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

Libraries

How to use sahilpwr/CodeVulnerabilityDPOModel with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sahilpwr/CodeVulnerabilityDPOModel")
model = AutoModelForCausalLM.from_pretrained("sahilpwr/CodeVulnerabilityDPOModel")
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 sahilpwr/CodeVulnerabilityDPOModel with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sahilpwr/CodeVulnerabilityDPOModel

SGLang

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

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

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Model Card for Model ID

The existing LLaMA models are not optimized for detecting critical security vulnerabilities or inefficient memory management within code.

In contrast, CodeVulnerabilityModel has been specially trained on a curated dataset of 5,000 samples from JetBrains, enabling it to accurately identify security flaws and memory management issues in source code.

This model is further refined through Direct Preference Optimization (DPO), aligning outputs more closely with human judgment using datasets that include rejected samples for better correction. Compared to traditional PPO-based RLHF approaches, DPO fine-tuning demonstrates superior precision in reducing false positives, while also enhancing both the detection of vulnerabilities and the quality of proposed fixes.

DPO is much simpler approach to align the response with human preference compared to PPO and RLHF set up

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Sahil Pawar
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Kubernetes + Ray Distributed Training 2 A100 GPUs

Time to Train: ~2hrs

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Evaluation

Llama 3B Base Model vs Llama 3B Fine Tuned with Direct Prefernce Optimization in Post Training

Note: I found out that BLEU and ROUGE are not ideal metrics for code evaluation since there can be multiple valid approaches to solving the same problem. This is why the overall scores appear lower for both models.

On the other hand, BERT measures semantic similarity using cosine similarity, making it a more reliable metric for accurate evaluation.

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

Ray + Kubernetes A100 GPUs

Citation [optional]

Glossary [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

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Safetensors

Model size

3B params

Tensor type

F32

Model tree for sahilpwr/CodeVulnerabilityDPOModel

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(2733)

this model

Dataset used to train sahilpwr/CodeVulnerabilityDPOModel

Paper for sahilpwr/CodeVulnerabilityDPOModel

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 66