Instructions to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF", filename="CohereForAI_c4ai-command-a-03-2025-IQ1_M.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 bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF: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 bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF: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 bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M
- Ollama
How to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with Ollama:
ollama run hf.co/bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M
- Unsloth Studio new
How to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF 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 bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF 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 bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF to start chatting
- Pi new
How to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF: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": "bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF: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 bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M
- Lemonade
How to use bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/CohereForAI_c4ai-command-a-03-2025-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.CohereForAI_c4ai-command-a-03-2025-GGUF-Q4_K_M
List all available models
lemonade list
Context size clarification
Hi! I saw you comment in https://huggingface.co/lmstudio-community/c4ai-command-a-03-2025-GGUF/discussions/1 about the context there. I assume the context window is correct (256k) in your quantized variants, right?
The default limit is set to 16k, cohere updated after the fact to 128k, but in any case you should be able to safely override it to whatever you'd like !
Thank you! Alas, we hoped for actual 256k (64k at least!). I was told that this model is a solid replacement for Mistral Large (finetunes even).
It should support the full 256k! You just need to set it at load time (lm studio should work, just have to edit the box to the value you want instead of using the slider)