Instructions to use mlx-community/Llama-3-Karamaru-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Llama-3-Karamaru-v1 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3-Karamaru-v1") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/Llama-3-Karamaru-v1 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Llama-3-Karamaru-v1"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Llama-3-Karamaru-v1" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Llama-3-Karamaru-v1", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- 4f166dd2ddaf43bf445f6c3b5db260dd4f73f9b4f43b2b29bc62962f293ea7c7
- Size of remote file:
- 5.35 GB
- SHA256:
- bd9ff974f31b9c3937d6c56d29e18b6635cddb75470597508559f2a10a91cc0f
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