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plaguss  authored a paper 15 days ago
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
gabrielmbmb  authored a paper 15 days ago
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
burtenshaw  authored a paper 15 days ago
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
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davanstrien 
posted an update 1 day ago
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1530
Hacked together a way to log trl GRPO training completions to a 🤗 dataset repo. This allows you to:

- Track rewards from multiple reward functions
- Treat the completion and rewards from training as a "proper" dataset and do EDA
- Share results for open science

The implementation is super hacky, but I'm curious if people would find this useful.

To push completions to the Hub, you just need two extra parameters:

log_completions=True
log_completions_hub_repo='your-username/repo-name'

Example dataset: davanstrien/test-logs
Colab: https://colab.research.google.com/drive/1wzBFPVthRYYTp-mEYlznLg_e_0Za1M3g
burtenshaw 
posted an update 2 days ago
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5700
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.
  • 2 replies
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burtenshaw 
posted an update 4 days ago
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3032
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.
  • 2 replies
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davanstrien 
posted an update 5 days ago
davanstrien 
posted an update 7 days ago
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1826
How do you make 1M+ Hugging Face models & datasets more discoverable?

davanstrien/Smol-Hub-tldr!

I fine-tuned HuggingFaceTB/SmolLM2-360M to generate one-line summaries from a model or dataset README.

Its own self-description?
"A model for generating concise summaries of model & dataset cards from the Hugging Face Hub"

The goal? Make it easier to find the right models and datasets for your specific needs. It's already powering a semantic search for datasets Space.

It's still a WIP but thanks to @loubnabnl , @anton-l , @eliebak et al, for cooking such a nice base model for fine-tuning small, efficient models for specific domains and tasks. 🙏
burtenshaw 
posted an update 7 days ago
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3223
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
  • 1 reply
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davanstrien 
posted an update 8 days ago
davidberenstein1957 
posted an update 9 days ago
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3068
🚀 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
  • 1 reply
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burtenshaw 
posted an update 10 days ago
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8836
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
davidberenstein1957 
posted an update 11 days ago
burtenshaw 
posted an update 14 days ago
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3497
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)
  • 1 reply
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plaguss 
authored a paper 15 days ago
gabrielmbmb 
authored a paper 15 days ago
burtenshaw 
authored a paper 15 days ago
davidberenstein1957 
posted an update 15 days ago
davidberenstein1957 
posted an update 16 days ago
davidberenstein1957 
posted an update 17 days ago
Tonic 
posted an update 18 days ago
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2146
🙋🏻‍♂️hey there folks ,

Goedel's Theorem Prover is now being demo'ed on huggingface : Tonic/Math

give it a try !
davidberenstein1957 
posted an update 22 days ago
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1613
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
not-lain 
posted an update 23 days ago