Qwen3.6-rust
Collection
12 items • Updated • 1
How to use jedisct1/Qwen3.6-27B-rust-v1-4bit.mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3.6-27B-rust-v1-4bit.mlx jedisct1/Qwen3.6-27B-rust-v1-4bit.mlx
A Rust-focused Qwen3.6-27B model for Apple Silicon, packaged in MLX.
Use it as a coding assistant for Rust projects: generating focused patches, explaining diffs, tightening tests, reading command output, and making small repo-aware edits. It is tuned for tool-calling workflows where the assistant has to inspect files, run commands, and keep changes narrow.
This is the plain 4-bit OptIQ variant. This plain package does not include native MTP tensors. It is the safer choice for general MLX loaders and clients that do not yet recognize the Qwen3.6 dense MTP layout.
jedisct1/Qwen3.6-27B-rust-v1-8bit.mlx for the plain 8-bit sibling.jedisct1/Qwen3.6-27B-rust-v1-bf16.mlx for the plain BF16 full-precision sibling.jedisct1/Qwen3.6-27B-rust-v1-MTP-4bit.mlx if your runtime supports native MTP and you want the faster MTP path.Requires mlx-lm:
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("jedisct1/Qwen3.6-27B-rust-v1-4bit.mlx")
messages = [
{"role": "system", "content": "You are an expert Rust developer."},
{"role": "user", "content": "Generate a focused patch that replaces unwrap() calls in parse_config() with proper error propagation."},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=500)
print(response)
4-bit
Base model
Qwen/Qwen3.6-27B