Unofficial Mistral Community [deprecated]

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Unofficial org for community upload of Mistral's Open Source models.

Recent Activity

nielsr submitted a paper 12 days ago

MonkeyOCRv2: A Visual-Text Foundation Model for Document AI

nielsr submitted a paper 13 days ago

Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model

nielsr submitted a paper 18 days ago

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

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danielhanchen

posted an update 7 days ago

Post

4149

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

danielhanchen

posted an update 9 days ago

Post

5464

Gemma 4 is now faster and much more accurate! 🚀

Google made huge improvements to tool-calling and chat accuracy, reliability + speed.
To get fixes, re-download our updated GGUF, MLX, NVFP4 quants!

Unsloth quants: https://huggingface.co/collections/unsloth/gemma-4
Gemma 4 Guide: https://unsloth.ai/docs/models/gemma-4

8 replies

nielsr

submitted a paper to Daily Papers 12 days ago

MonkeyOCRv2: A Visual-Text Foundation Model for Document AI

Paper • 2607.11562 • Published 14 days ago • 76

danielhanchen

posted an update 12 days ago

Post

4556

We’re releasing Gemma 4 NVFP4 quants that run 1.5× faster on your GPU.

Gemma-4-12B NVFP4 works on 11GB VRAM.
26B-A4B hits 13K tok/s (B200).

Unsloth NVFP4 enables faster, more accurate 4-bit Blackwell inference.

Blog: https://unsloth.ai/docs/basics/nvfp4
Gemma NVFP4: https://huggingface.co/collections/unsloth/nvfp4

2 replies

nielsr

submitted a paper to Daily Papers 13 days ago

Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model

Paper • 2607.11643 • Published 14 days ago • 44

danielhanchen

posted an update 17 days ago

Post

4163

We’re releasing new Qwen3.6 quants that run 2.5× faster on your GPU. ⚡

Qwen3.6-27B NVFP4 runs on 24GB VRAM.
35B-A3B can hit 17,561 tok/s (B200).

We also improved accuracy, tool calling, agent use, and looping.

Qwen3.6 NVFP4: https://huggingface.co/collections/unsloth/nvfp4
Guide: https://unsloth.ai/docs/models/qwen3.6#nvfp4

1 reply

nielsr

submitted 2 papers to Daily Papers 18 days ago

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

Paper • 2607.07508 • Published 19 days ago • 26

Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity

Paper • 2607.07386 • Published 19 days ago • 12

nielsr

submitted 2 papers to Daily Papers 19 days ago

SiamJEPA: On the Role of Siamese Student Encoders in JEPA

Paper • 2607.04044 • Published 23 days ago • 1

HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better

Paper • 2607.04884 • Published 21 days ago • 9

danielhanchen

posted an update 20 days ago

Post

6072

DeepSeek-V4 can now run locally with Unsloth GGUFs! 🐳

Run lossless DeepSeek-V4-Flash on 168GB RAM or
3-bit works on 110GB Mac, RAM, VRAM setups.

Run via Unsloth Studio or llama.cpp.

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

nielsr

submitted a paper to Daily Papers 20 days ago

Unified Audio Intelligence Without Regressing on Text Intelligence

Paper • 2607.05196 • Published 21 days ago • 23

danielhanchen

posted an update about 1 month ago

Post

3340

1-bit GLM-5.2 GGUF vs. Claude 4.8 Opus vs. GPT-5.5

We gave 3 models the same prompt and compared one-shot outputs.

The 1-bit GLM-5.2 GGUF ran locally on a Mac Studio M3 Ultra with 256GB RAM at ~21.6 tok/s.

Which output do you like best?
GGUF: unsloth/GLM-5.2-GGUF

3 replies

nielsr

submitted a paper to Daily Papers about 1 month ago

Duration Aware Scheduling for ASR Serving Under Workload Drift

Paper • 2603.11273 • Published Mar 11 • 3

danielhanchen

posted an update about 1 month ago

Post

4599

Google's new DiffusionGemma can now run at 2000+ tokens/sec! ⚡

We made local DiffusionGemma inference 1.8× faster.
Run it on 18GB RAM via Unsloth Studio.

GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/models/diffusiongemma

4 replies

danielhanchen

posted an update about 2 months ago

Post

1188

Google releases DiffusionGemma.✨
The new 26B-A4B diffusion text model runs locally on 18GB RAM.

Run with 4x faster text generation, thinking, image, video and 256K context. Run and train via Unsloth Studio.

GGUF: unsloth/diffusiongemma-26B-A4B-it-GGUF
Guide: https://unsloth.ai/docs/models/diffusiongemma

1 reply

danielhanchen

posted an update about 2 months ago

Post

4296

Google releases Gemma 4 QAT. ✨
You can now run Gemma 4 at 3x less memory with near original performance.

QAT makes it possible to run Gemma 4 26B-A4B on 16GB RAM.

GGUFs: https://huggingface.co/collections/unsloth/gemma-4-qat
QAT Guide: https://unsloth.ai/docs/models/gemma-4/qat

1 reply

danielhanchen

posted an update about 2 months ago

Post

9342

Gemma 4 12B can now run locally on just 8GB RAM via Dynamic GGUFs.

Google's new model, Gemma 4 12B Unified supports image, audio and 256K context.
You can run and train the model via Unsloth Studio.

GGUF: unsloth/gemma-4-12b-it-GGUF
Guide: https://unsloth.ai/docs/models/gemma-4