Qwen3.6-35B-A3B-sft-15020

Qwen3.6-35B-A3B-sft-15020

Qwen3.6-35B-A3B-sft-15020 is a code generation model obtained by supervised fine-tuning (SFT) of Qwen/Qwen3.6-35B-A3B, specialized for NPU (Ascend) operator kernel generation. Given an operator's functional description and interface definition, the model generates kernel implementations (e.g., AscendC) that run on Ascend NPUs.

Model Summary

Quick Start

The model shares the same architecture as Qwen3.6-35B-A3B and can be loaded directly with transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen3.6-35B-A3B-sft-15020"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Implement an AscendC operator that performs element-wise addition of two input tensors."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

The model can also be served with inference frameworks such as vLLM or SGLang, following the same usage as Qwen3.6-35B-A3B.

Evaluation

The model is evaluated on NPUKernelBench, a benchmark for NPU kernel code generation that measures the correctness (compilation success and numerical accuracy) and performance of generated operator kernels.

Training Details

• Base model: Qwen/Qwen3.6-35B-A3B
• Fine-tuning method: SFT (full-parameter)
• Training framework: MindSpeed-MM, a distributed training acceleration framework for multimodal large models on Ascend NPUs
• Training data: TBD

Limitations

• This model is specialized for NPU kernel code generation; general conversational or general-purpose coding capabilities may be weaker than the base model.
• Generated kernel code should be compiled and numerically validated on the target hardware before production use. The model does not guarantee correctness in all scenarios.
• By using this model, you agree to comply with the Apache-2.0 license and the license terms of the base model Qwen3.6-35B-A3B.

Citation

@misc{wiki_model,
  title  = {wiki_model: SFT for NPU Kernel Generation},
  author = {<your-name-or-org>},
  year   = {2026},
  url    = {https://huggingface.co/<your-namespace>/wiki_model}
}