README.md

metadata

license: apache-2.0
base_model:
  - Qwen/Qwen3.6-35B-A3B
library_name: mlx
tags:
  - mlx
  - qwen3.6
  - qwen3_5_moe
  - apple-silicon
  - tool-calling
  - go

Qwen3.6-35B-go-v2 4-bit MLX

A Go-focused Qwen3.6-35B-A3B model for Apple Silicon, packaged in MLX.

Use it as a coding assistant for Go projects: generating focused patches, explaining diffs, tightening tests, reading tool outputs, and making small repo-aware edits. It was tested with Swival on file-editing and command-running 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.

Which Variant Should I Use?

• Use this repo if you want the smallest plain MLX package or need a loader-friendly non-MTP model.
• Use jedisct1/Qwen3.6-35B-go-v2-8bit.mlx if you want more precision without native MTP.
• Use jedisct1/Qwen3.6-35B-go-v2-bf16.mlx if you want full precision without native MTP.
• Use jedisct1/Qwen3.6-35B-go-v2-MTP-4bit.mlx if your runtime supports native MTP and you want the faster MTP path.

Usage

Requires mlx-lm:

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("jedisct1/Qwen3.6-35B-go-v2-4bit.mlx")

messages = [
    {"role": "system", "content": "You are an expert Go developer."},
    {"role": "user", "content": "Generate a focused patch that replaces the manual retry loop in fetchUser() with the shared retry helper."},
]
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 Go patches from a concise change request.
• Explaining Go 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, command output, and repository context.

Limitations

• Outputs should be reviewed before use, especially patches that touch production systems.
• The model works best on focused Go 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.