Instructions to use JinnP/Qwen3-8B-Kernelbook-SFT-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JinnP/Qwen3-8B-Kernelbook-SFT-HF with Transformers:

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

pipe = pipeline("text-generation", model="JinnP/Qwen3-8B-Kernelbook-SFT-HF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JinnP/Qwen3-8B-Kernelbook-SFT-HF")
model = AutoModelForCausalLM.from_pretrained("JinnP/Qwen3-8B-Kernelbook-SFT-HF")
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 JinnP/Qwen3-8B-Kernelbook-SFT-HF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JinnP/Qwen3-8B-Kernelbook-SFT-HF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JinnP/Qwen3-8B-Kernelbook-SFT-HF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JinnP/Qwen3-8B-Kernelbook-SFT-HF

SGLang

How to use JinnP/Qwen3-8B-Kernelbook-SFT-HF 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 "JinnP/Qwen3-8B-Kernelbook-SFT-HF" \
    --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": "JinnP/Qwen3-8B-Kernelbook-SFT-HF",
		"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 "JinnP/Qwen3-8B-Kernelbook-SFT-HF" \
        --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": "JinnP/Qwen3-8B-Kernelbook-SFT-HF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JinnP/Qwen3-8B-Kernelbook-SFT-HF with Docker Model Runner:
```
docker model run hf.co/JinnP/Qwen3-8B-Kernelbook-SFT-HF
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-8B-Kernelbook-SFT (HuggingFace Format)

This is a fine-tuned version of Qwen3-8B using Supervised Fine-Tuning (SFT) on the filtered KernelBook dataset, optimized for kernel and system-level tasks.

Model Details

Base Model: Qwen3-8B
Training Method: Supervised Fine-Tuning (SFT)
Training Framework: SLIME (Megatron-LM based)
Training Data: Filtered KernelBook dataset (10,000 high-quality samples)
Model Size: 8.2B parameters
Format: HuggingFace Transformers compatible
Checkpoint: Iteration 515

Repository Links

This Repository: HuggingFace format - ready for inference with Transformers, vLLM, SGLang, etc.
Megatron Format: JinnP/Qwen3-8B-Kernelbook-SFT-filtered - for continued training with Megatron-LM

Usage

Quick Start with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "JinnP/Qwen3-8B-Kernelbook-SFT-HF"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Example usage
prompt = "Explain how the Linux kernel handles memory management:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=500)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Using with vLLM

from vllm import LLM, SamplingParams

llm = LLM(model="JinnP/Qwen3-8B-Kernelbook-SFT-HF")
sampling_params = SamplingParams(temperature=0.7, max_tokens=500)

prompts = ["Describe the process scheduling algorithm in Linux kernel"]
outputs = llm.generate(prompts, sampling_params)

Training Details

This model was fine-tuned using the SLIME framework on filtered KernelBook data specifically curated for kernel and system programming tasks. The training focused on:

Kernel internals and system calls
Memory management and process scheduling
Device drivers and I/O systems
File systems and networking stack
Performance optimization and debugging

Model Performance

The fine-tuned model shows improved performance on:

Kernel code generation and explanation
System-level debugging scenarios
Performance optimization recommendations
Operating system concept explanations

License

This model inherits the Apache 2.0 license from the base Qwen3-8B model. Please refer to the original Qwen3 license for usage terms.

Citation

If you use this model, please cite:

@misc{qwen3-kernelbook-sft,
  title={Qwen3-8B-Kernelbook-SFT: Fine-tuned for Kernel and System Programming},
  author={JinnP},
  year={2024},
  publisher={HuggingFace}
}

And the original Qwen3 model:

@article{qwen3,
  title={Qwen3 Technical Report},
  author={Qwen Team},
  year={2024}
}

Acknowledgments

Base model: Qwen Team for Qwen3-8B
Training data: KernelBook dataset
Training framework: SLIME (Megatron-LM based)

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for JinnP/Qwen3-8B-Kernelbook-SFT-HF

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Quantizations

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