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Introducing Halo, the best framework for post-training of open-source models. Halo delivers up to 2.8x the throughput of stock TRL with less peak memory, while models stay in their native HuggingFace format. Star us on GitHub:

White Circle

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2,252,490 görüntüleme • 6 gün önce •via X (Twitter)

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White Circle profil fotoğrafı

White Circle6 gün önce

Every model we train at White Circle now uses Halo. The same codebase runs LoRA on a 24 GB GPU, multi-node training on B300s, and async RL. Technical blog:

White Circle profil fotoğrafı

White Circle6 gün önce

Other training frameworks often require a separate implementation for each model family. In Halo, adding a new model family takes just 100 lines of connecting a wrapper instead of rewriting the model.

White Circle profil fotoğrafı

White Circle6 gün önce

One YAML file contains the model, training method, GPU setup and checkpoint settings. One command starts training.

White Circle profil fotoğrafı

White Circle6 gün önce

Halo supports data, tensor, context, expert and expert-tensor parallelism. It also includes optimized attention, grouped-GEMM and fused loss kernels.

White Circle profil fotoğrafı

White Circle6 gün önce

Halo is not limited to supervised fine-tuning. It supports async RL and training with external environments. We are excited to partner with the @sgl_project team to make it the primary engine for rollouts.

White Circle profil fotoğrafı

White Circle6 gün önce

On @OpenAI gpt-oss-20b, Halo delivered 2.3–2.8x the throughput of stock TRL with less peak memory. Both runs used the same FlashAttention, Liger, fused cross-entropy and grouped-GEMM optimizations.

White Circle profil fotoğrafı

White Circle6 gün önce

We partnered with @liquidai to add native Halo support for LFM2.5-8B-A1B. On one B300, Halo was up to 20% faster than next best open-source framework.

White Circle profil fotoğrafı

White Circle6 gün önce

We also used Halo to fine-tune @Zai_org GLM-4.7-Flash on 177M tokens of agentic traces. The resulting model improved SWE-rebench-V2 by @nebiusai from 33% to 42%. Halo reached up to 1.63× TRL throughput on the same model precision and data. Model and write-up:

White Circle profil fotoğrafı

White Circle6 gün önce

Thanks for reading to the end! Star us on GitHub: Read more:

Daniel Smidstrup profil fotoğrafı

Daniel Smidstrup6 gün önce

Open source is the way, and it's only going to get more and more relevant in the coming time! :)

White Circle profil fotoğrafı

White Circle6 gün önce

True

leo profil fotoğrafı

leo6 gün önce

insane way to kick off the week -- congrats @whitecircle team

White Circle profil fotoğrafı

White Circle6 gün önce

Lets go!

neural nets. profil fotoğrafı

neural nets.6 gün önce

crazy reading through the docs

Vishal Singh 🥑 profil fotoğrafı

Vishal Singh 🥑6 gün önce

love that this is open source, congrats team ❤️

Catalin profil fotoğrafı

Catalin6 gün önce

Congrats on the awesome work, team! Good to see training infra being open source 👏 Starred the repo to contribute to the growth.

White Circle profil fotoğrafı

White Circle6 gün önce

thanks!

Paul Mit profil fotoğrafı

Paul Mit6 gün önce

great idea you've got my star, guys

giulia profil fotoğrafı

giulia6 gün önce

took me one read to understand exactly who this is for. that almost never happens with infra launches. congrats team!!! starred 👀

Mac profil fotoğrafı

Mac6 gün önce

Peak improvement, I will try it from first hand

sui profil fotoğrafı

sui6 gün önce

so we get Grok 4.7 along with this today? nice.

White Circle profil fotoğrafı

White Circle6 gün önce

Crazy day!

Maxime Labonne profil fotoğrafı

Maxime Labonne6 gün önce

Congrats guys!

Virgile RIETSCH profil fotoğrafı

Virgile RIETSCH6 gün önce

This is huge !!

Levan Kvirkvelia profil fotoğrafı

Levan Kvirkvelia6 gün önce

haloshi framework

𝗕𝗿𝗶𝗮𝗻 𝗥𝗲𝘆 profil fotoğrafı

𝗕𝗿𝗶𝗮𝗻 𝗥𝗲𝘆6 gün önce

wait what this is incredible

White Circle profil fotoğrafı

White Circle6 gün önce

ty Brian! show the repo some love ⭐

ivy profil fotoğrafı

ivy6 gün önce

so excited!!!

White Circle profil fotoğrafı

White Circle6 gün önce

🥰

Ted | unfair.so profil fotoğrafı

Ted | unfair.so6 gün önce

Congrats team!

