open-source-metrics (Hugging Face OSS Metrics)

pagezyhf

posted an update about 18 hours ago

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🎉 New in Azure Model Catalog: NVIDIA Parakeet TDT 0.6B V2

We're excited to welcome Parakeet TDT 0.6B V2—a state-of-the-art English speech-to-text model—to the Azure Foundry Model Catalog.

What is it?

A powerful ASR model built on the FastConformer-TDT architecture, offering:
🕒 Word-level timestamps
✍️ Automatic punctuation & capitalization
🔊 Strong performance across noisy and real-world audio

It runs with NeMo, NVIDIA’s optimized inference engine.

Want to give it a try? 🎧 You can test it with your own audio (up to 3 hours) on Hugging Face Spaces before deploying.If it fits your need, deploy easily from the Hugging Face Hub or Azure ML Studio with secure, scalable infrastructure!

📘 Learn more by following this guide written by @alvarobartt

https://huggingface.co/docs/microsoft-azure/azure-ai/examples/deploy-nvidia-parakeet-asr

fdaudens

posted an update about 19 hours ago

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AI is reshaping everything—how we work, how we feel, even how nations compete.

Today’s reads cut across power, emotion, and disruption.

Here’s what stood out and why it matters 👇

AI might “solve” loneliness, but this could be a problem, as the discomfort of loneliness shapes us in important ways. 💔 https://t.co/k2Q9le6G0P

A new study warns of significant risks in using AI therapy chatbots, highlighting issues like stigmatization and inappropriate responses. 🤖 https://t.co/EFyW0RbYVl

AI is already showing signs of slashing job openings in the UK, particularly in roles exposed to the technology, suggesting a labor market slowdown. 📉 https://t.co/hhs0BbqIMa

AI firms like OpenAI are poaching Wall Street quants with massive paydays, shifting the talent landscape for building artificial general intelligence. 💰 https://www.businessinsider.com/ai-talent-openai-wall-street-quant-trading-firms-2025-7

Speaking of which: Nvidia CEO Jensen Huang disagrees with Anthropic CEO Dario Amodei on whether AI will create more jobs—or trigger a “white-collar apocalypse.” Huang believes AI will create vastly more, and better, jobs. ⚔️ https://t.co/YHWhY7qvSq

Can Nvidia convince governments to pay for “sovereign AI”? Politicians are warming to the idea of national AI systems, but it might not reduce dependence on US tech. 🌍 https://t.co/htQDzJAIDu

albertvillanova

posted an update 4 days ago

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266

🚀 New in smolagents v1.20.0: Remote Python Execution via WebAssembly (Wasm)

We've just merged a major new capability into the smolagents framework: the CodeAgent can now execute Python code remotely in a secure, sandboxed WebAssembly environment!

🔧 Powered by Pyodide and Deno, this new WasmExecutor lets your agent-generated Python code run safely: without relying on Docker or local execution.

Why this matters:
✅ Isolated execution = no host access
✅ No need for Python on the user's machine
✅ Safer evaluation of arbitrary code
✅ Compatible with serverless / edge agent workloads
✅ Ideal for constrained or untrusted environments

This is just the beginning: a focused initial implementation with known limitations. A solid MVP designed for secure, sandboxed use cases. 💡

💡 We're inviting the open-source community to help evolve this executor:
• Tackle more advanced Python features
• Expand compatibility
• Add test coverage
• Shape the next-gen secure agent runtime

🔗 Check out the PR: https://github.com/huggingface/smolagents/pull/1261

Let's reimagine what agent-driven Python execution can look like: remote-first, wasm-secure, and community-built.

This feature is live in smolagents v1.20.0!
Try it out.
Break things. Extend it. Give us feedback.
Let's build safer, smarter agents; together 🧠⚙️

👉 https://github.com/huggingface/smolagents/releases/tag/v1.20.0

#smolagents #WebAssembly #Python #AIagents #Pyodide #Deno #OpenSource #HuggingFace #AgenticAI

pagezyhf

posted an update 6 days ago

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If you want to dive into how the HF team worked with @seungrokj at @AMD
to optimize kernels on MI300, you should give a read to our latest blog!

