Instructions to use AscendKernelGen/KernelGen-LM-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AscendKernelGen/KernelGen-LM-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AscendKernelGen/KernelGen-LM-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AscendKernelGen/KernelGen-LM-8B")
model = AutoModelForCausalLM.from_pretrained("AscendKernelGen/KernelGen-LM-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AscendKernelGen/KernelGen-LM-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AscendKernelGen/KernelGen-LM-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AscendKernelGen/KernelGen-LM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AscendKernelGen/KernelGen-LM-8B

SGLang

How to use AscendKernelGen/KernelGen-LM-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AscendKernelGen/KernelGen-LM-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AscendKernelGen/KernelGen-LM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AscendKernelGen/KernelGen-LM-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AscendKernelGen/KernelGen-LM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AscendKernelGen/KernelGen-LM-8B with Docker Model Runner:
```
docker model run hf.co/AscendKernelGen/KernelGen-LM-8B
```

nielsr HF Staff commited on Apr 20

Commit

cdb3861

verified ·

1 Parent(s): 8e74169

Add model metadata and links to paper and code

Browse files

Hi! I'm Niels from the community science team at Hugging Face.

This PR improves the model card by adding relevant metadata and links:
- Added `pipeline_tag: text-generation`.
- Added `library_name: transformers` as the repository follows the standard Transformers structure.
- Added `license: apache-2.0` based on the project documentation.
- Linked the research paper to its arXiv page.
- Linked the official GitHub repository for the project.

Files changed (1) hide show

README.md +11 -5

README.md CHANGED Viewed

@@ -1,8 +1,11 @@
 ---
 language:
 - en
-...
 ---
 # AscendKernelGen/KernelGen-LM-8B
 ![License](https://img.shields.io/badge/License-Apache-yellow)
@@ -11,8 +14,9 @@ language:
 KernelGen-LM-8B is a state-of-the-art domain-adaptive large language model specialized for low-level NPU kernel generation, specifically for the Huawei Ascend architecture using the AscendC programming language. Built upon the Qwen3-8B backbone, it is trained on the Ascend-CoT dataset and refined via reinforcement learning with execution feedback.
 **Other artifacts:**
-* The **AscendKernelGen Technical Report** is published at https://arxiv.org/abs/2601.07160.
-* The **NPUKernelBench** evaluation framework is published at https://git.openi.org.cn/PCL-Benchmark/NPUKernelBench.
 ## Introduction
@@ -24,10 +28,12 @@ Our framework, **AscendKernelGen (AKGen)**, bridges the gap between general-purp
 * **Performance:** The model demonstrates siginificant improvement on complex Level-2 kernels compared to baselines, and effectively solving tasks where general-purpose models (like Qwen3, Llama3.1) fail completely.
 ## Citation
 @article{cao2026ascendkernelgen,
   title={AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units},
   author={Xinzi Cao and Jianyang Zhai and Pengfei Li and Zhiheng Hu and Cen Yan and Bingxu Mu and Guanghuan Fang and Bin She and Jiayu Li and Yihan Su and Dongyang Tao and Xiansong Huang and Fan Xu and Feidiao Yang and Yao Lu and Chang-Dong Wang and Yutong Lu and Weicheng Xue and Bin Zhou and Yonghong Tian},
   journal={arXiv preprint arXiv:2601.07160},
   year={2026},
-  url=https://arxiv.org/abs/2601.07160
-}

 ---
 language:
 - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 # AscendKernelGen/KernelGen-LM-8B
 ![License](https://img.shields.io/badge/License-Apache-yellow)
 KernelGen-LM-8B is a state-of-the-art domain-adaptive large language model specialized for low-level NPU kernel generation, specifically for the Huawei Ascend architecture using the AscendC programming language. Built upon the Qwen3-8B backbone, it is trained on the Ascend-CoT dataset and refined via reinforcement learning with execution feedback.
 **Other artifacts:**
+* The **AscendKernelGen Technical Report** is published at [https://arxiv.org/abs/2601.07160](https://arxiv.org/abs/2601.07160).
+* The **Official Code** and **NPUKernelBench** framework are available on GitHub: [https://github.com/weich97/NPUKernelBench](https://github.com/weich97/NPUKernelBench).
+* The **NPUKernelBench** evaluation framework is also published at [https://git.openi.org.cn/PCL-Benchmark/NPUKernelBench](https://git.openi.org.cn/PCL-Benchmark/NPUKernelBench).
 ## Introduction
 * **Performance:** The model demonstrates siginificant improvement on complex Level-2 kernels compared to baselines, and effectively solving tasks where general-purpose models (like Qwen3, Llama3.1) fail completely.
 ## Citation
+```bibtex
 @article{cao2026ascendkernelgen,
   title={AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units},
   author={Xinzi Cao and Jianyang Zhai and Pengfei Li and Zhiheng Hu and Cen Yan and Bingxu Mu and Guanghuan Fang and Bin She and Jiayu Li and Yihan Su and Dongyang Tao and Xiansong Huang and Fan Xu and Feidiao Yang and Yao Lu and Chang-Dong Wang and Yutong Lu and Weicheng Xue and Bin Zhou and Yonghong Tian},
   journal={arXiv preprint arXiv:2601.07160},
   year={2026},
+  url={https://arxiv.org/abs/2601.07160}
+}
+```