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philipp-zettl

philipp-zettl

AI & ML interests

NLP/CV/Multimodal learning

Recent Activity

upvoted a paper 6 days ago

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

liked a model 6 days ago

answerdotai/ModernBERT-large

reacted to lewtun's post with 🚀 9 days ago

We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥 How? By combining step-wise reward models with tree search algorithms :) We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think" We're open sourcing the full recipe and sharing a detailed blog post. In our blog post we cover: 📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time. 🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets. 🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM Here's the links: - Blog post: https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute - Code: https://github.com/huggingface/search-and-learn Enjoy!

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philipp-zettl's activity

reacted to lewtun's post with 🚀 9 days ago

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6442

We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥

How? By combining step-wise reward models with tree search algorithms :)

We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"

We're open sourcing the full recipe and sharing a detailed blog post.

In our blog post we cover:

📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.

🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.

🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM

Here's the links:

- Blog post: HuggingFaceH4/blogpost-scaling-test-time-compute

- Code: https://github.com/huggingface/search-and-learn

Enjoy!

2 replies

reacted to lhoestq's post with ❤️ 11 days ago

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Made a HF Dataset editor a la gg sheets here: lhoestq/dataset-spreadsheets

With Dataset Spreadsheets:
✏️ Edit datasets in the UI
🔗 Share link with collaborators
🐍 Use locally in DuckDB or Python

Available for the 100,000+ parquet datasets on HF :)

reacted to merve's post with ❤️ 17 days ago

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This week in open-source AI was insane 🤠 A small recap🕺🏻 merve/dec-6-releases-67545caebe9fc4776faac0a3

Multimodal 🖼️
> Google shipped a PaliGemma 2, new iteration of PaliGemma with more sizes: 3B, 10B and 28B, with pre-trained and captioning variants 👏
> OpenGVLab released InternVL2, seven new vision LMs in different sizes, with sota checkpoint with MIT license ✨
> Qwen team at Alibaba released the base models of Qwen2VL models with 2B, 7B and 72B ckpts

LLMs 💬
> Meta released a new iteration of Llama 70B, Llama3.2-70B trained further
> EuroLLM-9B-Instruct is a new multilingual LLM for European languages with Apache 2.0 license 🔥
> Dataset: CohereForAI released GlobalMMLU, multilingual version of MMLU with 42 languages with Apache 2.0 license
> Dataset: QwQ-LongCoT-130K is a new dataset to train reasoning models
> Dataset: FineWeb2 just landed with multilinguality update! 🔥 nearly 8TB pretraining data in many languages!

Image/Video Generation 🖼️
> Tencent released HunyuanVideo, a new photorealistic video generation model
> OminiControl is a new editing/control framework for image generation models like Flux

Audio 🔊
> Indic-Parler-TTS is a new text2speech model made by community

reacted to christopher's post with 🔥 20 days ago

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The Lichess database of games, puzzles, and engine evaluations is now on the Hub: https://huggingface.co/Lichess

Billions of chess data points to download, query, and stream and we're excited to see what you'll build with it! ♟️ 🤗

- Lichess/positions-datasets-66f50837db5cd3287d60d489
- Lichess/games-datasets-66f508df78f4b43e1bb2d353

posted an update 21 days ago

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alias rm='rm -i'

Better be safe than sorry.

reacted to andito's post with 🔥 26 days ago

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SmolVLM speeding locally on a laptop thanks to mlx-vlm and
@Gradio ! Try it with two lines:
pip install git+https://github.com/andimarafioti/mlx-vlm.git@stream-generate-fix
python -m mlx_vlm.chat_ui --model mlx-community/SmolVLM-Instruct-8bit

Gotta love the MLX community! Big thanks to @pcuenq and @prince_canuma !

reacted to merve's post with ❤️ about 1 month ago

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your hugging face profile now has your recent activities 🤗

replied to their post 2 months ago

Yikes... good point!

replied to their post 2 months ago

I think you got me wrong there. I'm mostly concerned about image generation LoRAs that are trained on your person or for instance the pictures of children.
Gate keeping the secret sauce for base models is different and I totally agree with you on that part.

replied to their post 2 months ago

I'm more concerned about bad actors using them to create content that might harm you or put you in a bad spot by creating visual content with your face.
For instance to blackmail you or harm your reputation.

I am for sure a big supporter of open source and publish all the things I have the rights to. Yet, I wouldn't publish a LoRA that is trained on my face.

posted an update 2 months ago

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This is probably a very hot take, but here goes nothing.

With the incredibly accurate LoRAs we see emerge for high quality models like FLUX from services like fal.ai that offer training within single digit minutes, e.g. 2 min per 1000 iterations.

Why the hell are people publishing private LoRAs as public models?!
Take a look at this listing: https://huggingface.co/models?other=base_model:adapter:black-forest-labs%2FFLUX.1-dev&sort=created

I would expect that people that hold a HF account have some kind of forward thinking. Heck, do you really want to give anyone the power to create ultra realistic images of yourself?!

Didn't we learn anything from social media?
I am puzzled..

6 replies

reacted to clem's post with ❤️ 2 months ago

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Open-source AI creates healthy competition in a field where natural tendencies lead to extreme concentration of power. Imagine a world where only one or two companies could build software. This is the biggest risk and ethical challenge of them all IMO. Let's fight this!

3 replies

reacted to reach-vb's post with 🔥 2 months ago

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Multimodal Ichigo Llama 3.1 - Real Time Voice AI 🔥

> WhisperSpeech X Llama 3.1 8B
> Trained on 50K hours of speech (7 languages)
> Continually trained on 45hrs 10x A1000s
> MLS -> WhisperVQ tokens -> Llama 3.1
> Instruction tuned on 1.89M samples
> 70% speech, 20% transcription, 10% text
> Apache 2.0 licensed ⚡

Architecture:
> WhisperSpeech/ VQ for Semantic Tokens
> Llama 3.1 8B Instruct for Text backbone
> Early fusion (Chameleon)

I'm super bullish on HomeBrew/ Jan and early fusion, audio and text, multimodal models!

