Instructions to use jackysnake/RedCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jackysnake/RedCoder with Transformers:

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

pipe = pipeline("text-generation", model="jackysnake/RedCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jackysnake/RedCoder")
model = AutoModelForCausalLM.from_pretrained("jackysnake/RedCoder")
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 jackysnake/RedCoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jackysnake/RedCoder

SGLang

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

Docker Model Runner
How to use jackysnake/RedCoder with Docker Model Runner:
```
docker model run hf.co/jackysnake/RedCoder
```

REDCODER: Automated Multi-Turn Red Teaming for Code LLMs

🔬 A model fine-tuned for adversarial multi-turn prompt generation to induce vulnerabilities in Code LLMs.
📄 [arXiv:2507.22063] • 🧠 💻 Full code & data: GitHub – luka-group/RedCoder

🧠 Model Summary

REDCODER is a red-teaming LLM trained to engage target Code LLMs in multi-turn conversations that gradually steer them into generating CWE vulnerabilities (e.g., Such as path traversal, SQL injection, etc.).

This model is designed to support:

⚔️ Red-teaming evaluations for Code LLMs
🧪 Security benchmarking of model guardrails and filters
🧩 Multi-turn adversarial prompt generation in research settings

⚠️ This model should not be used to generate real-world exploits. Its intended use is for research, safety evaluation, and secure LLM development.

If you find this work useful, please cite:

@article{mo2025redcoder,
  title   = {REDCODER: Automated Multi-Turn Red Teaming for Code LLMs},
  author  = {Wenjie Jacky Mo and Qin Liu and Xiaofei Wen and Dongwon Jung and
             Hadi Askari and Wenxuan Zhou and Zhe Zhao and Muhao Chen},
  journal = {arXiv preprint arXiv:2507.22063},
  year    = {2025}
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for jackysnake/RedCoder

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

(1080)

this model

Paper for jackysnake/RedCoder

RedCoder: Automated Multi-Turn Red Teaming for Code LLMs

Paper • 2507.22063 • Published Jun 25, 2025 • 2