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Jackmin108  authored a paper 8 days ago
Arcee Trinity Large Technical Report
1aurent  authored a paper 17 days ago
Ministral 3
JingzeShi  authored a paper 19 days ago
Towards Automated Kernel Generation in the Era of LLMs
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danielhanchen 
posted an update 4 days ago
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3234
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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danielhanchen 
posted an update 9 days ago
danielhanchen 
posted an update 12 days ago
danielhanchen 
posted an update 13 days ago
1aurent 
authored a paper 17 days ago
danielhanchen 
posted an update 18 days ago
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5153
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
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JingzeShi 
authored 2 papers 19 days ago
JingzeShi 
submitted a paper to Daily Papers 19 days ago
danielhanchen 
posted an update 23 days ago
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3686
We created a tool-calling guide for local LLMs!

Learn how to use any open model like Qwen3-Coder-Next and GLM-4.7-Flash for function calling.

Guide: https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms

We provide hands-on examples for: story writing, Python execution, terminal tool calls, maths and more.
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danielhanchen 
posted an update 25 days ago
danielhanchen 
posted an update about 1 month ago
danielhanchen 
posted an update about 1 month ago
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2619
You can now fine-tune embedding models in our free Unsloth notebook! 🤗

Fine-tuning embedding models improves retrieval & RAG by aligning vectors to your domain-specific notion of similarity, improving search, clustering, and recommendations on your data.

⭐ Blog + Notebooks: https://unsloth.ai/docs/new/embedding-finetuning

Unsloth trains embedding models 1.8-3.3x faster with 20% less VRAM, 2x longer context & no accuracy loss vs. FA2 setups.

We'd like to thank Hugging Face and Unsloth contributor: electroglyph for making this possible!
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danielhanchen 
posted an update about 1 month ago
danielhanchen 
posted an update about 1 month ago
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2863
You can now do reinforcement learning training with 7× longer context and no accuracy loss, via our new batching algorithms.

Long reasoning chains in RL are costly, but now we enable you to train gpt-oss with GRPO & reach 380K context on a 192GB GPU.

Blog: https://unsloth.ai/docs/new/grpo-long-context
andreaskoepf 
authored 3 papers about 1 month ago
danielhanchen 
posted an update about 2 months ago
csabakecskemeti 
posted an update 2 months ago
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3269
Just sharing a result of a homelab infrastructure experiment:

I've managed to setup a distributed inference infra at home using a DGX Spark (128GB unified gddr6) and a linux workstation with an RTX 6000 Pro (96GB gddr7) connected via 100Gbps RoCEv2. The model I've used (https://lnkd.in/gx6J7YuB) is about 140GB so could not fit either of the GPU. Full setup and tutorial soon on devquasar.com



Screen recording:
https://lnkd.in/gKM9H5GJ
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