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Daniel (Unsloth) PRO

danielhanchen

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https://unsloth.ai/

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liked a model about 7 hours ago

xtools-at/DeepSeek-V4-Pro-Qwen3.5-9B-UNCENSORED-heretic

new activity about 11 hours ago

unsloth/DeepSeek-V4.1-Flash-1017-GGUF:Does UD-Q3_K_XL up work on mainline llama.cpp?

liked a model about 17 hours ago

unsloth/Qwen3.8-Flash-Next-GGUF

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posted an update about 17 hours ago

Post

1,681

Qwen3.8-Flash-Next GGUFs now run faster with less thinking and less token use.

Unsloth Dynamic V3.1 GGUFs bring thinking & token use to match 95% of full precision Qwen3.8-Flash.

Results: 50% fewer tokens + 15% higher accuracy vs same size quants.

GGUF: unsloth/Qwen3.8-Flash-Next-GGUF
Analysis/Guide: https://unsloth.ai/docs/models/qwen3.8-next

posted an update 4 days ago

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7,102

We’re releasing new Qwen3.8-Flash-Next GGUFs with 15% higher accuracy.

Unsloth Dynamic V3.1 outperforms others by >15% on Terminal Bench, DeepSWE & more benchmarks (256K context).

Run on 32GB+ RAM setups.

GGUF: unsloth/Qwen3.8-Flash-Next-GGUF
Blog: https://unsloth.ai/docs/models/qwen3.8-next

posted an update 9 days ago

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803

We’re releasing new DeepSeek-V4.1-Flash-1017 GGUFs, ranking highest across benchmarks. 🐋

Dynamic V3.1 leads across nearly all benchmarks: GPQA Diamond, AIME25, LiveCodeBench v6, MMLU-Pro, Aider & more.

Run on 64GB+ RAM.

GGUF: unsloth/DeepSeek-V4.1-Flash-1017-GGUF
Blog: https://unsloth.ai/docs/models/deepseek-v4.1-flash

1 reply

posted an update 11 days ago

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2,506

Your local LLM can now take control of a browser and complete tasks autonomously via Unsloth!

Qwen3.8-27B successfully controlled the browser to complete tasks like chess, painting, with no human intervention.

GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/browser-use

1 reply

posted an update 20 days ago

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5,063

Run DeepSeek V4.1-Flash-1017 locally! 🐳

We made 1-bit retain ~88.2% accuracy while shrinking size from 508GB to 250GB (−52%).

Run on a 128GB+ RAM device. V4.1 Flash 1017 is the strongest open model to date

Guide: https://unsloth.ai/docs/models/deepseek-v4.1-flash
GGUF: unsloth/DeepSeek-V4.1-Flash-1017-GGUF

replied to their post about 1 month ago

Yes of course. Qwen3.8 is already supported in Unsloth

posted an update about 1 month ago

Post

5,177

You can now train your own Decision model like Jev locally!

We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.

Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.

GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth

5 replies

posted an update about 1 month ago

Post

4,157

Google releases EmbeddingGemma 2, a new open embedding model that runs locally on 0.5GB RAM.

The 740M parameter Apache 2.0 model combines a 270M text model with vision (170M) + audio (300M).

Run & train the model via Unsloth.

GGUF: unsloth/embeddinggemma-2-GGUF
Guide: https://unsloth.ai/docs/models/embeddinggemma-2

posted an update about 1 month ago

Post

9,206

Unsloth has surpassed 500M model downloads on Hugging Face! 🦥🤗

Qwen3.8-27B GGUF is already Unsloth’s #1 most-downloaded model ever.

Thanks for all your support! Follow us:

unsloth

1 reply

posted an update about 2 months ago

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3,778

Qwen-Image-2.1 can now run locally on 12GB VRAM with Unsloth GGUFs! 🖼️

The 7B model performs on par with Nano Banana 2.0.

For higher quality, you can also run Dynamic FP8 on just 6GB of VRAM via offloading.

GGUF: unsloth/Qwen-Image-2.1-GGUF
Guide: https://unsloth.ai/docs/models/qwen-image-2.1

posted an update 2 months ago

Post

6,941

Qwen3.8-27B Unsloth GGUF is now the #1 most-liked GGUF of all time! 🤗🦥

The model hit 10M downloads and 3.7K likes in just 24 days on Hugging Face - all thanks to you.

GGUF: unsloth/Qwen3.8-27B-GGUF
Guide: https://unsloth.ai/docs/models/qwen3.8

5 replies

replied to their post 3 months ago

No it's not but it's a completely new product! It's a desktop app now

replied to their post 3 months ago

Thank you I fixed it ♥️

posted an update 3 months ago

Post

7,591

Introducing Unsloth Desktop 🦥
The first desktop app to run and train models locally.

• Open-source. Runs on Mac, Windows and Linux
• Supports MLX, diffusion image/video, audio, GGUF
• Connect Claude Code and Codex to local LLMs
• 50% more accurate, self-healing tool calls + sandboxed code exec
• Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
• Train models 2× faster with 70% less VRAM
• Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
• Use Unsloth’s OpenAI-compatible API and cloud models
• Securely deploy LLMs remotely and access anywhere

Unsloth Desktop is now available on http://unsloth.ai
and GitHub.

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

6 replies

posted an update 3 months ago

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3,847

DeepSeek-V4-Flash can now run 2× faster locally with DSpark! ⚡️

DSpark enables V4-Flash-0731 GGUFs to generate ~1.4–2× faster with no accuracy change.

DeepSeek-V4-Flash-0731 can reach at 120 tokens/s.

GGUFs: unsloth/DeepSeek-V4-Flash-0731-GGUF
Guide: https://unsloth.ai/docs/models/deepseek-v4

1 reply

posted an update 3 months ago

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3,589

We compared 1-bit Kimi K3 to Claude Opus 5 and GPT 5.6. 🤯

We gave 4 models the same prompt: Create a glass aquarium whose side panel develops a visible crack and then bursts...

1-bit Kimi K3 GGUF ran locally on 4x B200s at 36 tok/s.

GGUF: unsloth/Kimi-K3-GGUF
GitHub repo: https://github.com/unslothai/unsloth

1 reply

replied to their post 3 months ago

Which ones? We are working on even smaller ones

posted an update 3 months ago

Post

5,791

Kimi K3 can now be run locally! ✨

The 1-bit model retains ~78.9% accuracy after we shrunk it from 1.56TB to 594GB (-62% size).

Run on a Mac Studio connected with 128GB RAM device. Kimi K3 is the strongest open model to date.

GGUF: unsloth/Kimi-K3-GGUF
Guide: https://unsloth.ai/docs/models/kimi-k3

5 replies

replied to their post 4 months ago

As long as it's above RDNA2 it should work

posted an update 4 months ago

Post

6,080

Introducing Unsloth for AMD 🚀
You can now train & run LLMs on your AMD hardware

• We collaborated with AMD to enable you to train & run 500+ models on AMD GPUs
• Works on Windows, WSL, Linux
• Train Qwen, Gemma on just 3GB VRAM

GitHub: https://github.com/unslothai/unsloth
Blog + Guide: https://unsloth.ai/docs/basics/amd

3 replies

Daniel (Unsloth) PRO

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