On the Shifting Global Compute Landscape
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alvarobartt
updated
a
dataset
about 5 hours ago
sergiopaniego
posted
an
update
2 days ago
sergiopaniego
posted
an
update
5 days ago
Post
1950
🎄 last talk of the year about open AI and HF today at Universidad Rey Juan Carlos for undergrad students
always a pleasure to be back at my alma mater
🎅 slides: https://github.com/sergiopaniego/talks
always a pleasure to be back at my alma mater
🎅 slides: https://github.com/sergiopaniego/talks
- 1 reply
Post
2489
🐦🔥 I've just published Sentence Transformers v5.2.0! It introduces multi-processing for CrossEncoder (rerankers), multilingual NanoBEIR evaluators, similarity score outputs in mine_hard_negatives, Transformers v5 support and more. Details:
- CrossEncoder multi-processing: Similar to SentenceTransformer and SparseEncoder, you can now use multi-processing with CrossEncoder rerankers. Useful for multi-GPU and CPU settings, and simple to configure: just
- Multilingual NanoBEIR Support: You can now use community translations of the tiny NanoBEIR retrieval benchmark instead of only the English one, by passing
- Similarity scores in Hard Negatives Mining: When mining for hard negatives to create a strong training dataset, you can now pass
- Transformers v5: This release works with both Transformers v4 and the upcoming v5. In the future, Sentence Transformers will only work with Transformers v5, but not yet!
- Python 3.9 deprecation: Now that Python 3.9 has lost security support, Sentence Transformers no longer supports it.
Check out the full changelog for more details: https://github.com/huggingface/sentence-transformers/releases/tag/v5.2.0
I'm quite excited about what's coming. There's a huge draft PR with a notable refactor in the works that should bring some exciting support. Specifically, better multimodality, rerankers, and perhaps some late interaction in the future!
- CrossEncoder multi-processing: Similar to SentenceTransformer and SparseEncoder, you can now use multi-processing with CrossEncoder rerankers. Useful for multi-GPU and CPU settings, and simple to configure: just
device=["cuda:0", "cuda:1"] or device=["cpu"]*4 on the model.predict or model.rank calls.- Multilingual NanoBEIR Support: You can now use community translations of the tiny NanoBEIR retrieval benchmark instead of only the English one, by passing
dataset_id, e.g. dataset_id="lightonai/NanoBEIR-de" for the German benchmark.- Similarity scores in Hard Negatives Mining: When mining for hard negatives to create a strong training dataset, you can now pass
output_scores=True to get similarity scores returned. This can be useful for some distillation losses!- Transformers v5: This release works with both Transformers v4 and the upcoming v5. In the future, Sentence Transformers will only work with Transformers v5, but not yet!
- Python 3.9 deprecation: Now that Python 3.9 has lost security support, Sentence Transformers no longer supports it.
Check out the full changelog for more details: https://github.com/huggingface/sentence-transformers/releases/tag/v5.2.0
I'm quite excited about what's coming. There's a huge draft PR with a notable refactor in the works that should bring some exciting support. Specifically, better multimodality, rerankers, and perhaps some late interaction in the future!
sergiopaniego
posted
an
update
6 days ago
Post
1560
TRL now includes agent training support for GRPO‼️
Train 🕵️ agents with 🔧 tools, enabling interaction with external functions and APIs.
And of course, a new notebook and scripts to get you up to speed
📘 notebook tutorial: https://github.com/huggingface/trl/blob/main/examples/notebooks/grpo_agent.ipynb
📂 script examples: https://github.com/huggingface/trl/blob/main/examples/scripts/grpo_agent.py
📦 TRL v0.26.0 release: https://github.com/huggingface/trl/releases/tag/v0.26.0
Train 🕵️ agents with 🔧 tools, enabling interaction with external functions and APIs.
And of course, a new notebook and scripts to get you up to speed
📘 notebook tutorial: https://github.com/huggingface/trl/blob/main/examples/notebooks/grpo_agent.ipynb
📂 script examples: https://github.com/huggingface/trl/blob/main/examples/scripts/grpo_agent.py
📦 TRL v0.26.0 release: https://github.com/huggingface/trl/releases/tag/v0.26.0
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- 2 replies
sergiopaniego
posted
an
update
7 days ago
Post
2760
ICYMI, you can fine-tune open LLMs using Claude Code
just tell it:
“Fine-tune Qwen3-0.6B on open-r1/codeforces-cots”
and Claude submits a real training job on HF GPUs using TRL.
it handles everything:
> dataset validation
> GPU selection
> training + Trackio monitoring
> job submission + cost estimation
when it’s done, your model is on the Hub, ready to use
read more about the process: https://huggingface.co/blog/hf-skills-training
just tell it:
“Fine-tune Qwen3-0.6B on open-r1/codeforces-cots”
and Claude submits a real training job on HF GPUs using TRL.
it handles everything:
> dataset validation
> GPU selection
> training + Trackio monitoring
> job submission + cost estimation
when it’s done, your model is on the Hub, ready to use
read more about the process: https://huggingface.co/blog/hf-skills-training
Post
2540
installama.sh at the TigerBeetle 1000x World Tour !
