optimum/bge-m3-neuron-bs16-sl1024
Updated
AI & ML interests
Accelerating DL
Recent Activity
View all activity
Jingya
updated
a
model
14 minutes ago
Jingya
published
a
model
44 minutes ago
Jingya
published
a
model
about 2 hours ago
Jingya
updated
a
model
about 7 hours ago
Jingya
published
a
model
about 21 hours ago
Jingya
updated
a
model
1 day ago
Jingya
published
a
model
1 day ago
Jingya
updated
a
model
6 days ago
IlyasMoutawwakil
posted
an
update
14 days ago
Post
2974
Transformers v5 just landed! 🚀
It significantly unifies and reduces modeling code across architectures, while opening the door to a whole new class of performance optimizations.
My favorite new feature? 🤔
The new dynamic weight loader + converter. Here’s why 👇
Over the last few months, the core Transformers maintainers built an incredibly fast weight loader, capable of converting tensors on the fly while loading them in parallel threads. This means we’re no longer constrained by how parameters are laid out inside the safetensors weight files.
In practice, this unlocks two big things:
- Much more modular modeling code. You can now clearly see how architectures build on top of each other (DeepSeek v2 → v3, Qwen v2 → v3 → MoE, etc.). This makes shared bottlenecks obvious and lets us optimize the right building blocks once, for all model families.
- Performance optimizations beyond what torch.compile can do alone. torch.compile operates on the computation graph, but it can’t change parameter layouts. With the new loader, we can restructure weights at load time: fusing MoE expert projections, merging attention QKV projections, and enabling more compute-dense kernels that simply weren’t possible before.
Personally, I'm honored to have contributed in this direction, including the work on optimizing MoE implementations and making modeling code more torch-exportable, so these optimizations can be ported cleanly across runtimes.
Overall, Transformers v5 is a strong signal of where the community and industry are converging: Modularity and Performance, without sacrificing Flexibility.
Transformers v5 makes its signature from_pretrained an entrypoint where you can mix and match:
- Parallelism
- Quantization
- Custom kernels
- Flash/Paged attention
- Continuous batching
- ...
Kudos to everyone involved! I highly recommend the:
Release notes: https://github.com/huggingface/transformers/releases/tag/v5.0.0
Blog post: https://huggingface.co/blog/transformers-v5
It significantly unifies and reduces modeling code across architectures, while opening the door to a whole new class of performance optimizations.
My favorite new feature? 🤔
The new dynamic weight loader + converter. Here’s why 👇
Over the last few months, the core Transformers maintainers built an incredibly fast weight loader, capable of converting tensors on the fly while loading them in parallel threads. This means we’re no longer constrained by how parameters are laid out inside the safetensors weight files.
In practice, this unlocks two big things:
- Much more modular modeling code. You can now clearly see how architectures build on top of each other (DeepSeek v2 → v3, Qwen v2 → v3 → MoE, etc.). This makes shared bottlenecks obvious and lets us optimize the right building blocks once, for all model families.
- Performance optimizations beyond what torch.compile can do alone. torch.compile operates on the computation graph, but it can’t change parameter layouts. With the new loader, we can restructure weights at load time: fusing MoE expert projections, merging attention QKV projections, and enabling more compute-dense kernels that simply weren’t possible before.
Personally, I'm honored to have contributed in this direction, including the work on optimizing MoE implementations and making modeling code more torch-exportable, so these optimizations can be ported cleanly across runtimes.
Overall, Transformers v5 is a strong signal of where the community and industry are converging: Modularity and Performance, without sacrificing Flexibility.
Transformers v5 makes its signature from_pretrained an entrypoint where you can mix and match:
- Parallelism
- Quantization
- Custom kernels
- Flash/Paged attention
- Continuous batching
- ...
Kudos to everyone involved! I highly recommend the:
Release notes: https://github.com/huggingface/transformers/releases/tag/v5.0.0
Blog post: https://huggingface.co/blog/transformers-v5
-
- 3 replies
IlyasMoutawwakil
posted
an
update
19 days ago
Post
2346
After 2 months of refinement, I'm happy to announce that a lot of Transformers' modeling code is now significantly more torch-compile & export-friendly 🔥
Why it had to be done 👇
PyTorch's Dynamo compiler is increasingly becoming the default interoperability layer for ML systems. Anything that relies on torch.export or torch.compile, from model optimization to cross-framework integrations, benefits directly when models can be captured as a single dynamo-traced graph !
Transformers models are now easier to:
⚙️ Compile end-to-end with torch.compile backends
📦 Export reliably via torch.export and torch.onnx.export
🚀 Deploy to ONNX / ONNX Runtime, Intel Corporation's OpenVINO, NVIDIA AutoDeploy (TRT-LLM), AMD's Quark, Meta's Executorch and more hardware-specific runtimes.
This work aims at unblocking entire TorchDynamo-based toolchains that rely on exporting Transformers across runtimes and accelerators.
We are doubling down on Transformers commitment to be a first-class citizen of the PyTorch ecosystem, more exportable, more optimizable, and easier to deploy everywhere.
There are definitely some edge-cases that we still haven't addressed so don't hesitate to try compiling / exporting your favorite transformers and to open issues / PRs.
PR in the comments ! More updates coming coming soon !
Why it had to be done 👇
PyTorch's Dynamo compiler is increasingly becoming the default interoperability layer for ML systems. Anything that relies on torch.export or torch.compile, from model optimization to cross-framework integrations, benefits directly when models can be captured as a single dynamo-traced graph !
