Instructions to use vanta-research/wraith-coder-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vanta-research/wraith-coder-7b with Transformers:

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

pipe = pipeline("text-generation", model="vanta-research/wraith-coder-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vanta-research/wraith-coder-7b")
model = AutoModelForCausalLM.from_pretrained("vanta-research/wraith-coder-7b")
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 Settings

vLLM

How to use vanta-research/wraith-coder-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vanta-research/wraith-coder-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": "vanta-research/wraith-coder-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vanta-research/wraith-coder-7b

SGLang

How to use vanta-research/wraith-coder-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 "vanta-research/wraith-coder-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": "vanta-research/wraith-coder-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 "vanta-research/wraith-coder-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": "vanta-research/wraith-coder-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vanta-research/wraith-coder-7b with Docker Model Runner:
```
docker model run hf.co/vanta-research/wraith-coder-7b
```

wraith-coder-7b / LICENSE.md

Tyler Williams

Initial commit: Wraith Coder 7B - Concise code assistant via iterative fine-tuning

cc49567 7 months ago

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	# License

	## Model License

	This model is licensed under the Qwen License Agreement as it is derived from Qwen2.5-Coder-7B-Instruct.

	The original Qwen2.5-Coder license permits:
	- Commercial use
	- Modification and derivative works
	- Distribution with attribution

	Full license text: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE

	## Training Data License

	Training datasets include:
	- Apollo V2.3 (various subsets)
	- Centauri coding datasets
	- Custom persona and reasoning datasets

	Dataset licenses vary by source. Users should review individual dataset licenses for compliance requirements.

	## Attribution

	When using this model, please cite:

	```bibtex
	@misc{wraith-coder-7b-2024,
	author = {Vanta},
	title = {Wraith Coder 7B: Concise Code Assistant via Iterative Fine-Tuning},
	year = {2024},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/vanta/wraith-coder-7b}}
	}
	```

	And cite the original Qwen2.5-Coder model:

	```bibtex
	@misc{qwen2.5-coder-2024,
	title={Qwen2.5-Coder Technical Report},
	author={Qwen Team},
	year={2024},
	publisher={Alibaba Cloud},
	howpublished={\url{https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct}}
	}
	```

	## Disclaimer

	This model is provided "as is" without warranties of any kind. Users are responsible for:
	- Validating outputs for production use
	- Ensuring compliance with applicable laws and regulations
	- Reviewing generated code for security vulnerabilities
	- Testing in appropriate environments before deployment

	The authors and contributors assume no liability for damages arising from model use.