unsloth/DeepSeek-V3-GGUF
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Unsloth AI
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danielhanchen
updated
a
model
29 minutes ago
danielhanchen
updated
a
model
about 3 hours ago
danielhanchen
posted
an
update
about 1 month ago
Post
1471
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
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
- 1 reply
danielhanchen
posted
an
update
about 2 months ago
Post
1391
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
- 1 reply
danielhanchen
posted
an
update
9 months ago
Post
3691
Yay we got 500K+ monthly HF downloads on our Unsloth HF repo! :) Super appreciate everyone in the OSS community - and thanks for using Unsloth!!
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- 4 replies
danielhanchen
posted
an
update
11 months ago
Post
Gemma QLoRA finetuning is now 2.4x faster and uses 58% less VRAM than FA2 through 🦥Unsloth! Had to rewrite our Cross Entropy Loss kernels to work on all vocab sizes, re-design our manual autograd engine to accept all activation functions, and more! I wrote all about our learnings in our blog post: https://unsloth.ai/blog/gemma.
Also have a Colab notebook with no OOMs, and has 2x faster inference for Gemma & how to merge and save to llama.cpp GGUF & vLLM: https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing
And uploaded 4bit pre-quantized versions for Gemma 2b and 7b: unsloth/gemma-7b-bnb-4bit unsloth/gemma-2b-bnb-4bit
Also have a Colab notebook with no OOMs, and has 2x faster inference for Gemma & how to merge and save to llama.cpp GGUF & vLLM: https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing
And uploaded 4bit pre-quantized versions for Gemma 2b and 7b: unsloth/gemma-7b-bnb-4bit unsloth/gemma-2b-bnb-4bit
from unsloth import FastLanguageModel
model, tokenzer = FastLanguageModel.from_pretrained("unsloth/gemma-7b")
model = FastLanguageModel.get_peft_model(model)-
- 4 replies