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 new

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

Pi new

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

Pham commited on Jun 20, 2024

Commit

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+---
+license: ms-pl
+---
+## Overview
+Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
+## Variants
+| No | Variant | Cortex CLI command |
+| --- | --- | --- |
+| 1 | [7B-gguf](https://huggingface.co/cortexhub/mistral/tree/7B-gguf) | `cortex run mistral:7B-gguf` |
+## Use it with Jan (UI)
+1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
+2. Use `cortexhub/mistral` in Jan model Hub
+## Use it with Cortex (CLI)
+1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
+2. Run the model with the command: `cortex run mistral`
+## Credits
+- **Author:** meta-llama
+- **Converter:** [Homebrew](https://www.homebrew.ltd/)
+- **Original License:** [Licence](https://llama.meta.com/mistral/license/)
+- **Papers:** N/A