Text Generation
MLX
Safetensors
Rust
English
qwen3
grammar
logic
rhetoric
math
programming
aarch64
c
nushell
conversational
4-bit precision
Instructions to use dougiefresh/jade_qwen3_4b_mlx_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dougiefresh/jade_qwen3_4b_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("dougiefresh/jade_qwen3_4b_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 dougiefresh/jade_qwen3_4b_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 "dougiefresh/jade_qwen3_4b_mlx_4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "dougiefresh/jade_qwen3_4b_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": "dougiefresh/jade_qwen3_4b_mlx_4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'

- Xet hash:
- d24d73fc0ed0237801df5914ecce629b24ee12f086b1b3b5942b9fc4d53f4fd1
- Size of remote file:
- 171 kB
- SHA256:
- 1ccdceafdb9b9de8070361c1422d5b2fe22f6b64894861e35f56f73f2edf8d4d
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