Qwen3.6-35B-rust-v2-bf16.mlx

Qwen3.6-35B-rust-v2 BF16 MLX

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 BF16 full-precision compatibility variant. It does not include native MTP tensors, so it is useful when you want full precision but your loader does not support MTP sidecars.

Which Variant Should I Use?

• Use this repo if you want full precision without native MTP.
• Use jedisct1/Qwen3.6-35B-rust-v2-4bit.mlx if you want the smallest plain package.
• Use jedisct1/Qwen3.6-35B-rust-v2-8bit.mlx if you want a quantized plain package with more precision than 4-bit.
• Use jedisct1/Qwen3.6-35B-rust-v2-MTP-bf16.mlx if your runtime supports native MTP and you want full precision with the MTP path.

Usage

Requires mlx-lm:

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("jedisct1/Qwen3.6-35B-rust-v2-bf16.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)

What It Is Good At

• Writing idiomatic Rust patches from a concise change request.
• Explaining Rust diffs in commit-message style.
• Following tool-calling workflows where it needs to inspect files before editing.
• Keeping changes focused instead of turning small fixes into broad rewrites.
• Working with tests, compiler errors, command output, and repository context.

Limitations

• Outputs should be reviewed before use, especially unsafe code, concurrency code, and changes that affect security boundaries.
• The model works best on focused Rust changes, tests, and explanations. Very large refactors may need to be split into smaller steps.
• Tool calling depends on the runtime and client preserving the chat template and tool schema format.