IlyasMoutawwakil (Ilyas Moutawwakil)

reacted to tsungyi's post with 🔥 23 days ago

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3661

We’re excited to share that Cosmos Reason has surpassed 1 million downloads on Hugging Face!

Cosmos Reason is an open, customizable, commercial-ready 7B-parameter reasoning vision language model (VLM) designed for physical AI. By combining physics understanding, prior knowledge, and common sense reasoning, Cosmos Reason empowers AI agents and robots to operate intelligently in real-world environments.

Key applications already unlocked include:

✅ Automating large-scale dataset curation and annotation

🤖 Powering robot planning and vision-language action (VLA) decision-making

📊 Driving advanced video analytics and actionable insight generation

We’re proud to see a global community of developers using Cosmos Reason to teach robots to think like humans—and we’re just getting started.

⚡ Get started with Cosmos Reason 1 NIM, an easy-to-use microservice for AI model deployment: https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/cosmos-reason1-7b?version=1

📈 See the leaderboard: facebook/physical_reasoning_leaderboard

posted an update 3 months ago

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3425

🚀 Optimum: The Last v1 Release 🚀
Optimum v1.27 marks the final major release in the v1 series. As we close this chapter, we're laying the groundwork for a more modular and community-driven future:
- Optimum v2: A lightweight core package for porting Transformers, Diffusers, or Sentence-Transformers to specialized AI hardware/software/accelerators..
- Optimum‑ONNX: A dedicated package where the ONNX/ONNX Runtime ecosystem lives and evolves, faster-moving and decoupled from the Optimum core.

🎯 Why this matters:
- A clearer governance path for ONNX, fostering stronger community collaboration and improved developer experience..
- Enable innovation at a faster pace in a more modular, open-source environment.

💡 What this means:
- More transparency, broader participation, and faster development driven by the community and key actors in the ONNX ecosystem (PyTorch, Microsoft, Joshua Lochner 👀, ...)
- A cleaner, more maintainable core Optimum, focused on extending HF libraries to special AI hardware/software/accelerators tooling and used by our partners (Intel Corporation, Amazon Web Services (AWS), AMD, NVIDIA, FuriosaAI, ...)

🛠️ Major updates I worked on in this release:
✅ Added support for Transformers v4.53 and SmolLM3 in ONNX/ONNXRuntime.
✅ Solved batched inference/generation for all supported decoder model architectures (LLMs).

✨ Big shoutout to @echarlaix for leading the refactoring work that cleanly separated ONNX exporter logic and enabled the creation of Optimum‑ONNX.

📝 Release Notes: https://lnkd.in/gXtE_qji
📦 Optimum : https://lnkd.in/ecAezNT6
🎁 Optimum-ONNX: https://lnkd.in/gzjyAjSi
#Optimum #ONNX #OpenSource #HuggingFace #Transformers #Diffusers

reacted to clem's post with 🚀 over 1 year ago

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3842

Who said you couldn't build a big business based on open-source AI? Congrats Mistral team:

mistralai

1 reply

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reacted to merve's post with 🤗 over 1 year ago

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6129

Fine-tune Florence-2 on any task 🔥

Today we release a notebook and a walkthrough blog on fine-tuning Florence-2 on DocVQA dataset @andito @SkalskiP

Blog: https://huggingface.co/blog 📕
Notebook: https://colab.research.google.com/drive/1hKDrJ5AH_o7I95PtZ9__VlCTNAo1Gjpf?usp=sharing 📖
Florence-2 is a great vision-language model thanks to it's massive dataset and small size!

This model requires conditioning through task prefixes and it's not as generalist, requiring fine-tuning on a new task, such as DocVQA 📝

We have fine-tuned the model on A100 (and one can also use a smaller GPU with smaller batch size) and saw that model picks up new tasks 🥹

See below how it looks like before and after FT 🤩
Play with the demo here andito/Florence-2-DocVQA 🏄‍♀️

reacted to their post with 🧠🚀 over 1 year ago

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4159

Last week, Intel's new Xeon CPUs, Sapphire Rapids (SPR), landed on Inference Endpoints and I think they got the potential to reduce the cost of your RAG pipelines 💸

Why ? Because they come with Intel® AMX support, which is a set of instructions that support and accelerate BF16 and INT8 matrix multiplications on CPU ⚡

I went ahead and built a Space to showcase how to efficiently deploy embedding models on SPR for both Retrieving and Ranking documents, with Haystack compatible components: https://huggingface.co/spaces/optimum-intel/haystack-e2e

Here's how it works:

- Document Store: A FAISS document store containing the seven-wonders dataset, embedded, indexed and stored on the Space's persistent storage to avoid unnecessary re-computation of embeddings.

