danielhanchen (Daniel (Unsloth))

posted an update 2 days ago

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3594

Run OpenAI's new gpt-oss models locally with Unsloth GGUFs! 🔥🦥
20b GGUF: unsloth/gpt-oss-20b-GGUF
120b GGUF: unsloth/gpt-oss-120b-GGUF

Model will run on 14GB RAM for 20b and 66GB for 120b.

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posted an update 15 days ago

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2549

It's Qwen3 week! 💜 We uploaded Dynamic 2-bit GGUFs for:

Qwen3-Coder: unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF
Qwen3-2507: unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF

So you can run them both locally!
Guides are in model cards.

1 reply

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posted an update 24 days ago

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2952

Made some 245GB (80% size reduction) 1.8bit quants for Kimi K2!

unsloth/Kimi-K2-Instruct-GGUF

posted an update about 1 month ago

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3226

We fixed more issues! Use --jinja for all!
* Fixed Nanonets OCR-s unsloth/Nanonets-OCR-s-GGUF
* Fixed THUDM GLM-4 unsloth/GLM-4-32B-0414-GGUF
* DeepSeek Chimera v2 is uploading! unsloth/DeepSeek-TNG-R1T2-Chimera-GGUF

replied to their post about 1 month ago

Thank you!

posted an update about 1 month ago

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2631

Gemma 3n finetuning is now 1.5x faster and uses 50% less VRAM in Unsloth!

Click "Use this model" and click "Google Colab"!

unsloth/gemma-3n-E4B-it

unsloth/gemma-3n-E2B-it

https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Conversational.ipynb

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posted an update about 2 months ago

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918

We updated lots of our GGUFs and uploaded many new ones!
* unsloth/dots.llm1.inst-GGUF
* unsloth/Jan-nano-GGUF
* unsloth/Nanonets-OCR-s-GGUF
* Updated and fixed Q8_0 upload for unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF
* Added Q2_K_XL for unsloth/DeepSeek-R1-0528-GGUF
* Updated and fixed Vision support for unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF

posted an update about 2 months ago

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2189

Mistral releases Magistral, their new reasoning models! 🔥
GGUFs to run: unsloth/Magistral-Small-2506-GGUF

Magistral-Small-2506 excels at mathematics and coding.

You can run the 24B model locally with just 32GB RAM by using our Dynamic GGUFs.

posted an update 2 months ago

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3535

New DeepSeek-R1-0528 1.65-bit Dynamic GGUF!

Run the model locally even easier! Will fit on a 192GB Macbook and run at 7 tokens/s.

DeepSeek-R1-0528 GGUFs: unsloth/DeepSeek-R1-0528-GGUF
Qwen3-8B DeepSeek-R1-0528 GGUFs: unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF

And read our Guide: https://docs.unsloth.ai/basics/deepseek-r1-0528

posted an update 3 months ago

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2135

💜 Qwen3 128K Context Length: We've released Dynamic 2.0 GGUFs + 4-bit safetensors!
Fixed: Now works on any inference engine and fixed issues with the chat template.
Qwen3 GGUFs:
30B-A3B: unsloth/Qwen3-30B-A3B-GGUF
235-A22B: unsloth/Qwen3-235B-A22B-GGUF
32B: unsloth/Qwen3-32B-GGUF

Read our guide on running Qwen3 here: https://docs.unsloth.ai/basics/qwen3-how-to-run-and-finetune

128K Context Length:
30B-A3B: unsloth/Qwen3-30B-A3B-128K-GGUF
235-A22B: unsloth/Qwen3-235B-A22B-128K-GGUF
32B: unsloth/Qwen3-32B-128K-GGUF

All Qwen3 uploads: unsloth/qwen3-680edabfb790c8c34a242f95

posted an update 3 months ago

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6060

🦥 Introducing Unsloth Dynamic v2.0 GGUFs!
Our v2.0 quants set new benchmarks on 5-shot MMLU and KL Divergence, meaning you can now run & fine-tune quantized LLMs while preserving as much accuracy as possible.

Llama 4: unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
DeepSeek-R1: unsloth/DeepSeek-R1-GGUF-UD
Gemma 3: unsloth/gemma-3-27b-it-GGUF

We made selective layer quantization much smarter. Instead of modifying only a subset of layers, we now dynamically quantize all layers so every layer has a different bit. Now, our dynamic method can be applied to all LLM architectures, not just MoE's.

