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", 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=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 / .gitignore

Tyler Williams

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

cc49567 9 months ago

Raw

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439 Bytes

	# Python
	__pycache__/
	*.py[cod]
	*$py.class
	*.so
	.Python
	build/
	develop-eggs/
	dist/
	downloads/
	eggs/
	.eggs/
	lib/
	lib64/
	parts/
	sdist/
	var/
	wheels/
	pip-wheel-metadata/
	share/python-wheels/
	*.egg-info/
	.installed.cfg
	*.egg
	MANIFEST

	# Virtual environments
	venv/
	ENV/
	env/
	.venv

	# Training artifacts
	checkpoints/
	logs/
	runs/
	wandb/
	*.log

	# IDE
	.vscode/
	.idea/
	*.swp
	*.swo
	*~

	# OS
	.DS_Store
	Thumbs.db

	# Temporary files
	*.tmp
	*.bak
	*.backup