Instructions to use itod/mistral-7B-instruct-v0.2-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itod/mistral-7B-instruct-v0.2-q8 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="itod/mistral-7B-instruct-v0.2-q8",
	filename="mistral-7B-instruct-v0.2-q8.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use itod/mistral-7B-instruct-v0.2-q8 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf itod/mistral-7B-instruct-v0.2-q8
# Run inference directly in the terminal:
llama-cli -hf itod/mistral-7B-instruct-v0.2-q8

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf itod/mistral-7B-instruct-v0.2-q8
# Run inference directly in the terminal:
llama-cli -hf itod/mistral-7B-instruct-v0.2-q8

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf itod/mistral-7B-instruct-v0.2-q8
# Run inference directly in the terminal:
./llama-cli -hf itod/mistral-7B-instruct-v0.2-q8

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf itod/mistral-7B-instruct-v0.2-q8
# Run inference directly in the terminal:
./build/bin/llama-cli -hf itod/mistral-7B-instruct-v0.2-q8

Use Docker

docker model run hf.co/itod/mistral-7B-instruct-v0.2-q8

LM Studio
Jan
Ollama
How to use itod/mistral-7B-instruct-v0.2-q8 with Ollama:
```
ollama run hf.co/itod/mistral-7B-instruct-v0.2-q8
```

Unsloth Studio new

How to use itod/mistral-7B-instruct-v0.2-q8 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 itod/mistral-7B-instruct-v0.2-q8 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 itod/mistral-7B-instruct-v0.2-q8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for itod/mistral-7B-instruct-v0.2-q8 to start chatting

Docker Model Runner
How to use itod/mistral-7B-instruct-v0.2-q8 with Docker Model Runner:
```
docker model run hf.co/itod/mistral-7B-instruct-v0.2-q8
```

Lemonade

How to use itod/mistral-7B-instruct-v0.2-q8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull itod/mistral-7B-instruct-v0.2-q8

Run and chat with the model

lemonade run user.mistral-7B-instruct-v0.2-q8-{{QUANT_TAG}}

List all available models

lemonade list

Source Mistral 7B model:
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/
This model is converted from Bfloat16 datatype to Int8 datatype with convert tool from:
https://github.com/ggerganov/llama.cpp
Deployment on CPU:
Pull the ready-made llama.cpp container:

docker pull ghcr.io/ggerganov/llama.cpp:server

Assuming mistral-7B-instruct-v0.2-q8.gguf file is downloaded to /path/to/models directory on local machine, run the container accesing the model with:

docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/istral-7B-instruct-v0.2-q8.gguf --port 8000 --host 0.0.0.0 -n 512

Test the deployment accessing the model with the browser at http://localhost:8000
llama.cpp server also provides OpenAI compatible API
Deployment on CUDA GPU:

docker pull ghcr.io/ggerganov/llama.cpp:server-cuda

docker run --gpus all -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server-cuda -m /models/mistral-7B-instruct-v0.2-q8.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 50

If CUDA GPU with 16GB RAM is available, the version of the model converted to float16 may be interesting, available in this repo:
https://huggingface.co/itod/mistral-7B-instruct-v0.2-f16
More details about usage is avalable in llama.cpp documentation:
https://github.com/ggerganov/llama.cpp/tree/master/examples/server

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

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