Instructions to use flock-io/Mistral-7B-CrewAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use flock-io/Mistral-7B-CrewAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="flock-io/Mistral-7B-CrewAI", filename="ggml-model-f16-Q4_K_M.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 flock-io/Mistral-7B-CrewAI with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf flock-io/Mistral-7B-CrewAI:Q4_K_M # Run inference directly in the terminal: llama-cli -hf flock-io/Mistral-7B-CrewAI:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf flock-io/Mistral-7B-CrewAI:Q4_K_M # Run inference directly in the terminal: llama-cli -hf flock-io/Mistral-7B-CrewAI: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 flock-io/Mistral-7B-CrewAI:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf flock-io/Mistral-7B-CrewAI: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 flock-io/Mistral-7B-CrewAI:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf flock-io/Mistral-7B-CrewAI:Q4_K_M
Use Docker
docker model run hf.co/flock-io/Mistral-7B-CrewAI:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use flock-io/Mistral-7B-CrewAI with Ollama:
ollama run hf.co/flock-io/Mistral-7B-CrewAI:Q4_K_M
- Unsloth Studio new
How to use flock-io/Mistral-7B-CrewAI 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 flock-io/Mistral-7B-CrewAI 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 flock-io/Mistral-7B-CrewAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for flock-io/Mistral-7B-CrewAI to start chatting
- Docker Model Runner
How to use flock-io/Mistral-7B-CrewAI with Docker Model Runner:
docker model run hf.co/flock-io/Mistral-7B-CrewAI:Q4_K_M
- Lemonade
How to use flock-io/Mistral-7B-CrewAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull flock-io/Mistral-7B-CrewAI:Q4_K_M
Run and chat with the model
lemonade run user.Mistral-7B-CrewAI-Q4_K_M
List all available models
lemonade list
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Check out the documentation for more information.
Mistral-7B-CrewAI Model
This repository provides the capability to invoke the model locally. For detailed usage, you can refer to the GitHub repository linked below.
Repository Link
Visit the GitHub repository for more details on how to use this model locally.
How to Use
To use this model, follow the instructions provided in the GitHub repository. It includes steps to set up your environment, load the model, and make predictions.
Support
If you encounter any issues while using this model, please open an issue in the GitHub repository for support.
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docker model run hf.co/flock-io/Mistral-7B-CrewAI: