Instructions to use kcxain/KernelZero-CUDA-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kcxain/KernelZero-CUDA-7B with Transformers:

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

pipe = pipeline("text-generation", model="kcxain/KernelZero-CUDA-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kcxain/KernelZero-CUDA-7B")
model = AutoModelForCausalLM.from_pretrained("kcxain/KernelZero-CUDA-7B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kcxain/KernelZero-CUDA-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kcxain/KernelZero-CUDA-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kcxain/KernelZero-CUDA-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kcxain/KernelZero-CUDA-7B

SGLang

How to use kcxain/KernelZero-CUDA-7B 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 "kcxain/KernelZero-CUDA-7B" \
    --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": "kcxain/KernelZero-CUDA-7B",
		"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 "kcxain/KernelZero-CUDA-7B" \
        --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": "kcxain/KernelZero-CUDA-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kcxain/KernelZero-CUDA-7B with Docker Model Runner:
```
docker model run hf.co/kcxain/KernelZero-CUDA-7B
```

KernelZero-CUDA-7B / README.md

kcxain

Link KernelZero paper

e45fb13 verified about 1 month ago

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---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
  - code
  - cuda
  - gpu-kernels
  - kernel-generation
  - kernelbench
  - arxiv:2609.33074
---
# KernelZero-CUDA-7B

KernelZero-CUDA-7B is the CUDA kernel generation checkpoint reported as the final KernelZero CUDA model. It is based on Qwen2.5-Coder-7B-Instruct and trained with the KernelZero pipeline.

## Paper

[KernelZero: Continual Co-Evolution of LLMs for Kernel Generation](https://arxiv.org/abs/2609.33074)

## KernelBench results

The paper reports the following results with 10 sampled candidates per problem.

| Benchmark | pass@1 | pass@5 | pass@10 | fast\_1@1 | fast\_1@10 | fast\_2@1 | fast\_2@10 |
|---|---:|---:|---:|---:|---:|---:|---:|
| Level 1 | 75.8 | 98.87 | 100.0 | 17.6 | 29.0 | 7.2 | 12.0 |
| Level 2 | 69.6 | 93.70 | 97.0 | 2.4 | 12.0 | 1.2 | 6.0 |

## Intended use

This checkpoint generates CUDA extensions from PyTorch modules. Use the same prompt template and validation environment as KernelZero for reproducible evaluation.

## Loading

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "kcxain/KernelZero-CUDA-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
```

## Checkpoint provenance

This release contains the merged Hugging Face inference weights from the final 80-step CUDA Coder followed by the 40-step continuation used for the paper's main CUDA result. Optimizer states and distributed training shards are excluded.

	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- code
	- cuda
	- gpu-kernels
	- kernel-generation
	- kernelbench
	- arxiv:2609.33074
	---

	# KernelZero-CUDA-7B

	KernelZero-CUDA-7B is the CUDA kernel generation checkpoint reported as the final KernelZero CUDA model. It is based on Qwen2.5-Coder-7B-Instruct and trained with the KernelZero pipeline.

	## Paper

	[KernelZero: Continual Co-Evolution of LLMs for Kernel Generation](https://arxiv.org/abs/2609.33074)

	## KernelBench results

	The paper reports the following results with 10 sampled candidates per problem.

	\| Benchmark \| pass@1 \| pass@5 \| pass@10 \| fast\_1@1 \| fast\_1@10 \| fast\_2@1 \| fast\_2@10 \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| Level 1 \| 75.8 \| 98.87 \| 100.0 \| 17.6 \| 29.0 \| 7.2 \| 12.0 \|
	\| Level 2 \| 69.6 \| 93.70 \| 97.0 \| 2.4 \| 12.0 \| 1.2 \| 6.0 \|

	## Intended use

	This checkpoint generates CUDA extensions from PyTorch modules. Use the same prompt template and validation environment as KernelZero for reproducible evaluation.

	## Loading

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "kcxain/KernelZero-CUDA-7B"
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype="auto",
	device_map="auto",
	trust_remote_code=True,
	)
	```

	## Checkpoint provenance

	This release contains the merged Hugging Face inference weights from the final 80-step CUDA Coder followed by the 40-step continuation used for the paper's main CUDA result. Optimizer states and distributed training shards are excluded.