How to use from
Unsloth Studio
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Alignment-Lab-AI/Mistral-Large-Instruct-2407-bnb-4bit to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Alignment-Lab-AI/Mistral-Large-Instruct-2407-bnb-4bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Alignment-Lab-AI/Mistral-Large-Instruct-2407-bnb-4bit to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Alignment-Lab-AI/Mistral-Large-Instruct-2407-bnb-4bit",
    max_seq_length=2048,
)
Quick Links

Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Mistral Nemo 12b (8B) here: https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports Free Notebooks Performance Memory use
Llama-3 8b ▶️ Start on Colab 2.4x faster 58% less
Gemma 7b ▶️ Start on Colab 2.4x faster 58% less
Mistral 7b ▶️ Start on Colab 2.2x faster 62% less
Llama-2 7b ▶️ Start on Colab 2.2x faster 43% less
TinyLlama ▶️ Start on Colab 3.9x faster 74% less
CodeLlama 34b A100 ▶️ Start on Colab 1.9x faster 27% less
Mistral 7b 1xT4 ▶️ Start on Kaggle 5x faster* 62% less
DPO - Zephyr ▶️ Start on Colab 1.9x faster 19% less
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Safetensors
Model size
126B params
Tensor type
F32
·
BF16
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U8
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