Such a great educational material for anyone curious about the world of optimizing low level ML.

https://huggingface.co/blog/mi300kernels

a-r-r-o-w

posted an update 6 days ago

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Caching is an essential technique used in diffusion inference serving for speeding up image/video generations. Diffusers just added support for another caching method: First Block Cache - a technique developed by @chengzeyi building upon the ideas of TeaCache.

The idea in short is: if the model predictions do not vary much over successive inference steps, we can skip certain steps where the prediction difference is small. To figure out whether an inference step will make a significant improvement to the overall velocity/noise prediction, we calculate the relative difference of the output of the first transformer block at timestep $t$ with $t-1$, and compare it against a selected threshold. If the difference is lower than the threshold, we skip the step. A higher threshold will lead to more steps being skipped. However, skipping many steps is bad because it can throw off the model predictions, and so we need to test and select the threshold based on level of quality-speed tradeoff for every model we use it with.

Diffusers usage with CogView4:

import torch
from diffusers import CogView4Pipeline
from diffusers.hooks import apply_first_block_cache, FirstBlockCacheConfig

pipe = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16)
pipe.to("cuda")

apply_first_block_cache(pipe.transformer, FirstBlockCacheConfig(threshold=0.2))

prompt = "A photo of an astronaut riding a horse on mars"
image = pipe(prompt, generator=torch.Generator().manual_seed(42)).images[0]
image.save("output.png")

Below, you'll find the benchmarks and visualizations of the predicted output at different blocks of the Flux DiT.

Docs: https://huggingface.co/docs/diffusers/main/en/optimization/cache
PR: https://github.com/huggingface/diffusers/pull/11180

References:
- First Block Cache: https://github.com/chengzeyi/ParaAttention
- TeaCache: https://github.com/ali-vilab/TeaCache

andito

posted an update 12 days ago

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🧠👁️ Can AI visualize solutions?

Humans often solve visual problems by sketching ideas in our minds. What if Vision-Language Models (VLMs) could do something similar, not by generating full images, but by using internal “mental sketches”?

That’s the idea behind Mirage, a new framework that empowers VLMs to reason using latent visual tokens. Instead of just thinking in words, Mirage mixes in abstract visual representations that help the model solve complex tasks.

These aren't photorealistic images. They're compact, internal representations optimized purely to support reasoning.

🔧 Mirage is trained in two phases:

1) Grounding: It learns to produce latent tokens anchored in real images.
2) Refinement: The model drops the images and learns to generate visual tokens on its own.

📈 And yes, it works!
On challenging benchmarks like Visual Spatial Planning, Jigsaw puzzles, and Spatial Attention Tasks, Mirage clearly outperforms GPT-4o and other strong baselines.
Smart sketches > empty words.

By mimicking the way humans visualize solutions, Mirage gives AI a new kind of imagination, one that’s faster, more efficient, and more human-like.
Kudos to the teams at UMass Amherst and MIT behind this exciting work.
Check the paper: Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens (2506.17218)

4 replies

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arthurbresnu

posted an update 14 days ago

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2002

‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module & much more. Sparse + Dense = 🔥 hybrid search performance!

