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woojun-jung  authored a paper 6 days ago
QEVA: A Reference-Free Evaluation Metric for Narrative Video Summarization with Multimodal Question Answering
xianbao  submitted a paper about 2 months ago
The Curse and Blessing of Mean Bias in FP4-Quantized LLM Training
julien-c  submitted a paper 3 months ago
Shaping capabilities with token-level data filtering
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Aurelien-Morgan 
posted an update about 13 hours ago
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@retrain-pipelines v0.2.0 is out !
I'm at Station F at My booth with GOSIM Paris 2026 today & tomorrow.
Come meet me for a live in-person demo and a chat !
danielhanchen 
posted an update about 14 hours ago
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2870
We made a guide on how to run open LLMs in Claude Code, Codex and OpenClaw.

Use Gemma 4 and Qwen3.6 GGUFs for local agentic coding on 24GB RAM

Run with self-healing tool calls, code execution, web search via the Unsloth API endpoint and llama.cpp

Guide: https://unsloth.ai/docs/basics/api
danielhanchen 
posted an update 7 days ago
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10633
Unsloth is now one of the top 10 most followed organizations on Hugging Face. 🤗🦥

Thanks so much for all the support!
Our HF page:
unsloth
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danielhanchen 
posted an update 14 days ago
danielhanchen 
posted an update 20 days ago
Aurelien-Morgan 
posted an update 23 days ago
danielhanchen 
posted an update 29 days ago
danielhanchen 
posted an update about 1 month ago
danielhanchen 
posted an update about 1 month ago
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2759
A new way to use Unsloth.

Coming soon...
1aurent 
authored a paper about 1 month ago
danielhanchen 
posted an update about 1 month ago
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930
You don’t need to set LLM parameters anymore! 🚀

llama.cpp uses only the context length + compute your local setup needs. Unsloth also auto-applies the correct model settings

Try in Unsloth Studio - now with precompiled llama.cpp binaries.

GitHub: https://github.com/unslothai/unsloth
  • 2 replies
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danielhanchen 
posted an update about 2 months ago
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3412
Introducing Unsloth Studio ✨
A new open-source web UI to train and run LLMs.

• Run models locally on Mac, Windows, Linux
• Train 500+ models 2x faster with 70% less VRAM
• Supports GGUF, vision, audio, embedding models
• Auto-create datasets from PDF, CSV, DOCX
• Self-healing tool calling and code execution
• Compare models side by side + export to GGUF

GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/new/studio

Available now on Hugging Face, NVIDIA, Docker and Colab.
danielhanchen 
posted an update about 2 months ago
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3933
We collaborated with NVIDIA to teach you about Reinforcement Learning and RL environments. 💚 Learn:

• Why RL environments matter + how to build them
• When RL is better than SFT
• GRPO and RL best practices
• How verifiable rewards and RLVR work

Blog: https://unsloth.ai/blog/rl-environments
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xianbao 
submitted a paper to Daily Papers about 2 months ago
danielhanchen 
posted an update 2 months ago
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3458
100,000+ models trained with Unsloth have now been open-sourced on 🤗Hugging Face! 🦥

Here are the most popular ones you can run local:
1. TeichAI - GLM-4.7-Flash distilled from Claude 4.5 Opus (high)
2. Zed - Qwen Coder 7B fine-tuned for stronger coding
3. DavidAU - Llama-3.3-8B distilled from Claude 4.5 Opus (high)
4. huihui - gpt-oss made “abliberated”

Links to models:
1. TeichAI: TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF
2. Zed: zed-industries/zeta
3. DavidAU: DavidAU/Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning
4. huihui: huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated

See all the 100K latest models fine-tuned with Unsloth here: https://huggingface.co/models?other=u
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Jackmin108 
authored a paper 2 months ago
danielhanchen 
posted an update 3 months ago
1aurent 
authored a paper 3 months ago
danielhanchen 
posted an update 3 months ago
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5225
We collaborated with Hugging Face to enable you to train MoE models 12× faster with 35% less VRAM via our new Triton kernels (no accuracy loss). 🤗

Train gpt-oss locally on 12.8GB VRAM with our free notebooks: https://unsloth.ai/docs/new/faster-moe
  • 1 reply
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JingzeShi 
authored a paper 3 months ago