Instructions to use cortexso/mixtral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cortexso/mixtral with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/mixtral # Run inference directly in the terminal: llama cli -hf cortexso/mixtral
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/mixtral # Run inference directly in the terminal: llama cli -hf cortexso/mixtral
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/mixtral # Run inference directly in the terminal: ./llama-cli -hf cortexso/mixtral
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/mixtral # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/mixtral
Use Docker
docker model run hf.co/cortexso/mixtral
- LM Studio
- Jan
- Ollama
How to use cortexso/mixtral with Ollama:
ollama run hf.co/cortexso/mixtral
- Unsloth Studio
How to use cortexso/mixtral 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/mixtral 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/mixtral to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/mixtral to start chatting
- Atomic Chat new
- Docker Model Runner
How to use cortexso/mixtral with Docker Model Runner:
docker model run hf.co/cortexso/mixtral
- Lemonade
How to use cortexso/mixtral with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/mixtral
Run and chat with the model
lemonade run user.mixtral-{{QUANT_TAG}}List all available models
lemonade list
File size: 822 Bytes
89c334b 2ffcdb9 89c334b 6914a5b 89c334b 2ffcdb9 89c334b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ---
license: apache-2.0
---
## Overview
The Mixtral-7x8B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-7x8Boutperforms Llama 2 70B on most benchmarks we tested.
## Variants
| No | Variant | Cortex CLI command |
| --- | --- | --- |
| 1 | [7x8b-gguf](https://huggingface.co/cortexhub/mixtral/tree/7x8b-gguf) | `cortex run mixtral:7x8b-gguf` |
## Use it with Jan (UI)
1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
2. Use in Jan model Hub:
```
cortexhub/mixtral
```
## Use it with Cortex (CLI)
1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
2. Run the model with command:
```
cortex run mixtral
```
## Credits
- **Author:** Mistralai
- **Converter:** [Homebrew](https://www.homebrew.ltd/) |