Instructions to use mastomi/bachuntr-14b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mastomi/bachuntr-14b-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("model_training/model_download")
model = PeftModel.from_pretrained(base_model, "mastomi/bachuntr-14b-lora")

Transformers

How to use mastomi/bachuntr-14b-lora with Transformers:

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

pipe = pipeline("text-generation", model="mastomi/bachuntr-14b-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mastomi/bachuntr-14b-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mastomi/bachuntr-14b-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mastomi/bachuntr-14b-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mastomi/bachuntr-14b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mastomi/bachuntr-14b-lora

SGLang

How to use mastomi/bachuntr-14b-lora 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 "mastomi/bachuntr-14b-lora" \
    --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": "mastomi/bachuntr-14b-lora",
		"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 "mastomi/bachuntr-14b-lora" \
        --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": "mastomi/bachuntr-14b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use mastomi/bachuntr-14b-lora with Docker Model Runner:
```
docker model run hf.co/mastomi/bachuntr-14b-lora
```

bachuntr-14b-LoRA

QLoRA adapter for Qwen3-14B, fine-tuned for automated BAC/IDOR vulnerability testing.

Model Details

Property	Value
Base Model	Qwen/Qwen3-14B
LoRA Rank	64
LoRA Alpha	16
Method	RSLoRA, all projection layers
Training Examples	413 train / 67 val
Epochs	4
Batch Size	2 × grad_accum 8
Learning Rate	2e-4
Max Seq Length	16384
Final Loss	0.155 (from 2.069)
Adapter Size	~500 MB

Usage

With Transformers + PEFT

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", device_map="auto")
model = PeftModel.from_pretrained(base, "your-username/bachuntr-14b-lora")
tokenizer = AutoTokenizer.from_pretrained("your-username/bachuntr-14b-lora")

With vLLM (recommended for production)

vllm serve Qwen/Qwen3-14B \
  --enable-lora \
  --lora-modules bachuntr-14b=./bachuntr-14b-lora \
  --served-model-name bachuntr-14b \
  --max-model-len 32768

Merge into full model

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="auto")
model = PeftModel.from_pretrained(base, "./bachuntr-14b-lora")
merged = model.merge_and_unload()
merged.save_pretrained("./bachuntr-14b-merged")

Training Data

Fine-tuned on distilled multi-agent security testing conversations covering:

Account provisioning & authentication
Endpoint discovery & API reconnaissance
Attack planning
BAC/IDOR exploitation (horizontal, vertical, privilege escalation)
Multi-persona vulnerability verification
Security report generation

7 target applications, 480 unique conversations, 22K+ message turns with full tool-calling schemas.

Intended Use

Automated security testing of web applications for Broken Access Control and IDOR vulnerabilities via the bachuntr framework.

Framework Versions

PEFT 0.18.1
TRL: 0.24.0
Transformers: 5.5.0
Pytorch: 2.10.0+cu126
Datasets: 4.3.0
Tokenizers: 0.22.2
Unsloth: 2026.4.1

Downloads last month: 3

Model tree for mastomi/bachuntr-14b-lora

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