Last week I had the chance to give a short talk during the TigerBeetle 1000x World Tour (organized by @jedisct1 👏 ) a fantastic event celebrating high-performance engineering and the people who love pushing systems to their limits!
In the talk, I focused on the CPU and Linux side of things, with a simple goal in mind: making the installation of llama.cpp instant, automatic, and optimal, no matter your OS or hardware setup.
For the curious, here are the links worth checking out:
Event page: https://tigerbeetle.com/event/1000x
GitHub repo: https://github.com/angt/installama.sh
Talk: https://youtu.be/pg5NOeJZf0o?si=9Dkcfi2TqjnT_30e
More improvements are coming soon. Stay tuned!
Last week I had the chance to give a short talk during the TigerBeetle 1000x World Tour (organized by @jedisct1 👏 ) a fantastic event celebrating high-performance engineering and the people who love pushing systems to their limits!
In the talk, I focused on the CPU and Linux side of things, with a simple goal in mind: making the installation of llama.cpp instant, automatic, and optimal, no matter your OS or hardware setup.
For the curious, here are the links worth checking out:
Event page: https://tigerbeetle.com/event/1000x
GitHub repo: https://github.com/angt/installama.sh
Talk: https://youtu.be/pg5NOeJZf0o?si=9Dkcfi2TqjnT_30e
More improvements are coming soon. Stay tuned!
- 1 reply
sergiopaniego
posted
an
update
7 days ago
Post
2191
We just released TRL v0.26.0!
It comes packed with updates:
> Agent training with tools in GRPO
> New CISPO & SAPO losses + reasoning rewards
> vLLM quantization in colocate mode
> Dataset shuffling in SFT
> Lots of NEW examples
> Tons of fixes and documentation improvements
It comes packed with updates:
> Agent training with tools in GRPO
> New CISPO & SAPO losses + reasoning rewards
> vLLM quantization in colocate mode
> Dataset shuffling in SFT
> Lots of NEW examples
> Tons of fixes and documentation improvements
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- 3 replies
sergiopaniego
posted
an
update
8 days ago
Post
2875
NEW:
@EssentialAI
just released Rnj-1, their first 8B model.
You can easily fine-tune it with GRPO using TRL to add reasoning capabilities to a compact mode
Free Colab link: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_rnj_1_instruct.ipynb
More free TRL notebooks: https://huggingface.co/docs/trl/main/en/example_overview#notebooks
You can easily fine-tune it with GRPO using TRL to add reasoning capabilities to a compact mode
Free Colab link: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_rnj_1_instruct.ipynb
More free TRL notebooks: https://huggingface.co/docs/trl/main/en/example_overview#notebooks
alozowski
authored
a
paper
8 days ago
sergiopaniego
posted
an
update
12 days ago
Post
2815
Want to get started with fine-tuning but don’t know where to begin? 🤓☝️
We’re expanding our collection of beginner-friendly free Colab notebooks so you can learn and fine-tune models using TRL at no cost
🔬 Check out the full list of free notebooks: https://huggingface.co/docs/trl/main/en/example_overview#notebooks
🔬 If you want more advanced content, we also have a lot to cover in the community tutorials: https://huggingface.co/docs/trl/community_tutorials
And now the obvious question: what would you like us to add next?
We’re expanding our collection of beginner-friendly free Colab notebooks so you can learn and fine-tune models using TRL at no cost
🔬 Check out the full list of free notebooks: https://huggingface.co/docs/trl/main/en/example_overview#notebooks
🔬 If you want more advanced content, we also have a lot to cover in the community tutorials: https://huggingface.co/docs/trl/community_tutorials
And now the obvious question: what would you like us to add next?
Post
1635
I'm excited to share that https://installama.sh is up and running! 🚀
On Linux / macOS / FreeBSD it is easier than ever:
And Windows just joined the party 🥳
Stay tuned for new backends on Windows!
