Instructions to use Krishnapadala55/brahmastra-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Krishnapadala55/brahmastra-0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Krishnapadala55/brahmastra-0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Krishnapadala55/brahmastra-0.1")
model = AutoModelForCausalLM.from_pretrained("Krishnapadala55/brahmastra-0.1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Krishnapadala55/brahmastra-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Krishnapadala55/brahmastra-0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Krishnapadala55/brahmastra-0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Krishnapadala55/brahmastra-0.1

SGLang

How to use Krishnapadala55/brahmastra-0.1 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 "Krishnapadala55/brahmastra-0.1" \
    --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": "Krishnapadala55/brahmastra-0.1",
		"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 "Krishnapadala55/brahmastra-0.1" \
        --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": "Krishnapadala55/brahmastra-0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Krishnapadala55/brahmastra-0.1 with Docker Model Runner:
```
docker model run hf.co/Krishnapadala55/brahmastra-0.1
```

BRAHMASTRA 0.1 — AI-Native DAST Security Scanner

"Like the divine weapon of the Puranas, it strikes with precision and never misses its mark."

BRAHMASTRA is a fine-tuned 7B language model purpose-built for Dynamic Application Security Testing (DAST).
It is trained to reason about web application vulnerabilities, generate targeted security payloads,
analyze HTTP responses, and produce structured security findings — all autonomously.

🎯 Capabilities

Module	Vulnerability Type
Naagastra	SQL Injection (Error-based, Blind, Time-based)
Aindrastra	Cross-Site Scripting (Reflected, Stored, DOM)
Pashupatastra	Server-Side Template Injection (Jinja2, Twig, ERB)
Vayavyastra	Server-Side Request Forgery
Brahmaanda Astra	Authentication Bypass
Pasha Astra	IDOR / Broken Object Level Auth
Kavachabhedana	WAF Detection & Bypass

🏋️ Training

BRAHMASTRA was trained in 5 phases using LoRA fine-tuning on top of Qwen2.5-Coder-7B-Instruct:

Phase	Focus	Samples
1a	SQLi + XSS fundamentals	2,000
1b	SSTI + SSRF	2,000
1c	IDOR + Auth bypass	2,000
2	Multi-step attack chains	3,000
3	WAF bypass + adversarial	2,000
Cleanup	Hallucination removal, concrete payloads	3,000

LoRA rank: 128, alpha: 256, rslora: true
Base model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
Training framework: Unsloth + PEFT + TRL SFTTrainer
Hardware: Custom GPU server (beast)

🚀 Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Krishnapadala55/brahmastra-0.1",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Krishnapadala55/brahmastra-0.1")

messages = [
    {"role": "system", "content": "You are BRAHMASTRA, an elite DAST security scanner."},
    {"role": "user",   "content": "Test /login.php parameter 'uname' for SQL injection."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected Output Format

<think>
Testing uname parameter for SQL injection. Will start with error-based payload.
</think>
inject_payload("/login.php", "uname", "' OR '1'='1", method="POST")

🛡️ Responsible Use

This model is intended for:

Authorized penetration testing of systems you own or have explicit written permission to test
Security research and education
Building DAST tooling for legitimate security teams

This model must NOT be used for:

Testing systems without authorization
Malicious exploitation of vulnerabilities
Any illegal activity

By downloading and using this model, you agree to use it only for authorized security testing.

📊 Benchmark Results

Tested against phpvulnbank (intentionally vulnerable PHP application):

Metric	Result
Requests made	436
Scan duration	176 seconds
Critical findings	18
Medium findings	2
False positives	0
Pages covered	6 (full crawl + auth)

🔧 BRAHMASTRA Scanner

The full BRAHMASTRA DAST engine (agent loop, crawler, report generator) is available at:
👉 github.com/krishnareddypadala/brahmastra

📄 License

Apache 2.0 — see LICENSE

Built with ❤️ by Krishna Padala — Hyderabad, India

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Krishnapadala55/brahmastra-0.1

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Adapter

(679)

this model

Adapters

2 models