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davidberenstein1957
updated
a
dataset
about 11 hours ago
burtenshaw
posted
an
update
1 day ago
Post
2532
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
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- 5 replies
burtenshaw
posted
an
update
2 days ago
Post
5462
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
updated
a
Space
3 days ago
burtenshaw
posted
an
update
9 days ago
Post
6881
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.
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- 2 replies
davidberenstein1957
updated
a
Space
10 days ago
burtenshaw
posted
an
update
11 days ago
Post
3248
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
14 days ago
Post
3376
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
16 days ago
Post
3231
🚀 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
18 days ago
Post
8946
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
18 days ago
Post
2445
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
21 days ago
Post
3559
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
22 days ago
davidberenstein1957
posted
an
update
22 days ago
Post
2059
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
23 days ago
Post
2862
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
24 days ago
Post
1999
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
29 days ago
Post
1626
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
davidberenstein1957
posted
an
update
about 1 month ago
Post
1798
Let's uncover the post-training dataset from DeepSeek-R1 with Magpie!
Pass pre-query tokens
We can get realistic examples!
Gist: https://gist.github.com/davidberenstein1957/3f20046ce57395a6aba13f8b4e956b59
Pass pre-query tokens
<｜begin▁of▁sentence｜>User: , let the model generate the rest.We can get realistic examples!
Gist: https://gist.github.com/davidberenstein1957/3f20046ce57395a6aba13f8b4e956b59
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- 6 replies
burtenshaw
posted
an
update
about 1 month ago
Post
3238
Manic few days in open source AI, with game changing development all over the place. Here's a round up of the resources:
- The science team at @huggingface reproduced and open source the seek r1. https://github.com/huggingface/open-r1
- @qwen released a series of models with 1 million token context! https://qwenlm.github.io/blog/qwen2.5-1m/
- SmolVLM got even smaller with completely new variants at 256m and 500m https://huggingface.co./blog/smolervlm
There's so much you could do with these developments. Especially combining them together into agentic applications or fine-tuning them on your use case.
- The science team at @huggingface reproduced and open source the seek r1. https://github.com/huggingface/open-r1
- @qwen released a series of models with 1 million token context! https://qwenlm.github.io/blog/qwen2.5-1m/
- SmolVLM got even smaller with completely new variants at 256m and 500m https://huggingface.co./blog/smolervlm
There's so much you could do with these developments. Especially combining them together into agentic applications or fine-tuning them on your use case.
- 1 reply
burtenshaw
posted
an
update
about 1 month ago
Post
1350
Hey 👋
I'm helping out on some community research to learn about the AI community. If you want to join in the conversation, head over here where I started a community discussion on the most influential model since BERT.
OSAIResearchCommunity/README#2
I'm helping out on some community research to learn about the AI community. If you want to join in the conversation, head over here where I started a community discussion on the most influential model since BERT.
OSAIResearchCommunity/README#2