Instructions to use mfxler/EuroLLM-1.7B-Instruct-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mfxler/EuroLLM-1.7B-Instruct-4bit-mlx 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("mfxler/EuroLLM-1.7B-Instruct-4bit-mlx") 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 mfxler/EuroLLM-1.7B-Instruct-4bit-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mfxler/EuroLLM-1.7B-Instruct-4bit-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mfxler/EuroLLM-1.7B-Instruct-4bit-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mfxler/EuroLLM-1.7B-Instruct-4bit-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- 66d7708c0c8b685f475de5168b408966642bbb73f99dc1b4a91c30a75b10b733
- Size of remote file:
- 15.8 MB
- SHA256:
- 0ceb7c0e31f64bb76a6ea109d59a9b9b706de432d1c7f23d0f99884990ee0664
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