I see you mentioned in the README using gpt and claude. Have you determined this is ok under anthropic's terms? I suppose it is because Anthropic offers no model training framework and therefore it's not competing ?? Not sure on this.

reacted to medmekk's post with ❤️ 4 days ago

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4298

🚀 Introducing Halo 1.0

Today, we are open-sourcing Halo, the training framework we use to train every model at White Circle.

It comes with:
🧠 Full post-training stack: SFT, DPO/KTO/SMPO, reward modeling, GRPO, distillation
🤖 Async multi-turn RL with vLLM/SGLang rollouts and sandboxed tool use
⚡ ~2.8× TRL throughput on 8× B300 (EP+FSDPv2, FA4, fp8/fp4)
🤗 Dense HF models + 15 MoE families (Qwen, GLM, Mistral, DeepSeek-V4…)
🛠️ One halo command, prebuilt Docker images, and docs for humans and agents

💻 https://github.com/whitecircle/halo

Try it and tell us what you're training

1 reply

reacted to prithivMLmods's post with 👍 4 days ago

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790

VisionGuardrail EVO-2, a multimodal image-classification content-safety model based on Qwen/Qwen3.8-27B, is now available on the Hub!

Stricter image classification than before, with a dense 27-billion-parameter multimodal model, more precise reasoning, and improved captions for classifying visual media.

➠ Models: prithivMLmods/VisionGuardrail-Evo2-27B, prithivMLmods/VisionGuardrail-Evo2-27B-GGUF

➠ Collection: https://huggingface.co/collections/prithivMLmods/visionguardrail-evo2

➠ Previous Models: https://huggingface.co/collections/prithivMLmods/visionguardrail-collection

⤷ To learn more, visit the app page or the respective model pages.

replied to OppaAI's post 4 days ago

I read the chat - this is really entertaining. I need to check this out.

liked a Space 4 days ago

Open Discovery Challenge

🧬

Neglected-disease drug leaderboard for AI-designed molecules

New activity in BananaMind/BananaMind-SLM-Leaderboard 4 days ago

CodeSoft SLM Arena

#9 opened 4 days ago by

wayneworkman2012

reacted to Datdanboi25's post with ❤️ 6 days ago

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3424

THE SLM FRONTIER ADVANCES!

bench-labs/cagliostro-v3 just hit an Intelligence Index of 26.13 on the AxiomicLabs/Open_SLM_Leaderboard a 146M-param model trained completely from scratch on a single consumer GPU. That's 2nd place overall, and as far as I can tell, the most capable SLM trained on consumer hardware to date. Beating SmolLM-135m on 1/8th of the data is just silly levels of efficiency.

Big congrats to the @BenchLabs team and specifically @TobiasLogic !