Transformers models are now easier to:
⚙️ Compile end-to-end with torch.compile backends
📦 Export reliably via torch.export and torch.onnx.export
🚀 Deploy to ONNX / ONNX Runtime, Intel Corporation's OpenVINO, NVIDIA AutoDeploy (TRT-LLM), AMD's Quark, Meta's Executorch and more hardware-specific runtimes.
This work aims at unblocking entire TorchDynamo-based toolchains that rely on exporting Transformers across runtimes and accelerators.
We are doubling down on Transformers commitment to be a first-class citizen of the PyTorch ecosystem, more exportable, more optimizable, and easier to deploy everywhere.
There are definitely some edge-cases that we still haven't addressed so don't hesitate to try compiling / exporting your favorite transformers and to open issues / PRs.
PR in the comments ! More updates coming coming soon !
-
- 1 reply
Post
501
Building high-performance, reproducible kernels for AMD ROCm just got a lot easier.
I've put together a guide on building, testing, and sharing ROCm-compatible kernels using the Hugging Face
Learn how to:
- Use Nix for reproducible builds
- Integrate kernels as native PyTorch operators
- Share your kernels on the Hub for anyone to use with
We use the 🏆 award-winning RadeonFlow GEMM kernel as a practical example.
📜 Check out the full guide here : https://huggingface.co/blog/build-rocm-kernels
I've put together a guide on building, testing, and sharing ROCm-compatible kernels using the Hugging Face
kernel-builder and kernels libraries; so you can focus on optimizing performance rather than spending time on setup.Learn how to:
- Use Nix for reproducible builds
- Integrate kernels as native PyTorch operators
- Share your kernels on the Hub for anyone to use with
kernels.get_kernel()We use the 🏆 award-winning RadeonFlow GEMM kernel as a practical example.
📜 Check out the full guide here : https://huggingface.co/blog/build-rocm-kernels
badaoui
updated
a
Space
3 months ago
tengomucho
updated
a
Space
3 months ago
feat: add progress indicator while compiling is running
1
#2 opened 3 months ago
by
tengomucho
feat: add progress indicator while compiling is running
1
#2 opened 3 months ago
by
tengomucho
Post
2904
🚀 Big news for AI builders!
We’re thrilled to announce that the Qwen3-VL family of vision-language models is now available on Azure AI Foundry, thanks to our collaboration with Microsoft.
We bring open-source innovation to enterprise-grade AI infrastructure, making it easier than ever for enterprise to deploy and scale the latest and greatest from models from hugging Face securely within Azure.
🔍 Highlights:
- Deploy Qwen3-VL instantly via managed endpoints
- Built-in governance, telemetry, and lifecycle management
- True multimodal reasoning — vision, language, and code understanding
- State-of-the-art performance, outperforming closed-source models like Gemini 2.5 Pro and GPT-5
- Available in both *Instruct* and *Thinking* modes, across 24 model sizes
👉 Get started today: search for Qwen3-VL in the Hugging Face Collection on Azure AI Foundry.
We’re thrilled to announce that the Qwen3-VL family of vision-language models is now available on Azure AI Foundry, thanks to our collaboration with Microsoft.
We bring open-source innovation to enterprise-grade AI infrastructure, making it easier than ever for enterprise to deploy and scale the latest and greatest from models from hugging Face securely within Azure.
🔍 Highlights:
- Deploy Qwen3-VL instantly via managed endpoints
- Built-in governance, telemetry, and lifecycle management
- True multimodal reasoning — vision, language, and code understanding
- State-of-the-art performance, outperforming closed-source models like Gemini 2.5 Pro and GPT-5
- Available in both *Instruct* and *Thinking* modes, across 24 model sizes
👉 Get started today: search for Qwen3-VL in the Hugging Face Collection on Azure AI Foundry.
- 1 reply
Post
860
What’s your biggest headache deploying Hugging Face models to the cloud—and how can we fix it for you?
-
- 8 replies
Post
499
Post
3924
🤝 Collaborating with AMD to ensure Hugging Face Transformers runs smoothly on AMD GPUs!
We run daily CI on AMD MI325 to track the health of the most important model architectures and we’ve just made our internal dashboard public.
By making this easily accessible, we hope to spark community contributions and improve support for everyone!
We run daily CI on AMD MI325 to track the health of the most important model architectures and we’ve just made our internal dashboard public.
By making this easily accessible, we hope to spark community contributions and improve support for everyone!
-
- 2 replies
Post
435
🚀 Optimum libraries keep growing, and Optimum v2 is just around the corner!
I recently added ONNX export support for a bunch of new models in the optimum-onnx library, including: DeepSeek-V3, Cohere, Nemotron, Arcee, StableLM … and more!
⚡ With ONNX export, you can run your favorite models faster and more efficiently across different hardware backends, making deployment and experimentation much smoother.
💡 Have a model you’d love to see supported? Contributions are super welcome — let’s make Optimum even better together!
#ONNX #Optimum #HuggingFace #OpenSource #AI
I recently added ONNX export support for a bunch of new models in the optimum-onnx library, including: DeepSeek-V3, Cohere, Nemotron, Arcee, StableLM … and more!
⚡ With ONNX export, you can run your favorite models faster and more efficiently across different hardware backends, making deployment and experimentation much smoother.
💡 Have a model you’d love to see supported? Contributions are super welcome — let’s make Optimum even better together!
#ONNX #Optimum #HuggingFace #OpenSource #AI