Instructions to use niranjan2777/SENTINEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use niranjan2777/SENTINEL with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "niranjan2777/SENTINEL")

Transformers

How to use niranjan2777/SENTINEL with Transformers:

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

pipe = pipeline("text-generation", model="niranjan2777/SENTINEL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("niranjan2777/SENTINEL", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use niranjan2777/SENTINEL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "niranjan2777/SENTINEL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "niranjan2777/SENTINEL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/niranjan2777/SENTINEL

SGLang

How to use niranjan2777/SENTINEL 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 "niranjan2777/SENTINEL" \
    --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": "niranjan2777/SENTINEL",
		"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 "niranjan2777/SENTINEL" \
        --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": "niranjan2777/SENTINEL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use niranjan2777/SENTINEL 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 niranjan2777/SENTINEL 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 niranjan2777/SENTINEL to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for niranjan2777/SENTINEL to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="niranjan2777/SENTINEL",
    max_seq_length=2048,
)

Docker Model Runner
How to use niranjan2777/SENTINEL with Docker Model Runner:
```
docker model run hf.co/niranjan2777/SENTINEL
```

SENTINEL — Llama-3-8B QLoRA SFT + GRPO Fine-tune

This repository contains the Final (QLoRA SFT + GRPO) adapter weights for SENTINEL, an autonomous web-exploitation agent.

Model Overview

SENTINEL is trained to autonomously navigate, analyze, and exploit web vulnerabilities using a structured JSON-based reasoning and action schema .

This repository represents the completion of both Stage 1 and Stage 2:

Stage 1: QLoRA SFT on 415 SENTINEL trajectory pairs. This stage teaches the agent the correct JSON schema, action vocabulary, and basic reasoning patterns for web exploitation.
Stage 2: GRPO (Generative Reward Proximal Policy Optimization). This stage reinforces successful exploitation pathways, heavily penalizing JSON formatting errors and hallucinated actions, while rewarding verified vulnerability exploitation.

Training Details

The model was fine-tuned using Unsloth for optimized, 2x faster training.

Hardware & Configuration

GPU: 1x Tesla T4 (16GB VRAM)
Precision: FP16 (bf16=False)
Base Model: unsloth/llama-3-8b-Instruct-bnb-4bit
Training Time: ~45 minutes

Hyperparameters

Num Examples: 415
Epochs: 2
Total Steps: 104
Batch Size per Device: 2
Gradient Accumulation Steps: 4
Total Effective Batch Size: 8
Trainable Parameters: 41,943,040 of 8,072,204,288 (0.52% trained)

Results

Final Training Loss: 1.0764
Final Validation Loss: 1.1465

Loss curve during training:

Step	Training Loss	Validation Loss
20	1.333500	1.492488
40	1.127200	1.263251
60	0.689200	1.210084
80	0.724800	1.163273
100	0.763000	1.146508

Dataset

The model was trained on a custom dataset (train_llama3.jsonl) consisting of 415 SENTINEL trajectory pairs. These trajectories represent successful pentesting workflows, teaching the model how to target vulnerability sinks (form actions, hidden fields, query parameters, JSON bodies, etc.), infer backend technologies, and deliver appropriate payloads.

Usage

Because this is a PEFT (Parameter-Efficient Fine-Tuning) adapter, you must load the base model (unsloth/llama-3-8b-Instruct-bnb-4bit or the standard meta-llama/Meta-Llama-3-8B-Instruct) and apply these LoRA weights on top using the peft library.

Framework Versions

PEFT 0.19.1
Transformers
Unsloth

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