1️⃣ Sparse Encoder Models - New support for sparse embeddings (30k+ dims, <1% non-zero)

* Full SPLADE, Inference-free SPLADE, CSR support
* 4 new modules, 12 losses, 9 evaluators
* Integration with elastic, opensearch-project, Qdrant, ibm-granite
* Decode interpretable embeddings
* Hybrid search integration

2️⃣ Enhanced Encode Methods

* encode_query & encode_document with auto prompts
* Direct device list passing to encode()
* Cleaner multi-processing

3️⃣ Router Module & Training

* Different paths for queries vs documents
* Custom learning rates per parameter group
* Composite loss logging
* Perfect for two-tower architectures

4️⃣ Documentation & Training

* New Training/Loss Overview docs
* 6 training example pages
* Search engine integration examples

Read the comprehensive blogpost about training sparse embedding models: https://huggingface.co/blog/train-sparse-encoder

See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v5.0.0

What's next? We would love to hear from the community! What sparse encoder models would you like to see? And what new capabilities should Sentence Transformers handle - multimodal embeddings, late interaction models, or something else? Your feedback shapes our roadmap!

I'm incredibly excited to see the community explore sparse embeddings and hybrid search! The interpretability alone makes this a game-changer for understanding what your models are actually doing.

🙏 Thanks to @tomaarsen for this incredible opportunity!

tomaarsen

posted an update 14 days ago

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2461

‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module for asymmetric models & much more. Sparse + Dense = 🔥 hybrid search performance! Details:

1️⃣ Sparse Encoder Models
Brand new support for sparse embedding models that generate high-dimensional embeddings (30,000+ dims) where <1% are non-zero:

- Full SPLADE, Inference-free SPLADE, and CSR architecture support
- 4 new modules, 12 new losses, 9 new evaluators
- Integration with @elastic-co , @opensearch-project , @NAVER LABS Europe, @qdrant , @IBM , etc.
- Decode interpretable embeddings to understand token importance
- Hybrid search integration to get the best of both worlds

2️⃣ Enhanced Encode Methods & Multi-Processing
- Introduce encode_query & encode_document automatically use predefined prompts
- No more manual pool management - just pass device list directly to encode()
- Much cleaner and easier to use than the old multi-process approach

3️⃣ Router Module & Advanced Training
- Router module with different processing paths for queries vs documents
- Custom learning rates for different parameter groups
- Composite loss logging - see individual loss components
- Perfect for two-tower architectures

4️⃣ Comprehensive Documentation & Training
- New Training Overview, Loss Overview, API Reference docs
- 6 new training example documentation pages
- Full integration examples with major search engines
- Extensive blogpost on training sparse models

Read the comprehensive blogpost about training sparse embedding models: https://huggingface.co/blog/train-sparse-encoder

See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v5.0.0

What's next? We would love to hear from the community! What sparse encoder models would you like to see? And what new capabilities should Sentence Transformers handle - multimodal embeddings, late interaction models, or something else? Your feedback shapes our roadmap!

pagezyhf

posted an update 15 days ago

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In case you missed it, Hugging Face expanded its collaboration with Azure a few weeks ago with a curated catalog of 10,000 models, accessible from Azure AI Foundry and Azure ML!

@alvarobartt cooked during these last days to prepare the one and only documentation you need, if you wanted to deploy Hugging Face models on Azure. It comes with an FAQ, great guides and examples on how to deploy VLMs, LLMs, smolagents and more to come very soon.

We need your feedback: come help us and let us know what else you want to see, which model we should add to the collection, which model task we should prioritize adding, what else we should build a tutorial for. You’re just an issue away on our GitHub repo!

https://huggingface.co/docs/microsoft-azure/index

fdaudens

posted an update 18 days ago

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Three big AI copyright updates this week alone. Tracking it all is getting almost impossible!

That’s why @BrigitteTousi and I built this interactive tracker to keep you up to date fdaudens/ai-copyright-lawsuits

(Prototyped in minutes with DeepSite!)

a-r-r-o-w

posted an update 18 days ago

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As you might have already heard, FLUX.1-Kontext-dev is now released and taken the generative community by storm!

In case you haven't come across it, you can get started with Kontext using 🤗 diffusers. See the official [model]( black-forest-labs/FLUX.1-Kontext-dev) and [docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux#flux).

