Instructions to use jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jedisct1/Qwen3-Coder-30B-A3B-Instruct-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("jedisct1/Qwen3-Coder-30B-A3B-Instruct-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
- LM Studio
- Unsloth Studio new
How to use jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx", max_seq_length=2048, ) - Pi new
How to use jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx
Run Hermes
hermes
- MLX LM
How to use jedisct1/Qwen3-Coder-30B-A3B-Instruct-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 "jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx
This model jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx was converted to MLX format from unsloth/Qwen3-Coder-30B-A3B-Instruct using mlx-lm version 0.26.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
31B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
Model tree for jedisct1/Qwen3-Coder-30B-A3B-Instruct-mlx
Base model
Qwen/Qwen3-Coder-30B-A3B-Instruct Finetuned
unsloth/Qwen3-Coder-30B-A3B-Instruct