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

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 / QUICKSTART.md

Tyler Williams

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

cc49567 6 months ago

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Wraith Coder 7B

Signal-dense code generation model fine-tuned from Qwen2.5-Coder-7B-Instruct.

Quick Start

Installation

pip install transformers torch

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "vanta-research/wraith-coder-7b",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("vanta-research/wraith-coder-7b")

messages = [
    {"role": "user", "content": "Implement binary search with complexity analysis."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Ollama Deployment

# Convert to GGUF (Q4_K_M recommended)
ollama create wraith-coder:7b -f Modelfile

# Run inference
ollama run wraith-coder:7b "Implement a LRU cache with O(1) operations"

Key Features

62.6% more concise than base Qwen2.5-Coder-7B while maintaining correctness
60% complexity analysis coverage across diverse coding challenges
Multiple solution approaches with trade-off discussions
Systems programming knowledge integrated throughout
Production-ready for senior engineering applications

Performance Highlights

Metric	Base Qwen	Wraith Coder	Improvement
Avg Response Length	2,900 chars	1,084 chars	62.6% shorter
Complexity Analysis	40%	60%	+50% coverage
Multiple Approaches	35%	65%	+86% frequency
Trade-off Discussion	45%	75%	+67% depth

Documentation

Full documentation available in README.md

License

Apache 2.0

Citation

@misc{wraith-coder-7b,
  author = {Vanta Research},
  title = {Wraith Coder 7B: Signal-Dense Code Generation through Iterative Fine-Tuning},
  year = {2025},
  publisher = {Hugging Face}
}