Instructions to use unsloth/mistral-7b-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/mistral-7b-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="unsloth/mistral-7b-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/mistral-7b-v0.2")
model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-v0.2")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use unsloth/mistral-7b-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/mistral-7b-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/mistral-7b-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/unsloth/mistral-7b-v0.2

SGLang

How to use unsloth/mistral-7b-v0.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "unsloth/mistral-7b-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/mistral-7b-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "unsloth/mistral-7b-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/mistral-7b-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use unsloth/mistral-7b-v0.2 with 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 unsloth/mistral-7b-v0.2 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 unsloth/mistral-7b-v0.2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/mistral-7b-v0.2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/mistral-7b-v0.2",
    max_seq_length=2048,
)

Docker Model Runner
How to use unsloth/mistral-7b-v0.2 with Docker Model Runner:
```
docker model run hf.co/unsloth/mistral-7b-v0.2
```

AQLM quantization version please

by AiModelsMarket - opened Mar 24, 2024

Discussion

AiModelsMarket

Mar 24, 2024

Hello, can you add an AQLM (https://arxiv.org/abs/2401.06118) quantization version of this model please ? And if possible a jupyter notebook to fine tune it faster ? Thank you! You are the best!

shimmyshimmer

Unsloth AI org Mar 25, 2024

Thank you we'll take a look! Regarding notebooks, have you tried using our Mistral 7b v2 notebook? Here: https://twitter.com/danielhanchen/status/1771737648266178801

And thanks for the kind words! ❤️

AiModelsMarket

Mar 25, 2024

Thanks for your answer! The speed of progress is really incredible. Just found out few days ago about AQLM 1 bit quantization and now it was discovered even better quantization of lower than 1 bit ... QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models https://huggingface.co/papers/2310.16795 . Do not get me wrong ...you are already are doing wonderful things . I just put here what I found so that maybe we advance faster and obtain very powerful models at very low computation costs and affordable for anyone . If you get a chance look at those new quantization methods and try to adapt it to your already great work . Thank you again !

shimmyshimmer

Unsloth AI org Mar 26, 2024

Yes thank you for your suggestions, we'll definitely be taking a look at doing more optimizations soon! If you have anymore advice/suggestions please send them our way! ^^

AiModelsMarket

Mar 26, 2024

LOL. I can't even have time to read all revolutionary progress that is done in this AI wonder field of science :P . Here is another interesting direction for high optimization : "Quantized Embeddings are here! Unlike model quantization, embedding quantization is a post-processing step for embeddings that converts e.g. float32 embeddings to binary or int8 embeddings. This saves 32x or 4x memory & disk space, and these embeddings are much easier to compare!

Results show 25-45x speedups in retrieval compared to full-size embeddings, while keeping 96% of the performance! " https://huggingface.co/blog/embedding-quantization

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