(P.S. Play with the demo on Hugging Face: jan-hq/Ichigo-llama3.1-s-instruct)

reacted to tomaarsen's post with 🔥 3 months ago

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📣 Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost.

1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.

Usage is as simple as SentenceTransformer("all-MiniLM-L6-v2", backend="onnx"). Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later 😉

🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:

1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with from_model2vec or with from_distillation where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed.
2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.

Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html

1 reply

posted an update 3 months ago

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🚀 Finishing up the prototype of my weekend project called ChessPT 🚀

- The game state is now being rendered. This simplifies coming up with own new moves
- The model space philipp-zettl/ChessPT was updated to provide an interactive mode.
- The space is currently running v0.4 of philipp-zettl/chessPT
- New updates will come this week.
- Training runs will be logged under https://wandb.ai/philipp-zettl/chessPT/

**Note**: The model is still not performing on a level that I want it to. It predicts too frequently invalid moves (according to the game state). In addition to that the post-processing step is a little faulty, so it might be possible that you end up in a state where the model didn't provide a next move.

posted an update 3 months ago

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Version 0.2a of ChessPT is currently training.

I decided to wait with the actual v1.0 until I have a better understanding where I want to go and successfully trained the first fine tune.

I'm playing around with a loss that is highly influenced by the idea of reinforcement.

Basically I'm punishing the model for generating invalid PGN strings.
The current approach sets on simplicity

-2: wrong characters in output
-1: invalid PGN string, but valid charset
0: valid PGN string, incl. valid moves

GPT-4o helped me with the implementation. I'm expecting some errors in the implementation.

The training should finish in somewhat 14h, I will upload the new weights then.
But I still need to run extensive tests on this loss before I can happily call it v0.2 ✌️

BTW, I'm also building a space for the model which will be published tonight after adding descriptions and a nice interface. ♟️

philipp-zettl/chessPT
philipp-zettl/ChessPT

posted an update 3 months ago

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This is my first post, so I need to start with a bang!

The people over at https://huggingface.co/Lichess published some amazing data sets over the past weeks, including a collection of >1M standard chess games ( Lichess/standard-chess-games).

Finally it's time to revive my chess buddy project from back in 2021 🎉

So without any further ado... I'm currently training my first character level LLM, and to be quite frank, I'm pretty astonished with the quality of my testing samples.

I'm using e4 g6, the Modern Defense (https://en.wikipedia.org/wiki/Modern_Defense) as a validation sample.
My model currently predicts mostly d4 Bg7 which are the strongest next moves for white and black.

Now in between I see some results that take lower ranked moves, which makes me very excited.

Once the pre-training is done for the base model, I want to run some fine tuning on more specific data sets, which are
Lichess/chess-openings
Lichess/chess-puzzles

Here are some intermediate examples

Step 6000: 
1. e4 g6 13. Rb1 d5 14. Bd3 Nxf3 15. Nxf3 Nxe3+ 16. Rxd3 Rxd3 17. Rxd6 Rhe8 18. Nd6 Rxd4 19. Rxd7+ Kxd7 20. Re7 Rxe7 21. Qxe7 1-0

Step 12000:
1. e4 g6 22. Be2 Re8 23. Kg2 1-0
1. d4 d5 2. c4 c6 3. Nf3 e6 4. dxe6 Qe7 5. Bb5+ Be8 6. Bxb7# 1-0
1. d4 d5 2. dxe5 Bd6 3. Nc3 h6 4. e4 Bf5 5. exf5 Nd7 6. exd5 Nxd5 7. Bxc4 Bxe2 8. f4 d4 9. Ng3 Bb4+ 10. Bxd4 Qxd4 11. Nfxe2 O-O-O 12. Ne6 Qf5 13. fxg4 Nxe5

Step 30000:
1. e4 g6 2. d4 Bg7 3. Nf3 d6 4. b3 e6 5. Bb2 f5 6. e5 c5 7. dxc5 dxc5 8. Nbd2 Nf6 9. Nce2 O-O 10. Qe2 c4 11. Na4 Bd6 12. f3 Ng4 13. fxg4 1-0
1. c4 c5 2. a3 Nc6 3. cxd5 Nxd5 4. Bf4 g6 5. Be2 Bg7 6. Nf3 Bg4 7. b4 Nf6 8. h3 Bxf3 9. Bxf3 a6 10. Nc3 O-O 11. Qc2 e

(each line starting with 1. is a set of moves)

You can find a first pre trained version here:
philipp-zettl/chessPT

reacted to Wauplin's post with 🤗🔥 3 months ago

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What a great milestone to celebrate! The huggingface_hub library is slowly becoming a cornerstone of the Python ML ecosystem when it comes to interacting with the @huggingface Hub. It wouldn't be there without the hundreds of community contributions and feedback! No matter if you are loading a model, sharing a dataset, running remote inference or starting jobs on our infra, you are for sure using it! And this is only the beginning so give a star if you wanna follow the project 👉 https://github.com/huggingface/huggingface_hub

1 reply

reacted to clem's post with ❤️ 4 months ago

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This isn’t a goal of ours because we have plenty of money in the bank but quite excited to see that @huggingfaceis profitable these days, with 220 team members and most of our platform being free (like model hosting) and open-source for the community!

Especially noteworthy at a time when most AI startups wouldn’t survive a year or two without VC money. Yay!

4 replies