Text Generation
PEFT
Safetensors
GGUF
English
Portuguese
cybersecurity
pentest
qlora
lora
llama-3.1
foundation-sec
persona
conversational
Instructions to use virgiliolrf2/bagley-v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use virgiliolrf2/bagley-v10 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/vol/foundation-sec-8b") model = PeftModel.from_pretrained(base_model, "virgiliolrf2/bagley-v10") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use virgiliolrf2/bagley-v10 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf virgiliolrf2/bagley-v10:Q4_K_M # Run inference directly in the terminal: llama cli -hf virgiliolrf2/bagley-v10:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf virgiliolrf2/bagley-v10:Q4_K_M # Run inference directly in the terminal: llama cli -hf virgiliolrf2/bagley-v10:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf virgiliolrf2/bagley-v10:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf virgiliolrf2/bagley-v10:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf virgiliolrf2/bagley-v10:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf virgiliolrf2/bagley-v10:Q4_K_M
Use Docker
docker model run hf.co/virgiliolrf2/bagley-v10:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use virgiliolrf2/bagley-v10 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "virgiliolrf2/bagley-v10" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "virgiliolrf2/bagley-v10", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/virgiliolrf2/bagley-v10:Q4_K_M
- Ollama
How to use virgiliolrf2/bagley-v10 with Ollama:
ollama run hf.co/virgiliolrf2/bagley-v10:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use virgiliolrf2/bagley-v10 with Docker Model Runner:
docker model run hf.co/virgiliolrf2/bagley-v10:Q4_K_M
- Lemonade
How to use virgiliolrf2/bagley-v10 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull virgiliolrf2/bagley-v10:Q4_K_M
Run and chat with the model
lemonade run user.bagley-v10-Q4_K_M
List all available models
lemonade list
- Atomic Chat
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "/vol/foundation-sec-8b", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 16, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "down_proj", | |
| "q_proj", | |
| "k_proj", | |
| "up_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj" | |
| ], | |
| "task_type": "CAUSAL_LM", | |
| "use_dora": false, | |
| "use_rslora": false | |
| } |