Soraia profil fotoğrafı

Soraia6 gün önce

Great work! You got my star ⭐️

Nidhi Singh profil fotoğrafı

Nidhi Singh6 gün önce

love that this is open source, congrats!

Rushil Chopra profil fotoğrafı

Rushil Chopra6 gün önce

I have always wanted to train a custom math model, let’s see how this goes!

White Circle profil fotoğrafı

White Circle6 gün önce

Keep us posted, any feedback is welcome

AshutoshShrivastava profil fotoğrafı

AshutoshShrivastava6 gün önce

love seeing more training infrastructure get open-sourced. congrats on the release!

Ion profil fotoğrafı

Ion6 gün önce

Congrats! Impressive what you delivered there 👀

Nikita Andersson profil fotoğrafı

Nikita Andersson6 gün önce

@sarapenrique This is fabulous

Hauke 🌱🦀 profil fotoğrafı

Hauke 🌱🦀6 gün önce

Uh training my own models for my products would be pretty neat. I need better hardware 👀

White Circle profil fotoğrafı

White Circle6 gün önce

💚

Prasenjit profil fotoğrafı

Prasenjit6 gün önce

oh this is sick actually, well cooked

Kalash profil fotoğrafı

Kalash6 gün önce

banger stuff 💥

Dan Kulkov profil fotoğrafı

Dan Kulkov6 gün önce

insane start of the week congrats team!

Aditi profil fotoğrafı

Aditi6 gün önce

wait… 2.8x faster than stock trl ??

Vasko profil fotoğrafı

Vasko6 gün önce

congrats to the team!

Lucas Valbuena profil fotoğrafı

Lucas Valbuena6 gün önce

congrats!

White Circle profil fotoğrafı

White Circle6 gün önce

Thank you

Sameer profil fotoğrafı

Sameer6 gün önce

congrats! def keeping an eye on this one.

Suraj Sharma profil fotoğrafı

Suraj Sharma6 gün önce

The interesting part is keeping models in native HuggingFace format while getting that throughput boost.

White Circle profil fotoğrafı

White Circle6 gün önce

Worked hard on this

Suraj Sharma profil fotoğrafı

Suraj Sharma6 gün önce

LFG 🫡🙌

Jackson profil fotoğrafı

Jackson6 gün önce

Really cool. Really unique and impressive numbers :•)

Marco Franzon profil fotoğrafı

Marco Franzon6 gün önce

The only limit now is the hardware. Everyone can now train or finetune a model without rewriting it for the training phase. Awesome!

White Circle profil fotoğrafı

White Circle6 gün önce

thanks Marco!

Marco Franzon profil fotoğrafı

Marco Franzon6 gün önce

Great work !

Deni profil fotoğrafı

Deni6 gün önce

Congrats on the launch team! Starred.

Ole Lehmann profil fotoğrafı

Ole Lehmann6 gün önce

happy launch day guys"

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>build a 1.5B VLA on top of MiniCPM-V 4.6 >compete with π0.5 and Qwen-VLA >use roughly half the visual-processing compute >remember previous steps without blowing up inference cost >pull the network plug >let the robot keep tracking — fully local, fully onboard >with CUDA Graph optimization and custom Triton fused kernels, MiniCPM-Robot inference throughput goes from 10 Hz → 33 Hz, and up to 36 Hz on the same NVIDIA H20 >release the models and framework as open source so why does robotics still rely on bigger models and the cloud? OpenBMB just dropped MiniCPM-Robot at WAIC two models + one inference framework 🧵

>build a 1.5B VLA on top of MiniCPM-V 4.6 >compete with π0.5 and Qwen-VLA >use roughly half the visual-processing compute >remember previous steps without blowing up inference cost >pull the network plug >let the robot keep tracking — fully local, fully onboard >with CUDA Graph optimization and custom Triton fused kernels, MiniCPM-Robot inference throughput goes from 10 Hz → 33 Hz, and up to 36 Hz on the same NVIDIA H20 >release the models and framework as open source so why does robotics still rely on bigger models and the cloud? OpenBMB just dropped MiniCPM-Robot at WAIC two models + one inference framework 🧵

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Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

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30,392 görüntüleme • 1 ay önce

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses three NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,867 görüntüleme • 8 ay önce

Introducing Silico: the platform for building AI models with the precision of written software. Silico lets researchers and engineers see inside their models, debug failures, and intentionally design them from the ground up. Early access is open now. 🧵(1/10)

Introducing Silico: the platform for building AI models with the precision of written software. Silico lets researchers and engineers see inside their models, debug failures, and intentionally design them from the ground up. Early access is open now. 🧵(1/10)

Goodfire

112,156 görüntüleme • 5 ay önce