Instructions to use oxdev/security-auditor-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oxdev/security-auditor-grpo with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oxdev/security-auditor-grpo")
model = AutoModelForCausalLM.from_pretrained("oxdev/security-auditor-grpo")
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 oxdev/security-auditor-grpo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/oxdev/security-auditor-grpo

SGLang

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

Docker Model Runner
How to use oxdev/security-auditor-grpo with Docker Model Runner:
```
docker model run hf.co/oxdev/security-auditor-grpo
```

oxdev commited on Apr 25

Commit

7168e35

verified ·

1 Parent(s): 74022f8

Upload train_grpo_job.py with huggingface_hub

Browse files

Files changed (1) hide show

train_grpo_job.py +2 -2

train_grpo_job.py CHANGED Viewed

@@ -174,9 +174,9 @@ def main():
     config = GRPOConfig(
         output_dir=OUTPUT_DIR,
         num_train_epochs=2,
-        per_device_train_batch_size=2,
         num_generations=4,
-        max_completion_length=1536,
         learning_rate=5e-7,
         beta=0.0,
         scale_rewards=True,

     config = GRPOConfig(
         output_dir=OUTPUT_DIR,
         num_train_epochs=2,
+        per_device_train_batch_size=4,
         num_generations=4,
+        max_completion_length=1024,
         learning_rate=5e-7,
         beta=0.0,
         scale_rewards=True,