Instructions to use cortexso/mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortexso/mistral with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cortexso/mistral",
	filename="mistral-7b-instruct-v0.3-q2_k.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cortexso/mistral with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/mistral:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/mistral:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/mistral:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/mistral:Q4_K_M

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 cortexso/mistral:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/mistral:Q4_K_M

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 cortexso/mistral:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/mistral:Q4_K_M

Use Docker

docker model run hf.co/cortexso/mistral:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/mistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/mistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortexso/mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortexso/mistral:Q4_K_M

Ollama
How to use cortexso/mistral with Ollama:
```
ollama run hf.co/cortexso/mistral:Q4_K_M
```

Unsloth Studio

How to use cortexso/mistral 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 cortexso/mistral 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 cortexso/mistral to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cortexso/mistral to start chatting

How to use cortexso/mistral with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cortexso/mistral:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "cortexso/mistral:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use cortexso/mistral with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cortexso/mistral:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default cortexso/mistral:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use cortexso/mistral with Docker Model Runner:
```
docker model run hf.co/cortexso/mistral:Q4_K_M
```

Lemonade

How to use cortexso/mistral with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/mistral:Q4_K_M

Run and chat with the model

lemonade run user.mistral-Q4_K_M

List all available models

lemonade list

mistral / README.md

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	---
	license: other
	---

	## Overview

	Mistral 7B, a 7-billion-parameter Large Language Model by Mistral AI. Designed for efficiency and performance, it suits real-time applications requiring swift responses.

	## Variants

	\| No \| Variant \| Cortex CLI command \|
	\| --- \| --- \| --- \|
	\| 1 \| [7b-gguf](https://huggingface.co/cortexhub/mistral/tree/7b-gguf) \| `cortex run mistral:7b-gguf` \|
	\| 2 \| [tensorrt-llm](https://huggingface.co/cortexso/mistral/tree/tensorrt-llm-windows-ada) \| `cortex run mistral:tensorrt-llm` \|
	\| 3 \| [7b-onnx](https://huggingface.co/cortexso/mistral/tree/7b-onnx) \| `cortex run mistral:7b-onnx` \|
	\| 4 \| [7b-v0.3](https://huggingface.co/cortexso/mistral/tree/7b-v0.3-gguf-q4-km) \| `cortex run mistral:7b-v0.3-gguf-q4-km` \|
	\| 4 \| [7b-small](https://huggingface.co/cortexso/mistral/tree/small-gguf-q4-km) \| `cortex run mistral:small-gguf-q4-km \|

	## Use it with Jan (UI)

	1. Install Jan using [Quickstart](https://jan.ai/docs/quickstart)
	2. Use in Jan model Hub:
	```
	cortexhub/mistral
	```

	## Use it with Cortex (CLI)

	1. Install Cortex using [Quickstart](https://cortex.jan.ai/docs/quickstart)
	2. Run the model with command:
	```
	cortex run mistral
	```

	## Credits

	- Author: MistralAI
	- Converter: [Homebrew](https://www.homebrew.ltd/)
	- Original License: [Licence]((https://mistral.ai/licenses/MNPL-0.1.md))
	- Papers: [Mistral paper](https://arxiv.org/abs/2310.06825)