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burtenshawΒ  updated a Space about 13 hours ago
agents-course-students/README
burtenshawΒ  published a Space about 13 hours ago
agents-course-students/README
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burtenshawΒ 
posted an update about 12 hours ago
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Hey πŸ‘‹

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
burtenshawΒ 
updated a Space about 13 hours ago
burtenshawΒ 
published a Space about 13 hours ago
burtenshawΒ 
posted an update about 14 hours ago
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πŸ“£ Teachers and Students! Here's a handy quiz app if you're preparing your own study material.

TLDR, It's a quiz that uses a dataset to make questions and save answers

Here's how it works:

- make a dataset of multiple choice questions
- duplicate the space add set the dataset repo
- log in and do the quiz
- submit the questions to create a new dataset

I made this to get ready for the agents course, but I hope it's useful for you projects too!

quiz app burtenshaw/dataset_quiz

dataset with questions burtenshaw/exam_questions

agents course we're working on https://huggingface.co/agents-course
burtenshawΒ 
posted an update 1 day ago
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AI was built on side projects!
burtenshawΒ 
posted an update 3 days ago
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🚧 Work in Progress! 🚧

πŸ‘·β€β™€οΈ We're working hard on getting the official agents course ready for the 50,000 students that have signed up.

If you want to contribute to the discussion, I started these community posts. Looking forward to hearing from you:

- smolagents unit in the agents course - agents-course/README#7
- LlamaIndex Unit in the agents course - agents-course/README#6
- LangChain and LangGraph unit in the agents course - agents-course/README#5
- Real world use cases in the agents course - agents-course/README#8


burtenshawΒ 
posted an update 8 days ago
burtenshawΒ 
posted an update 9 days ago
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We’re launching a FREE and CERTIFIED course on Agents!

We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.

Here's what you'll learn:

- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions.
- Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors.
- Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents.
- Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents.
Audience

This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.

Enroll today and start building the next generation of AI agent applications!

https://bit.ly/hf-learn-agents
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burtenshawΒ 
posted an update about 1 month ago
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People are flexing their end of year stats, so I made this app to show hub stats in a tidy design!

Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
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burtenshawΒ 
posted an update about 2 months ago
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Quick update from week 1 of smol course. The community is taking the driving seat and using the material for their own projects. If you want to do the same, join in!

- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish
- 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning
- 3 chapters are in progress on evaluation, vision language models, and synthetic data.
- around 780 people have forked the repo to use it for learning, teaching, sharing.

⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR

REPO: https://buff.ly/3ZCMKX2
discord channel: https://buff.ly/4f9F8jA
burtenshawΒ 
posted an update about 2 months ago
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For anyone looking to boost their LLM fine-tuning and alignment skills this decemeber. We're running this free and open course called smol course. It’s not big like Li Yin and @mlabonne , it’s just smol.

πŸ‘· It focuses on practical use cases, so if you’re working on something, bring it along.

πŸ‘―β€β™€οΈ It’s peer reviewed and open so you can discuss and get feedback.

🀘 If you’re already a smol pro, feel free to drop a star or issue.

> > Part 1 starts now, and it’s on instruction tuning!

https://github.com/huggingface/smol-course
burtenshawΒ 
posted an update about 2 months ago
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[SATURDAY ROUNDUP] β˜•οΈπŸ§‘β€πŸŽ“

In case you missed everything this week. It’s all about vision language models and image preference datasets. Here are the models and datasets you can use in your projects.

QWQ-32B-Preview is the first open weights model to reason like o1 with comparable performance. It’s large but is acing some of the hardest tasks.

https://bsky.app/profile/philschmid.bsky.social/post/3lbylz6nzqk25

SmolVLM is a vision implementation of the recently released SmolLM2. It uses the Idefics3 approach to add a vision encoder. The main difference being the smaller language model (8b > 1.7b) and more compression of images. This results in a model that is very accurate for its memory footprint.

https://huggingface.co/blog/smolvlm

ColSmolVLM is a vision embedding model based on SmolVLM using the Colbert approach from ColPali. This is shown to be great at document retrieval and everyone should test it out in their RAG setups.

https://huggingface.co/posts/merve/663466156074132

In an effort to build a FLUX level open source image generation model, the community is building a dataset of image preferences. The dataset is already open and the project is still running. Join in!

https://huggingface.co/posts/davidberenstein1957/405018978675827

TRL tutorial Drop - This week I dropped a load of tutorials on finetuning and aligning models with TRL. If you’re upskilling in this space, you should check these out.

https://bsky.app/profile/benburtenshaw.bsky.social/post/3lbrc56ap3222
burtenshawΒ 
posted an update 6 months ago
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SFT + Quantisation + Unsloth is a super easy way of squeezing extra performance out of an LLM at low latencies. Here are some hand y resources to bootstrap your projects.

Here's a filtered dataset from Helpsteer2 with the most correct and coherent samples: burtenshaw/helpsteer-2-plus
This is a SFT finetuned model: ttps://huggingface.co/burtenshaw/gemma-help-tiny-sft
This is the notebook I use to train the model: https://colab.research.google.com/drive/17oskw_5lil5C3jCW34rA-EXjXnGgRRZw?usp=sharing
Here's a load of Unsloth notebook on finetuning and inference: https://docs.unsloth.ai/get-started/unsloth-notebooks