Qwen3.6-golang
Collection
12 items • Updated
How to use jedisct1/Qwen3.6-27B-go-v1-MTP-bf16.mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3.6-27B-go-v1-MTP-bf16.mlx jedisct1/Qwen3.6-27B-go-v1-MTP-bf16.mlx
A Go-focused Qwen3.6-27B model for Apple Silicon, packaged in MLX.
Use it as a coding assistant for Go 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 native-MTP BF16 full-precision variant. This native-MTP package includes the model's MTP weights for runtimes that support the Qwen3.6 dense MTP layout, such as oMLX. If a loader rejects MTP weights, use the matching plain repo instead.
jedisct1/Qwen3.6-27B-go-v1-MTP-4bit.mlx for the native-MTP 4-bit OptIQ sibling.jedisct1/Qwen3.6-27B-go-v1-MTP-8bit.mlx for the native-MTP 8-bit sibling.jedisct1/Qwen3.6-27B-go-v1-bf16.mlx if your runtime does not support native MTP weights.Requires mlx-lm:
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("jedisct1/Qwen3.6-27B-go-v1-MTP-bf16.mlx")
messages = [
{"role": "system", "content": "You are an expert Go developer."},
{"role": "user", "content": "Generate a focused patch that replaces panic() in loadConfig() with returned errors and table-driven tests."},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=500)
print(response)
Quantized
Base model
Qwen/Qwen3.6-27B