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

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 about 1 month ago

Commit

eac5c9b

verified ·

1 Parent(s): 7168e35

Upload train_grpo_job.py with huggingface_hub

Browse files

Files changed (1) hide show

train_grpo_job.py +10 -3

train_grpo_job.py CHANGED Viewed

@@ -191,7 +191,7 @@ def main():
         save_steps=50,
         save_total_limit=2,
         log_completions=True,
-        push_to_hub=True,
         hub_model_id=HUB_MODEL_ID,
         report_to="none",
         seed=42,
@@ -211,9 +211,16 @@ def main():
     logger.info("Saving model...")
     trainer.save_model(OUTPUT_DIR)
-    trainer.push_to_hub()
-    logger.info(f"✅ Done! Model pushed to https://huggingface.co/{HUB_MODEL_ID}")
 if __name__ == "__main__":

         save_steps=50,
         save_total_limit=2,
         log_completions=True,
+        push_to_hub=False,
         hub_model_id=HUB_MODEL_ID,
         report_to="none",
         seed=42,
     logger.info("Saving model...")
     trainer.save_model(OUTPUT_DIR)
+    # Manual push to hub using HF_TOKEN from environment
+    hf_token = os.environ.get("HF_TOKEN")
+    if hf_token:
+        logger.info(f"Pushing to hub: {HUB_MODEL_ID}")
+        trainer.model.push_to_hub(HUB_MODEL_ID, token=hf_token)
+        trainer.processing_class.push_to_hub(HUB_MODEL_ID, token=hf_token)
+        logger.info(f"✅ Done! Model pushed to https://huggingface.co/{HUB_MODEL_ID}")
+    else:
+        logger.info(f"✅ Done! Model saved to {OUTPUT_DIR} (no HF_TOKEN for push)")
 if __name__ == "__main__":