Instructions to use aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft with Transformers:

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

pipe = pipeline("text-generation", model="aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft")
model = AutoModelForCausalLM.from_pretrained("aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft

SGLang

How to use aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft 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 "aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft" \
    --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": "aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft",
		"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 "aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft" \
        --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": "aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft with Docker Model Runner:
```
docker model run hf.co/aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft
```

qwen2-5-coder-7b-kernelbook-sdft / README.md

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Update model card with KernelBook post-training description

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metadata

library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - triton
  - kernelbook
  - code-generation
  - self-distillation
  - sdft
  - text-generation
datasets:
  - custom
language:
  - en
pipeline_tag: text-generation

Qwen2.5-Coder-7B KernelBook SDFT

Self-Distillation Fine-Tuning (SDFT) checkpoint of Qwen/Qwen2.5-Coder-7B-Instruct, post-trained on the KernelBook Triton kernel dataset.

Method

This model was trained with SDFT (self-distillation fine-tuning): the student sees the user prompt plus privileged reference context (the target Triton implementation) and learns to reproduce the reference completion via forced-completion distillation (cross-entropy + KL on completion tokens). Training used a custom KernelBookSDFTTrainer on top of transformers.Trainer with DeepSpeed ZeRO-3.

Dataset

KernelBook — PyTorch module prompts paired with reference Triton kernels
Deduplicated, filtered to completions ≤4096 tokens, repo-stratified 80/10/10 split
1 training epoch on the KernelBook train split

Intended use

Generate Triton GPU kernels from PyTorch-style module descriptions. Best for KernelBook-style conversion prompts; not evaluated as a general-purpose chat or reasoning model.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "aadityabuilds/qwen2-5-coder-7b-kernelbook-sdft"
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
)

messages = [
    {
        "role": "user",
        "content": "Convert the following PyTorch code to an equivalent Triton kernel...",
    }
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1200, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True))

Training summary

Setting	Value
Base model	Qwen2.5-Coder-7B-Instruct
Method	SDFT (forced-completion distillation)
Epochs	1
Hardware	4× H100 (Modal)
Parallelism	DeepSpeed ZeRO-3, bf16

Limitations

Specialized for KernelBook Triton codegen. May show reduced performance on general coding, math, and knowledge benchmarks compared to the base instruct model.