Blog with Details: https://docs.unsloth.ai/basics/dynamic-v2.0

All our future GGUF uploads will leverage Dynamic 2.0 and our hand curated 300K–1.5M token calibration dataset to improve conversational chat performance.

For accurate benchmarking, we built an evaluation framework to match the reported 5-shot MMLU scores of Llama 4 and Gemma 3. This allowed apples-to-apples comparisons between full-precision vs. Dynamic v2.0, QAT and standard iMatrix quants.

Dynamic v2.0 aims to minimize the performance gap between full-precision models and their quantized counterparts.

posted an update 4 months ago

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5116

You can now run Llama 4 on your own local device! 🦙
Run our Dynamic 1.78-bit and 2.71-bit Llama 4 GGUFs:
unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF

You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4

1 reply

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posted an update 4 months ago

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3608

You can now run DeepSeek-V3-0324 on your own local device!
Run our Dynamic 2.42 and 2.71-bit DeepSeek GGUFs: unsloth/DeepSeek-V3-0324-GGUF

You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-v3-0324-locally

posted an update 7 months ago

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3236

I uploaded DeepSeek R1 GGUFs!

unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF
unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF
2bit for MoE: unsloth/DeepSeek-R1-GGUF
unsloth/DeepSeek-R1-Zero-GGUF

More at unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5

posted an update 7 months ago

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4832

We fixed many bugs in Phi-4 & uploaded fixed GGUF + 4-bit versions! ✨

Our fixed versions are even higher on the Open LLM Leaderboard than Microsoft's!

GGUFs: unsloth/phi-4-GGUF
Dynamic 4-bit: unsloth/phi-4-unsloth-bnb-4bit

You can also now finetune Phi-4 for free on Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb

Read our blogpost for more details on bug fixes etc: https://unsloth.ai/blog/phi4

posted an update 7 months ago

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3305

Deepseek V3, including GGUF + bf16 versions are now uploaded!

Includes 2, 3, 4, 5, 6 and 8-bit quantized versions.

GGUFs: unsloth/DeepSeek-V3-GGUF
bf16: unsloth/DeepSeek-V3-bf16

Min. hardware requirements to run: 48GB RAM + 250GB of disk space for 2-bit.

See how to run them with examples and the full collection: unsloth/deepseek-v3-all-versions-677cf5cfd7df8b7815fc723c

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posted an update 8 months ago

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1633

I uploaded GGUFs, 4bit bitsandbytes and full 16bit precision weights for Llama 3.3 70B Instruct are here: unsloth/llama-33-all-versions-67535d7d994794b9d7cf5e9f

You can also finetune Llama 3.3 70B in under 48GB of VRAM with Unsloth!
GGUFs: unsloth/Llama-3.3-70B-Instruct-GGUF
BnB 4bit: unsloth/Llama-3.3-70B-Instruct-bnb-4bit
16bit: unsloth/Llama-3.3-70B-Instruct

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posted an update 9 months ago

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Vision finetuning is in 🦥Unsloth! You can now finetune Llama 3.2, Qwen2 VL, Pixtral and all Llava variants up to 2x faster and with up to 70% less VRAM usage! Colab to finetune Llama 3.2: https://colab.research.google.com/drive/1j0N4XTY1zXXy7mPAhOC1_gMYZ2F2EBlk?usp=sharing

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replied to louisbrulenaudet's post 11 months ago

Great work Louis!!

reacted to louisbrulenaudet's post with 🔥 11 months ago

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2626

The Romulus model series has been released on Hugging Face, continually pre-trained on 34,864,949 tokens of French laws and intended to serve as a foundation for fine-tuning on labeled data 🤗

The training code, dataset and model weights are open and available free on HF and the training was based on H100 provided by Microsoft for Startups using Unsloth AI by @danielhanchen and @shimmyshimmer 🦥

Link to the base model: louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1

Link to the instruct model: louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1-Instruct

Link to the dataset: louisbrulenaudet/Romulus-cpt-fr

Please note that these models have not been aligned for the production of usable texts as they stand, and will certainly need to be refined for the desired tasks in order to produce satisfactory results.

1 reply

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Daniel (Unsloth) PRO

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