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jedisct1/Qwen3.6-35B-rust-v2-4bit.mlx
Qwen3.6-35B-rust-v2-4bit.mlx is a machine learning model from jedisct1. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as apache-2.0.
A Rust-focused Qwen3.6-35B-A3B model for Apple Silicon, packaged in MLX.
Downloads · 30 days
12
4% of all-time downloads
All-time downloads
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.safetensors23 GB · 100%
How the weights are stored.
U3234.7B · 100%
From the Hugging Face model README
A Rust-focused Qwen3.6-35B-A3B 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 was tested with Swival on local tool-calling workflows.
This is the plain 4-bit compatibility variant. It does not include native MTP tensors, so it is the best starting point if your MLX loader does not support MTP sidecars.
jedisct1/Qwen3.6-35B-rust-v2-8bit.mlx if you want more precision without native MTP.jedisct1/Qwen3.6-35B-rust-v2-bf16.mlx if you want full precision without native MTP.jedisct1/Qwen3.6-35B-rust-v2-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/Qwen3.6-35B-rust-v2-4bit.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)