- Retriever: It embeds the query at runtime and retrieves from the dataset N documents that are most semantically similar to the query's embedding.
We use the small variant of the BGE family here because we want a model that's fast to run on the entire dataset and has a small embedding space for fast similarity search. Specifically we use an INT8 quantized bge-small-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.

- Ranker: It re-embeds the retrieved documents at runtime and re-ranks them based on semantic similarity to the query's embedding. We use the large variant of the BGE family here because it's optimized for accuracy allowing us to filter the most relevant k documents that we'll use in the LLM prompt. Specifically we use an INT8 quantized bge-large-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.

Space: https://huggingface.co/spaces/optimum-intel/haystack-e2e
Retriever IE: optimum-intel/fastrag-retriever
Ranker IE: optimum-intel/fastrag-ranker

posted an update over 1 year ago

Post

4159

Last week, Intel's new Xeon CPUs, Sapphire Rapids (SPR), landed on Inference Endpoints and I think they got the potential to reduce the cost of your RAG pipelines 💸

Why ? Because they come with Intel® AMX support, which is a set of instructions that support and accelerate BF16 and INT8 matrix multiplications on CPU ⚡

I went ahead and built a Space to showcase how to efficiently deploy embedding models on SPR for both Retrieving and Ranking documents, with Haystack compatible components: https://huggingface.co/spaces/optimum-intel/haystack-e2e

Here's how it works:

- Document Store: A FAISS document store containing the seven-wonders dataset, embedded, indexed and stored on the Space's persistent storage to avoid unnecessary re-computation of embeddings.

- Retriever: It embeds the query at runtime and retrieves from the dataset N documents that are most semantically similar to the query's embedding.
We use the small variant of the BGE family here because we want a model that's fast to run on the entire dataset and has a small embedding space for fast similarity search. Specifically we use an INT8 quantized bge-small-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.

- Ranker: It re-embeds the retrieved documents at runtime and re-ranks them based on semantic similarity to the query's embedding. We use the large variant of the BGE family here because it's optimized for accuracy allowing us to filter the most relevant k documents that we'll use in the LLM prompt. Specifically we use an INT8 quantized bge-large-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.

Space: https://huggingface.co/spaces/optimum-intel/haystack-e2e
Retriever IE: optimum-intel/fastrag-retriever
Ranker IE: optimum-intel/fastrag-ranker

reacted to Molbap's post with 🤯🤗🚀🔥 over 1 year ago

Post

5516

🚀🚀 Exciting times for the document AI community!

We're thrilled to announce the release of some of the largest OCR datasets available to the public.
🔥 With over 26 million pages , 18 billion text tokens, and 6TB of data, these resources are a significant leap forward for document AI research.

Here's how to access these datasets quickly:

from datasets import load_dataset

pdfa_dataset = load_dataset('pixparse/pdfa-eng-wds', streaming=True)
IDL_dataset = load_dataset('pixparse/idl-wds', streaming=True)

This enables you to stream them directly, integrating seamlessly with your projects using the Hugging Face datasets library. On the hub, you can find them here:

pixparse/pdfa-eng-wds
pixparse/idl-wds

For lean data loading, the new [chug](https://github.com/huggingface/chug) library offers a solution with pdf decoding:

import chug

task_cfg = chug.DataTaskDocReadCfg(
    page_sampling='all',
)
data_cfg = chug.DataCfg(
    source='pixparse/pdfa-eng-wds',
    split='train',
    batch_size=None,
    format='hfids',
    num_workers=0,
)
data_loader = chug.create_loader(
    data_cfg,
    task_cfg,
)
sample = next(iter(data_loader))

We owe a huge thank you to Peter Wyatt, Kate Tasker, Rachel Taketa, Ali Furkan Biten, Ruben Tito, and their colleagues for their contributions. Their work putting these datasets together has been invaluable. 🤗

Looking Ahead:

We're on a mission to enhance document AI capabilities, and these datasets are just the beginning. With your engagement and innovation, we're confident in the community's ability to develop robust OCR solutions. We encourage you to explore these datasets, experiment with the code, and contribute to the collective progress in document AI.

For detailed information on usage and licensing, please refer to the dataset cards on the Hugging Face hub.

4 replies

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replied to Molbap's post over 1 year ago

This is so cool and the kind of AI many industries are need for !

reacted to akhaliq's post with 👍 almost 2 years ago

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Here is my selection of papers for today (9 Jan)

https://huggingface.co/papers

AGG: Amortized Generative 3D Gaussians for Single Image to 3D

MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

DiarizationLM: Speaker Diarization Post-Processing with Large Language Models

TeleChat Technical Report

Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon

AST-T5: Structure-Aware Pretraining for Code Generation and Understanding

Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation

CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

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