On Linux / macOS / FreeBSD it is easier than ever:
curl https://installama.sh | shAnd Windows just joined the party 🥳
irm https://installama.sh | iexStay tuned for new backends on Windows!
sergiopaniego
posted
an
update
13 days ago
Post
2345
NEW:
@mistralai
released a fantastic family of multimodal models, Ministral 3.
You can fine-tune them for free on Colab using TRL ⚡️, supporting both SFT and GRPO
Link to the notebooks:
- SFT: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_ministral3_vl.ipynb
- GRPO: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_ministral3_vl.ipynb
- TRL and more examples: https://huggingface.co/docs/trl/index
You can fine-tune them for free on Colab using TRL ⚡️, supporting both SFT and GRPO
Link to the notebooks:
- SFT: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_ministral3_vl.ipynb
- GRPO: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_ministral3_vl.ipynb
- TRL and more examples: https://huggingface.co/docs/trl/index
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- 2 replies
sergiopaniego
posted
an
update
15 days ago
Post
2170
ICYMI, transformers v5 is out!
Grab a coffee ☕ and go read the announcement blog https://huggingface.co/blog/transformers-v5
Grab a coffee ☕ and go read the announcement blog https://huggingface.co/blog/transformers-v5
sergiopaniego
posted
an
update
16 days ago
Post
3100
want to use open models easily through an API?
Inference Providers might be exactly what you’re looking for sooo here’s a complete beginner-friendly walkthrough 🧐
https://www.youtube.com/watch?v=oxwsizy1Spw
Inference Providers might be exactly what you’re looking for sooo here’s a complete beginner-friendly walkthrough 🧐
https://www.youtube.com/watch?v=oxwsizy1Spw
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- 2 replies
Post
406
🚀
The fastest way to install and run
We are expanding hardware and OS support to make local AI even more accessible. This includes:
🌋 Vulkan support for Linux on
😈 FreeBSD support (CPU backend) on
✨ Lots of small optimizations and improvements under the hood.
Give it a try right now:
installama.sh update: Vulkan & FreeBSD support added!The fastest way to install and run
llama.cpp has just been updated!We are expanding hardware and OS support to make local AI even more accessible. This includes:
🌋 Vulkan support for Linux on
x86_64 and aarch64.😈 FreeBSD support (CPU backend) on
x86_64 and aarch64 too.✨ Lots of small optimizations and improvements under the hood.
Give it a try right now:
curl angt.github.io/installama.sh | MODEL=unsloth/Qwen3-4B-GGUF:Q4_0 sh
sergiopaniego
posted
an
update
20 days ago
Post
1749
nanochat is now in transformers!
The LLM by @karpathy is officially in the library, and we wrote a blog covering: how did we port the model, differences from the original, and how to run or train it.
go read it 🤓
nanochat-students/transformers
The LLM by @karpathy is officially in the library, and we wrote a blog covering: how did we port the model, differences from the original, and how to run or train it.
go read it 🤓
nanochat-students/transformers
sergiopaniego
posted
an
update
21 days ago
Post
3963
you gotta go fast and go read the latest blog by
@ror
et al. explaining Continuous Batching in depth
https://huggingface.co/blog/continuous_batching
https://huggingface.co/blog/continuous_batching
sergiopaniego
posted
an
update
23 days ago
Post
1720
Interested in RL training environments?
We just released a beginner-friendly walkthrough notebook!
Train a model to play Wordle using TRL + OpenEnv (TextArena) + GRPO + vLLM.
happy learning! 🌱
Notebook: https://github.com/huggingface/trl/blob/main/examples/notebooks/openenv_wordle_grpo.ipynb
OpenEnv guide in TRL: https://huggingface.co/docs/trl/main/en/openenv
We just released a beginner-friendly walkthrough notebook!
Train a model to play Wordle using TRL + OpenEnv (TextArena) + GRPO + vLLM.
happy learning! 🌱
Notebook: https://github.com/huggingface/trl/blob/main/examples/notebooks/openenv_wordle_grpo.ipynb
OpenEnv guide in TRL: https://huggingface.co/docs/trl/main/en/openenv
Post
1971
One command line is all you need...
...to launch a local llama.cpp server on any Linux box or any Metal-powered Mac 🚀
Learn more: https://github.com/angt/installama.sh
...to launch a local llama.cpp server on any Linux box or any Metal-powered Mac 🚀
curl angt.github.io/installama.sh | MODEL=unsloth/gpt-oss-20b-GGUF shLearn more: https://github.com/angt/installama.sh