Text Generation
MLX
Safetensors
English
Japanese
deepseek_v3
conversational
custom_code
4-bit precision
Instructions to use mlx-community/RAI-3.0-R1-VECTOR-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/RAI-3.0-R1-VECTOR-MLX-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/RAI-3.0-R1-VECTOR-MLX-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/RAI-3.0-R1-VECTOR-MLX-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/RAI-3.0-R1-VECTOR-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/RAI-3.0-R1-VECTOR-MLX-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/RAI-3.0-R1-VECTOR-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 490 Bytes
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language:
- en
- ja
tags:
- mlx
pipeline_tag: text-generation
library_name: mlx
license: apache-2.0
base_model:
- Local-Novel-LLM-project/RAI-3.0-R1-VECTOR
---
This model was converted to MLX format from [`Local-Novel-LLM-project/RAI-3.0-R1-VECTOR`](https://huggingface.co/Local-Novel-LLM-project/RAI-3.0-R1-VECTOR) using mlx-vlm version **0.4.1**.
Refer to the [original model card](https://huggingface.co/Local-Novel-LLM-project/RAI-3.0-R1-VECTOR) for more details on the model.
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