@mlabonne
to build the first practical example in the reasoning course.
🔗 https://huggingface.co./reasoning-course
Here's a quick walk through of the first drop of material that works toward the use case:
- a fundamental introduction to reinforcement learning. Answering questions like, ‘what is a reward?’ and ‘how do we create an environment for a language model?’
- Then it focuses on Deepseek R1 by walking through the paper and highlighting key aspects. This is an old school way to learn ML topics, but it always works.
- Next, it takes to you Transformers Reinforcement Learning and demonstrates potential reward functions you could use. This is cool because it uses Marimo notebooks to visualise the reward.
- Finally, Maxime walks us through a real training notebook that uses GRPO to reduce generation length. I’m really into this because it works and Maxime took the time to validate it share assets and logging from his own runs for you to compare with.
Maxime’s work and notebooks have been a major part of the open source community over the last few years. I, like everyone, have learnt so much from them.
🔗 https://huggingface.co./reasoning-course
Here's a quick walk through of the first drop of material that works toward the use case:
- a fundamental introduction to reinforcement learning. Answering questions like, ‘what is a reward?’ and ‘how do we create an environment for a language model?’
- Then it focuses on Deepseek R1 by walking through the paper and highlighting key aspects. This is an old school way to learn ML topics, but it always works.
- Next, it takes to you Transformers Reinforcement Learning and demonstrates potential reward functions you could use. This is cool because it uses Marimo notebooks to visualise the reward.
- Finally, Maxime walks us through a real training notebook that uses GRPO to reduce generation length. I’m really into this because it works and Maxime took the time to validate it share assets and logging from his own runs for you to compare with.
Maxime’s work and notebooks have been a major part of the open source community over the last few years. I, like everyone, have learnt so much from them.
davidberenstein1957
updated
a
dataset
5 days ago
davidberenstein1957
posted
an
update
5 days ago
Post
4079
🥊 Epic Agent Framework Showdown! Available today!
🔵 In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!
🛑 In the red corner, the defender, weighing in with lightweight efficiency: Hugging Face smolagents!
🔗 URL: https://huggingface.co./agents-course
We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at
- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows
The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.
Who won? You decide!
🔵 In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!
🛑 In the red corner, the defender, weighing in with lightweight efficiency: Hugging Face smolagents!
🔗 URL: https://huggingface.co./agents-course
We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at
- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows
The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.
Who won? You decide!
davidberenstein1957
posted
an
update
5 days ago
Post
2918
🫸 New release to push vector search to the Hub with vicinity and work with any serialisable objects.
🧑🏫 KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.
🔗 Example Repo: minishlab/my-vicinity-repo
🧑🏫 KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.
🔗 Example Repo: minishlab/my-vicinity-repo
burtenshaw
posted
an
update
10 days ago
Post
5278
I made a real time voice agent with FastRTC, smolagents, and hugging face inference providers. Check it out in this space:
🔗 burtenshaw/coworking_agent
🔗 burtenshaw/coworking_agent
-
- 9 replies
burtenshaw
posted
an
update
11 days ago
Post
6091
Now the Hugging Face agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain.
🔗 Follow the org for updates https://huggingface.co./agents-course
This week we are releasing the first framework unit in the course and it’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 .
🔗 Follow the org for updates https://huggingface.co./agents-course
This week we are releasing the first framework unit in the course and it’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 .
burtenshaw
posted
an
update
18 days ago
Post
7199
AGENTS + FINETUNING! This week Hugging Face learn has a whole pathway on finetuning for agentic applications. You can follow these two courses to get knowledge on levelling up your agent game beyond prompts:
1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co./learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co./learn/agents-course/bonus-unit1/introduction
Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.
1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co./learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co./learn/agents-course/bonus-unit1/introduction
Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.
-
- 2 replies
burtenshaw
posted
an
update
20 days ago
Post
3472
NEW COURSE! We’re cooking hard on Hugging Face courses, and it’s not just agents. The NLP course is getting the same treatment with a new chapter on Supervised Fine-Tuning!
👉 Follow to get more updates https://huggingface.co./nlp-course
The new SFT chapter will guide you through these topics:
1️⃣ Chat Templates: Master the art of structuring AI conversations for consistent and helpful responses.
2️⃣ Supervised Fine-Tuning (SFT): Learn the core techniques to adapt pre-trained models to your specific outputs.
3️⃣ Low Rank Adaptation (LoRA): Discover efficient fine-tuning methods that save memory and resources.
4️⃣ Evaluation: Measure your model's performance and ensure top-notch results.
This is the first update in a series, so follow along if you’re upskilling in AI.
👉 Follow to get more updates https://huggingface.co./nlp-course
The new SFT chapter will guide you through these topics:
1️⃣ Chat Templates: Master the art of structuring AI conversations for consistent and helpful responses.
2️⃣ Supervised Fine-Tuning (SFT): Learn the core techniques to adapt pre-trained models to your specific outputs.
3️⃣ Low Rank Adaptation (LoRA): Discover efficient fine-tuning methods that save memory and resources.
4️⃣ Evaluation: Measure your model's performance and ensure top-notch results.
This is the first update in a series, so follow along if you’re upskilling in AI.
- 2 replies
burtenshaw
posted
an
update
23 days ago
Post
3554
Hey, I’m Ben and I work at Hugging Face.
Right now, I’m focusing on educational stuff and getting loads of new people to build open AI models using free and open source tools.
