The Future of the Global Open-Source AI Ecosystem: From DeepSeek to AI+
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alvarobartt
updated
a
dataset
about 2 hours ago
dlouapre
updated
a
Space
about 7 hours ago
sayakpaul
updated
a
dataset
about 21 hours ago
lysandre
updated
a
dataset
1 day ago
coyotte508
updated
a
dataset
1 day ago
Deploy Space from Model - images
#168 opened over 2 years ago
by
michellehbn
burtenshaw
updated
a
dataset
1 day ago
sergiopaniego
posted
an
update
2 days ago
Post
1140
Tiny Aya 🌿 just dropped from
@CohereLabs
, a really powerful multilingual small model!
To celebrate, we cooked up fresh resources to train it for tool calling 🔧
> Free Google Colab guide: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_tool_calling.ipynb
> Standalone training script: https://github.com/huggingface/trl/blob/main/examples/scripts/sft_tiny_aya_tool_calling.py
To celebrate, we cooked up fresh resources to train it for tool calling 🔧
> Free Google Colab guide: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_tool_calling.ipynb
> Standalone training script: https://github.com/huggingface/trl/blob/main/examples/scripts/sft_tiny_aya_tool_calling.py
nielsr
submitted
a
paper
to Daily Papers
3 days ago
Post
3335
Hugging Face MCP Server v0.3.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Replace model_search and dataset_search with combined hub_repo_search tool.
- Less distracting description for hf_doc_search
- model_search and dataset_search tool calls will still function (plan to remove next release).
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Replace model_search and dataset_search with combined hub_repo_search tool.
- Less distracting description for hf_doc_search
- model_search and dataset_search tool calls will still function (plan to remove next release).
- 4 replies
sergiopaniego
posted
an
update
8 days ago
Post
410
The latest piece by
@MiniMax-AI
is a must-read.
It tries to break the impossible triangle of agent RL: throughput × stability × flexibility.
A lot to learn here, go read it 🫵
https://huggingface.co/blog/MiniMax-AI/forge-scalable-agent-rl-framework-and-algorithm
It tries to break the impossible triangle of agent RL: throughput × stability × flexibility.
A lot to learn here, go read it 🫵
https://huggingface.co/blog/MiniMax-AI/forge-scalable-agent-rl-framework-and-algorithm
albertvillanova
posted
an
update
9 days ago
Post
1576
5 years already working in democratizing AI 🤗
Grateful to be part of such an awesome team making it happen every day.
Grateful to be part of such an awesome team making it happen every day.
sergiopaniego
posted
an
update
12 days ago
Post
421
if you're looking for a good first issue to get your open-source journey started, you could contribute to this TRL issue by documenting one impactful paper in the docs
we have a broad list to cover!! 🧐
https://github.com/huggingface/trl/issues/4407
we have a broad list to cover!! 🧐
https://github.com/huggingface/trl/issues/4407
sergiopaniego
posted
an
update
22 days ago
Post
474
Meet the Post-Training Toolkit (PTT), which easily integrates with TRL via a single callback, by Aditya Challapally (
@microsoft
):
🔍 Detects training issues early
🛠 Lets you intervene safely
📊 Keeps long training runs stable, auditable & efficient
Microsoft blog: https://devblogs.microsoft.com/engineering-at-microsoft/diagnosing-instability-in-production-scale-agent-rl/
Integration guide: https://huggingface.co/docs/trl/main/en/ptt_integration
Code: https://github.com/microsoft/post-training-toolkit
🔍 Detects training issues early
🛠 Lets you intervene safely
📊 Keeps long training runs stable, auditable & efficient
Microsoft blog: https://devblogs.microsoft.com/engineering-at-microsoft/diagnosing-instability-in-production-scale-agent-rl/
Integration guide: https://huggingface.co/docs/trl/main/en/ptt_integration
Code: https://github.com/microsoft/post-training-toolkit
nielsr
submitted
a
paper
to Daily Papers
23 days ago
Post
242
Last week,
zai-org
dropped
zai-org/GLM-4.7-Flash. Now, we bring it to Microsoft Foundry!
- 🏆 30B-A3B MoE, the strongest model in the 30B class. It excels at coding tasks, agentic workflows and reasoning.
- 🤏🏻 Lighter version of his 358B big brother, balancing performance and efficiency.
Not light enough for you? We are also adding
unsloth
unsloth/GLM-4.7-Flash-GGUF to the catalog, with GPU and CPU support powered by llama.cpp 🔥
Go join the hype and deploy them from the Hugging Face collection on Microsoft Foundry!
- 🏆 30B-A3B MoE, the strongest model in the 30B class. It excels at coding tasks, agentic workflows and reasoning.
- 🤏🏻 Lighter version of his 358B big brother, balancing performance and efficiency.
Not light enough for you? We are also adding
Go join the hype and deploy them from the Hugging Face collection on Microsoft Foundry!
- 2 replies
alvarobartt
posted
an
update
23 days ago
Post
3007
💥
💡 Alternatively, you can also set the
hf-mem v0.4.1 now also estimates KV cache memory requirements for any context length and batch size with the --experimental flag!uvx hf-mem --model-id ... --experimental will automatically pull the required information from the Hugging Face Hub to include the KV cache estimation, when applicable.💡 Alternatively, you can also set the
--max-model-len, --batch-size and --kv-cache-dtype arguments (à la vLLM) manually if preferred.
-
- 1 reply
Post
268
Hugging Face MCP Server v0.3.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Streamable HTTP used for Gradio Connectivity
- SSE Transport (as Server) removed
- Proxy Configuration added for launch of sub-agent tools
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Streamable HTTP used for Gradio Connectivity
- SSE Transport (as Server) removed
- Proxy Configuration added for launch of sub-agent tools
sergiopaniego
posted
an
update
23 days ago
Post
2559
New TRL + OpenEnv example! 💥
Fine tune an LLM for playing Sudoku using an RL env via OpenEnv
Includes a script that runs on 1 or multiple GPUs with vLLM, plus a Colab-ready notebook.
Enjoy!
Notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/openenv_sudoku_grpo.ipynb
Script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/sudoku.py
Fine tune an LLM for playing Sudoku using an RL env via OpenEnv
Includes a script that runs on 1 or multiple GPUs with vLLM, plus a Colab-ready notebook.
Enjoy!
Notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/openenv_sudoku_grpo.ipynb
Script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/sudoku.py
- 1 reply
IlyasMoutawwakil
posted
an
update
25 days ago
Post
3019
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