Instructions to use AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF with PEFT:
```
Task type is invalid.
```

How to use AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF

SGLang

How to use AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF 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 "AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF
```

AB-Y0/PentesterFlow-V3-Reasoning-LoRA-Q8_0-GGUF

This LoRA adapter was converted to GGUF format from AB-Y0/PentesterFlow-V3-Reasoning-LoRA via the ggml.ai's GGUF-my-lora space. Refer to the original adapter repository for more details.

Use with llama.cpp

# with cli
llama-cli -m base_model.gguf --lora PentesterFlow-V3-Reasoning-LoRA-q8_0.gguf (...other args)

# with server
llama-server -m base_model.gguf --lora PentesterFlow-V3-Reasoning-LoRA-q8_0.gguf (...other args)

To know more about LoRA usage with llama.cpp server, refer to the llama.cpp server documentation.