I’ve made a collection of some of the tools I’m building and using for teaching. Stuff like quizzes, code challenges, and certificates.
burtenshaw/tools-for-learning-ai-6797453caae193052d3638e2
Right now, I’m focusing on educational stuff and getting loads of new people to build open AI models using free and open source tools.
I’ve made a collection of some of the tools I’m building and using for teaching. Stuff like quizzes, code challenges, and certificates.
burtenshaw/tools-for-learning-ai-6797453caae193052d3638e2
- 1 reply
davidberenstein1957
posted
an
update
25 days ago
Post
3279
🚀 Find banger tools for your smolagents!
I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools
Space: davidberenstein1957/smolagents-and-tools
I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools
Space: davidberenstein1957/smolagents-and-tools
- 1 reply
burtenshaw
posted
an
update
26 days ago
Post
9103
The Hugging Face agents course is finally out!
👉 https://huggingface.co./agents-course
This first unit of the course sets you up with all the fundamentals to become a pro in agents.
- What's an AI Agent?
- What are LLMs?
- Messages and Special Tokens
- Understanding AI Agents through the Thought-Action-Observation Cycle
- Thought, Internal Reasoning and the Re-Act Approach
- Actions, Enabling the Agent to Engage with Its Environment
- Observe, Integrating Feedback to Reflect and Adapt
👉 https://huggingface.co./agents-course
This first unit of the course sets you up with all the fundamentals to become a pro in agents.
- What's an AI Agent?
- What are LLMs?
- Messages and Special Tokens
- Understanding AI Agents through the Thought-Action-Observation Cycle
- Thought, Internal Reasoning and the Re-Act Approach
- Actions, Enabling the Agent to Engage with Its Environment
- Observe, Integrating Feedback to Reflect and Adapt
davidberenstein1957
posted
an
update
27 days ago
Post
2466
Fine-tune Deepseek-R1 with a Synthetic Reasoning Dataset
Blog: https://huggingface.co./blog/sdiazlor/fine-tune-deepseek-with-a-synthetic-reasoning-data
Blog: https://huggingface.co./blog/sdiazlor/fine-tune-deepseek-with-a-synthetic-reasoning-data
burtenshaw
posted
an
update
about 1 month ago
Post
3674
SmolLM2 paper is out! 😊
😍 Why do I love it? Because it facilitates teaching and learning!
Over the past few months I've engaged with (no joke) thousands of students based on SmolLM.
- People have inferred, fine-tuned, aligned, and evaluated this smol model.
- People used they're own machines and they've used free tools like colab, kaggle, and spaces.
- People tackled use cases in their job, for fun, in their own language, and with their friends.
upvote the paper SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model (2502.02737)
😍 Why do I love it? Because it facilitates teaching and learning!
Over the past few months I've engaged with (no joke) thousands of students based on SmolLM.
- People have inferred, fine-tuned, aligned, and evaluated this smol model.
- People used they're own machines and they've used free tools like colab, kaggle, and spaces.
- People tackled use cases in their job, for fun, in their own language, and with their friends.
upvote the paper SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model (2502.02737)
- 1 reply
burtenshaw
authored
a
paper
about 1 month ago
davidberenstein1957
posted
an
update
about 1 month ago
Post
2070
Agentic RAG: Applied, visual, and step-by-step! 🐾
Get familiar with the Agents and tools, not the bells and whistles!
Retrieve - Augment and now GENERATE.
part 3: https://huggingface.co./blog/davidberenstein1957/ai-blueprint-agentic-rag-part-3-generate
Get familiar with the Agents and tools, not the bells and whistles!
Retrieve - Augment and now GENERATE.
part 3: https://huggingface.co./blog/davidberenstein1957/ai-blueprint-agentic-rag-part-3-generate
davidberenstein1957
posted
an
update
about 1 month ago
Post
2873
Anyone can create free hosted tools for their AI agents! 🔥
Agentic RAG stack part 2 - augment
Augment retrieval results by reranking optimises content without increasing time too much
part2: https://huggingface.co./blog/davidberenstein1957/ai-blueprint-agentic-rag-part-2-augment
code: https://github.com/huggingface/ai-blueprint
Agentic RAG stack part 2 - augment
Augment retrieval results by reranking optimises content without increasing time too much
part2: https://huggingface.co./blog/davidberenstein1957/ai-blueprint-agentic-rag-part-2-augment
code: https://github.com/huggingface/ai-blueprint
davidberenstein1957
posted
an
update
about 1 month ago
Post
2004
Creating an agentic RAG stack on the Hugging Face Hub - part 1 - retrieval (1/5).
🚀 Web apps and microservices included!
Chunk, embed and index documents at a huge scale without overhead.
Blog: https://huggingface.co./blog/davidberenstein1957/ai-blueprint-agentic-rag-part-1-retrieve
🚀 Web apps and microservices included!
Chunk, embed and index documents at a huge scale without overhead.
Blog: https://huggingface.co./blog/davidberenstein1957/ai-blueprint-agentic-rag-part-1-retrieve
davidberenstein1957
posted
an
update
about 1 month ago
Post
1630
tldr; Parquet is awesome, DuckDB too!
Datasets on the Hugging Face Hub rely on parquet files. We can interact with these files using DuckDB as a fast in-memory database system. One of DuckDB’s features is vector similarity search which can be used with or without an index.
blog:
https://huggingface.co./learn/cookbook/vector_search_with_hub_as_backend
Datasets on the Hugging Face Hub rely on parquet files. We can interact with these files using DuckDB as a fast in-memory database system. One of DuckDB’s features is vector similarity search which can be used with or without an index.
blog:
https://huggingface.co./learn/cookbook/vector_search_with_hub_as_backend