Instructions to use oke39/llama3-8b-secure-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oke39/llama3-8b-secure-code with Transformers:

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

pipe = pipeline("text-generation", model="oke39/llama3-8b-secure-code")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oke39/llama3-8b-secure-code")
model = AutoModelForCausalLM.from_pretrained("oke39/llama3-8b-secure-code", device_map="auto")
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 oke39/llama3-8b-secure-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oke39/llama3-8b-secure-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oke39/llama3-8b-secure-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/oke39/llama3-8b-secure-code

SGLang

How to use oke39/llama3-8b-secure-code 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 "oke39/llama3-8b-secure-code" \
    --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": "oke39/llama3-8b-secure-code",
		"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 "oke39/llama3-8b-secure-code" \
        --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": "oke39/llama3-8b-secure-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use oke39/llama3-8b-secure-code with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for oke39/llama3-8b-secure-code to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for oke39/llama3-8b-secure-code to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for oke39/llama3-8b-secure-code to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="oke39/llama3-8b-secure-code",
    max_seq_length=2048,
)

Docker Model Runner
How to use oke39/llama3-8b-secure-code with Docker Model Runner:
```
docker model run hf.co/oke39/llama3-8b-secure-code
```

llama3-8b-secure-code / README.md

oke39

Update README.md

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	---
	base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- llama
	- security
	- code-generation
	- cybersecurity
	- llama-3
	- unsloth
	- fine-tune
	license: apache-2.0
	datasets:
	- custom-vulnerability-fix-dataset
	metrics:
	- code_eval
	language:
	- en
	---

	Jack: The Secure Code Agent (Llama-3 8B)

	Jack is a specialized fine-tune of Llama-3 8B, engineered to detect and fix security vulnerabilities in Python code. It acts as an automated security auditor, taking insecure code as input and outputting a hardened, secure version.

	## Key Metrics
	\| Metric \| Score \| Description \|
	\| :--- \| :--- \| :--- \|
	\| Bandit Pass Rate \| 88.0% \| Percentage of fixes that pass the Bandit static analysis security tool. \|
	\| BLEU Score \| 69.27 \| High structural similarity to human-expert security patches. \|

	## Quick Start
	You can use this model directly via the Hugging Face Inference API or load it locally.

	### Inference API (Serverless)
	```python
	import requests

	API_URL = "[https://api-inference.huggingface.co/models/oke39/llama3-8b-secure-code](https://api-inference.huggingface.co/models/oke39/llama3-8b-secure-code)"
	headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

	payload = {
	"inputs": """<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>

	You are Jack, a Secure Code Agent. Fix the security vulnerability in the provided code.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	def login(username, password):
	# Vulnerable SQL Injection
	query = "SELECT * FROM users WHERE username = '" + username + "' AND password = '" + password + "'"
	cursor.execute(query)<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>
	"""
	}

	response = requests.post(API_URL, headers=headers, json=payload)
	print(response.json())
	```

	# Training Details
	- Dataset: [Vulnerability Fix Dataset](https://www.kaggle.com/datasets/jiscecseaiml/vulnerability-fix-dataset)
	- Vulnerabilities Covered: SQL Injection, XSS, Command Injection, Insecure Deserialization, Hardcoded Credentials.

	# Training Details
	- GGUF Version: [oke39/llama3-8b-secure-code-gguf](https://huggingface.co/oke39/llama3-8b-secure-code-gguf)
	- MLflow Experiment Tracking: [Dasgshub Project](https://dagshub.com/oke39/llama3-code-agent)

	# Uploaded finetuned model

	- Developed by: oke39
	- License: apache-2.0
	- Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit

	This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)