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

File size: 1,315 Bytes

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{
  "model_name": "wraith-coder-7b",
  "base_model": "Qwen/Qwen2.5-Coder-7B-Instruct",
  "version": "1.0.0",
  "release_date": "2025-11-19",
  
  "architecture": {
    "type": "CausalLM",
    "parameters": "7.6B",
    "layers": 28,
    "hidden_size": 3584,
    "attention_heads": 28,
    "kv_heads": 4,
    "context_length": 32768,
    "vocab_size": 152064
  },
  
  "training": {
    "method": "LoRA Fine-tuning",
    "iterations": 3,
    "total_examples": 14244,
    "lora_rank": 16,
    "lora_alpha": 32,
    "learning_rate": 5e-5,
    "epochs_per_iteration": 2,
    "optimizer": "adamw_8bit"
  },
  
  "performance": {
    "conciseness_improvement": "62.6%",
    "complexity_analysis_coverage": "60%",
    "base_model_complexity_coverage": "40%",
    "evaluation_questions": 20,
    "correctness_rate": "100%"
  },
  
  "recommended_parameters": {
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 40,
    "repeat_penalty": 1.1,
    "max_tokens": 2048
  },
  
  "quantization": {
    "supported_formats": ["fp16", "q8_0", "q4_k_m", "q4_0"],
    "recommended": "q4_k_m",
    "model_size_q4_k_m": "4.4GB"
  },
  
  "license": "Apache-2.0",
  "languages": ["en"],
  "tags": [
    "code-generation",
    "algorithms",
    "systems-programming",
    "complexity-analysis",
    "qwen2.5",
    "fine-tuned"
  ]
}