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 / README.md

jackysnake

Update README.md

5262ef0 verified 10 months ago

preview code

raw

history blame contribute delete

1.73 kB

	---
	library_name: transformers
	license: other
	base_model: meta-llama/Meta-Llama-3-8B-Instruct
	tags:
	- llama-factory
	- full
	- generated_from_trainer
	model-index:
	- name: sft
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# 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](https://arxiv.org/pdf/2507.22063)] • 🧠
	> 💻 Full code & data: [GitHub – luka-group/RedCoder](https://github.com/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:

	```bibtex
	@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}
	}
	```