Instructions to use akshatkot/linus-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use akshatkot/linus-coder with Transformers:

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

pipe = pipeline("text-generation", model="akshatkot/linus-coder")
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("akshatkot/linus-coder")
model = AutoModelForCausalLM.from_pretrained("akshatkot/linus-coder", 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 akshatkot/linus-coder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/akshatkot/linus-coder

SGLang

How to use akshatkot/linus-coder 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 "akshatkot/linus-coder" \
    --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": "akshatkot/linus-coder",
		"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 "akshatkot/linus-coder" \
        --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": "akshatkot/linus-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use akshatkot/linus-coder with Docker Model Runner:
```
docker model run hf.co/akshatkot/linus-coder
```

linus-coder

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---
title: Linus Coder
language:
  - en
license: mit
library_name: transformers
tags:
  - text-generation
  - lora
  - qwen2.5-coder
  - code-review
  - kernel-patching
datasets:
  - linus-torvalds-reviews
base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
---

# Linus Coder

A Qwen2.5-Coder-7B model fine-tuned with LoRA to review Linux kernel patches in the blunt, deeply technical style of Linus Torvalds.

## Intended Use

Feed a patch diff and get a Linus-style review ending with `Verdict: <approve|request_changes|reject|question>`.

## Input format

```
Reviewer style target: linus-torvalds
Subsystem: <subsystem>
Patch subject: <subject>

<patch diff>

Write your review of this patch.
End your review with exactly: Verdict: <approve|request_changes|reject|question>
```

## Usage (merged model)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("akshatkot/linus-coder")
model = AutoModelForCausalLM.from_pretrained("akshatkot/linus-coder", torch_dtype=torch.float16)
```

## Training

Fine-tuned with Unsloth SFTTrainer, 2 epochs, lr=1e-4, cosine schedule on LKML patch reviews.

## License

MIT