Want to know how inference companies like Fal & Replicate are able to run the model so fast and in under 2 seconds per image? Check out this [gist](https://gist.github.com/a-r-r-o-w/d08c37e8bd3e9c26b4ce80360be148c6) for some details!

1 reply

·

lvwerra

authored a paper 18 days ago

FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language

Paper • 2506.20920 • Published 19 days ago • 61

fdaudens

posted an update 19 days ago

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This is what efficient AI looks like: Gemma 3n just dropped - a natively multimodal model that runs entirely on your device. No cloud. No API calls.

🧠 Text, image, audio, and video - handled locally.
⚡️Only needs 2B in GPU memory to run
🤯 First sub-10B model to hit 1300+ Elo
✅ Plug-and-play with Hugging Face, MLX, llama.cpp, and more.

Plus: Multilingual out of the box (140+ languages), fine-tune in a free Colab notebook.

google/gemma-3n-685065323f5984ef315c93f4

1 reply

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freddyaboulton

posted an update 20 days ago

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The new multimodalart/self-forcing model and demo are truly impressive!

jeffboudier

posted an update 21 days ago

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AMD summer hackathons are here!
A chance to get hands-on with MI300X GPUs and accelerate models.
🇫🇷 Paris - Station F - July 5-6
🇮🇳 Mumbai - July 12-13
🇮🇳 Bengaluru - July 19-20

Hugging Face and GPU Mode will be on site and on July 6 in Paris @ror will share lessons learned while building new kernels to accelerate Llama 3.1 405B on ROCm

Register to Paris event: https://lu.ma/fmvdjmur?tk=KeAbiP
All dates: https://lu.ma/calendar/cal-3sxhD5FdxWsMDIz

pagezyhf

posted an update 21 days ago

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Hackathons in Paris on July 5th and 6th!

Hugging Face just wrapped 4 months of deep work with AMD to push kernel-level optimization on their MI300X GPUs. Now, it's time to share everything we learned.

Join us in Paris at STATION F for a hands-on weekend of workshops and a hackathon focused on making open-source LLMs faster and more efficient on AMD.

Prizes, amazing host speakers, ... if you want more details, navigate to https://lu.ma/fmvdjmur!

2 replies

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albertvillanova

posted an update 21 days ago

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🚀 SmolAgents v1.19.0 is live!
This release brings major improvements to agent flexibility, UI usability, streaming architecture, and developer experience: making it easier than ever to build smart, interactive AI agents. Here's what's new:

🔧 Agent Upgrades
- Support for managed agents in ToolCallingAgent
- Context manager support for cleaner agent lifecycle handling
- Output formatting now uses XML tags for consistency

🖥️ UI Enhancements
- GradioUI now supports reset_agent_memory: perfect for fresh starts in dev & demos.

🔄 Streaming Refactor
- Streaming event aggregation moved off the Model class
- ➡️ Better architecture & maintainability

📦 Output Tracking
- CodeAgent outputs are now stored in ActionStep
- ✅ More visibility and structure to agent decisions

🐛 Bug Fixes
- Smarter planning logic
- Cleaner Docker logs
- Better prompt formatting for additional_args
- Safer internal functions and final answer matching

📚 Docs Improvements
- Added quickstart examples with tool usage
- One-click Colab launch buttons
- Expanded reference docs (AgentMemory, GradioUI docstrings)
- Fixed broken links and migrated to .md format

🔗 Full release notes:
https://github.com/huggingface/smolagents/releases/tag/v1.19.0

💬 Try it out, explore the new features, and let us know what you build!

#smolagents #opensource #AIagents #LLM #HuggingFace

fdaudens

posted an update 21 days ago

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ASMR Shiba has something to say 🐾

cfahlgren1

posted an update 21 days ago

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I ran the Anthropic Misalignment Framework for a few top models and added it to a dataset: cfahlgren1/anthropic-agentic-misalignment-results

You can read the reasoning traces of the models trying to blackmail the user and perform other actions. It's very interesting!!