Instructions to use jaithree/MN-12B-Mag-Mell-R1-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jaithree/MN-12B-Mag-Mell-R1-OptiQ-4bit 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("jaithree/MN-12B-Mag-Mell-R1-OptiQ-4bit") 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 jaithree/MN-12B-Mag-Mell-R1-OptiQ-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "jaithree/MN-12B-Mag-Mell-R1-OptiQ-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "jaithree/MN-12B-Mag-Mell-R1-OptiQ-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jaithree/MN-12B-Mag-Mell-R1-OptiQ-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 321 Bytes
9f5657f | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<s>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"is_local": true,
"local_files_only": false,
"model_max_length": 1000000000000000019884624838656,
"tokenizer_class": "TokenizersBackend",
"unk_token": "<unk>"
}
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