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plaguss  authored a paper 5 days ago
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
gabrielmbmb  authored a paper 5 days ago
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
burtenshaw  authored a paper 5 days ago
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
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burtenshaw 
posted an update about 11 hours ago
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1582
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
mmhamdy 
posted an update about 13 hours ago
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1211
⛓ Evaluating Long Context #2: SCROLLS and ZeroSCROLLS

In this series of posts about tracing the history of long context evaluation, we started with Long Range Arena (LRA). Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation. But it wasn't introduced to evaluate LLMs, but rather the transformer architecture in general.

📜 The SCROLLS benchmark, introduced in 2022, addresses this gap in NLP/LLM research. SCROLLS challenges models with tasks that require reasoning over extended sequences (according to 2022 standards). So, what does it offer?

1️⃣ Long Text Focus: SCROLLS (unlike LRA) focus mainly on text and contain inputs with thousands of words, testing models' ability to synthesize information across lengthy documents.
2️⃣ Diverse Tasks: Includes summarization, question answering, and natural language inference across domains like literature, science, and business.
3️⃣ Unified Format: All datasets are available in a text-to-text format, facilitating easy evaluation and comparison of models.

Building on SCROLLS, ZeroSCROLLS takes long text evaluation to the next level by focusing on zero-shot learning. Other features include:

1️⃣ New Tasks: Introduces tasks like sentiment aggregation and sorting book chapter summaries.
2️⃣ Leaderboard: A live leaderboard encourages continuous improvement and competition among researchers.

💡 What are some other landmark benchmarks in the history of long context evaluation? Feel free to share your thoughts and suggestions in the comments.

- SCROLLS Paper: SCROLLS: Standardized CompaRison Over Long Language Sequences (2201.03533)
- ZeroSCROLLS Paper: ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding (2305.14196)
davidberenstein1957 
posted an update 1 day ago
fdaudens 
posted an update 1 day ago
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1033
🔥 Video AI is taking over! Out of 17 papers dropped on Hugging Face today, 6 are video-focused - from Sliding Tile Attention to On-device Sora. The race for next-gen video tech is heating up! 🎬🚀
prithivMLmods 
posted an update 3 days ago
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3619
QwQ Edge Gets a Small Update..! 💬
try now: prithivMLmods/QwQ-Edge

🚀Now, you can use the following commands for different tasks:

🖼️ @image 'prompt...' → Generates an image
🔉@tts1 'prompt...' → Generates speech in a female voice
🔉 @tts2 'prompt...' → Generates speech in a male voice
🅰️@text 'prompt...' → Enables textual conversation (If not specified, text-to-text generation is the default mode)

💬Multimodality Support : prithivMLmods/Qwen2-VL-OCR-2B-Instruct
💬For text generation, the FastThink-0.5B model ensures quick and efficient responses, prithivMLmods/FastThink-0.5B-Tiny
💬Image Generation: sdxl lightning model, SG161222/RealVisXL_V4.0_Lightning

Github: https://github.com/PRITHIVSAKTHIUR/QwQ-Edge

graph TD
    A[User Interface] --> B[Chat Logic]
    B --> C{Command Type}
    C -->|Text| D[FastThink-0.5B]
    C -->|Image| E[Qwen2-VL-OCR-2B]
    C -->|@image| F[Stable Diffusion XL]
    C -->|@tts| G[Edge TTS]
    D --> H[Response]
    E --> H
    F --> H
    G --> H
burtenshaw 
posted an update 4 days ago
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3031
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)
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fdaudens 
posted an update 5 days ago
plaguss 
authored a paper 5 days ago
gabrielmbmb 
authored a paper 5 days ago
burtenshaw 
authored a paper 5 days ago
davidberenstein1957 
posted an update 5 days ago
davidberenstein1957 
posted an update 6 days ago
davidberenstein1957 
posted an update 7 days ago
fdaudens 
posted an update 9 days ago
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2353
📊 R1 just built its own download dashboard!

Some fresh stats: +6M downloads for 800+ derivative models vs 2M for originals. Watch the numbers grow here: fdaudens/deepseek-download-stats
prithivMLmods 
posted an update 9 days ago
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4732
o3-Mini and Deepseek R1
Worked out with some famous and weird examples.

🔥Blog: https://huggingface.co/blog/prithivMLmods/o3-mini-vs-deepseek-r1

Prompt : Using HTML, CSS, and JavaScript in a single HTML file to create a simulation of the solar system. Pay extreme attention to the UI to make it as intuitive as possible. Ensure that every planet appears as a sphere and is labeled with its corresponding name.

example 1: o3 Mini , example 2: Deepseek R1

Q2 : https://huggingface.co/blog/prithivMLmods/o3-mini-vs-deepseek-r1#q2--web-solar-system-explorer
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ZennyKenny 
posted an update 12 days ago
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421
GradientBoostingClassifier is an algorithm supported by the Python SciKit library, and now you can quickly train an ML model using this powerful technique on any (viable) dataset in the Hugging Face Hub without a line of code.

Love finishing a project right when the late night starts to turn into the early morning: sklearn-docs/GradientBoostingClassifier

Long time listener, first time caller, but always pleased to contribute, even if only adjacently, to the power of SciKit.
fdaudens 
posted an update 12 days ago
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3295
🎯 Kokoro TTS just hit v1.0! 🚀

Small but mighty: 82M parameters, runs locally, speaks multiple languages. The best part? It's Apache 2.0 licensed!
This could unlock so many possibilities ✨

Check it out: hexgrad/Kokoro-82M
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sayakpaul 
posted an update 12 days ago
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1845
We have been cooking a couple of fine-tuning runs on CogVideoX with finetrainers, smol datasets, and LoRA to generate cool video effects like crushing, dissolving, etc.

We are also releasing a LoRA extraction utility from a fully fine-tuned checkpoint. I know that kind of stuff has existed since eternity, but the quality on video models was nothing short of spectacular. Below are some links:

* Models and datasets: https://huggingface.co/finetrainers
* finetrainers: https://github.com/a-r-r-o-w/finetrainers
* LoRA extraction: https://github.com/huggingface/diffusers/blob/main/scripts/extract_lora_from_model.py
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davidberenstein1957 
posted an update 12 days ago
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1555
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 13 days ago