Text Generation
Transformers
Safetensors
GGUF
English
llama
sql
forensics
text-to-sql
fine-tuned
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use pawlaszc/DigitalForensicsText2SQLite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pawlaszc/DigitalForensicsText2SQLite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pawlaszc/DigitalForensicsText2SQLite") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pawlaszc/DigitalForensicsText2SQLite") model = AutoModelForCausalLM.from_pretrained("pawlaszc/DigitalForensicsText2SQLite", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use pawlaszc/DigitalForensicsText2SQLite 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 pawlaszc/DigitalForensicsText2SQLite:Q4_K_M # Run inference directly in the terminal: llama cli -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M # Run inference directly in the terminal: llama cli -hf pawlaszc/DigitalForensicsText2SQLite: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 pawlaszc/DigitalForensicsText2SQLite:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf pawlaszc/DigitalForensicsText2SQLite: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 pawlaszc/DigitalForensicsText2SQLite:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
Use Docker
docker model run hf.co/pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use pawlaszc/DigitalForensicsText2SQLite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pawlaszc/DigitalForensicsText2SQLite" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pawlaszc/DigitalForensicsText2SQLite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
- SGLang
How to use pawlaszc/DigitalForensicsText2SQLite 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 "pawlaszc/DigitalForensicsText2SQLite" \ --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": "pawlaszc/DigitalForensicsText2SQLite", "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 "pawlaszc/DigitalForensicsText2SQLite" \ --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": "pawlaszc/DigitalForensicsText2SQLite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use pawlaszc/DigitalForensicsText2SQLite with Ollama:
ollama run hf.co/pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
- Unsloth Desktop
- Pi
How to use pawlaszc/DigitalForensicsText2SQLite with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "pawlaszc/DigitalForensicsText2SQLite:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use pawlaszc/DigitalForensicsText2SQLite with Docker Model Runner:
docker model run hf.co/pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
- Lemonade
How to use pawlaszc/DigitalForensicsText2SQLite with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
Run and chat with the model
lemonade run user.DigitalForensicsText2SQLite-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use pawlaszc/DigitalForensicsText2SQLite with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use pawlaszc/DigitalForensicsText2SQLite with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "pawlaszc/DigitalForensicsText2SQLite:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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Verified signature# ForensicSQL-Llama-3.2-3B ## Model Description **ForensicSQL** is a fine-tuned Llama 3.2 3B model specialised for generating SQLite queries for mobile forensics databases. The model converts natural language forensic investigation requests into executable SQL queries across various mobile app databases (WhatsApp, Signal, iOS Health, Android SMS, etc.). This model was developed as part of a master's thesis investigating LLM fine-tuning for forensic database analysis. ## Model Details - **Base Model:** Llama 3.2 3B Instruct - **Fine-tuning Method:** LoRA (Low-Rank Adaptation) - **Training Dataset:** 768 forensic SQL examples across 148 categories - **Training Framework:** Hugging Face Transformers + PEFT - **Model Size:** - Full (FP16): ~6 GB - GGUF Q4_K_M: ~2.3 GB - GGUF Q5_K_M: ~2.8 GB - GGUF Q8_0: ~3.8 GB ## Performance ### Overall Results - **Overall Accuracy:** 79.0% - **Schema Generation Errors:** 0% (completely eliminated) - **Executable Queries:** 79% ### Breakdo…ffbc229 -
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