Text Generation
MLX
Safetensors
ouro
looped-language-model
reasoning
recurrent-depth
conversational
custom_code
4-bit precision
Instructions to use mlx-community/Ouro-1.4B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Ouro-1.4B-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("mlx-community/Ouro-1.4B-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 mlx-community/Ouro-1.4B-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 "mlx-community/Ouro-1.4B-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Ouro-1.4B-4bit" # 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/Ouro-1.4B-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
File size: 503 Bytes
37fa953 | 1 | {%- if messages[0]['role'] == 'system' -%}{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}{%- else -%}{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{%- endif -%}{%- for message in messages -%}{%- if message.role == 'system' and loop.first -%}{# Skip #}{%- else -%}{{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<|im_start|>assistant\n' }}{%- endif -%} |