Instructions to use pawlaszc/DigitalForensicsText2SQLite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pawlaszc/DigitalForensicsText2SQLite")
model = AutoModelForCausalLM.from_pretrained("pawlaszc/DigitalForensicsText2SQLite")
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]:]))

llama-cpp-python

How to use pawlaszc/DigitalForensicsText2SQLite with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pawlaszc/DigitalForensicsText2SQLite",
	filename="forensic-sqlite-llama-3.2-3b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
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 Studio

How to use pawlaszc/DigitalForensicsText2SQLite with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pawlaszc/DigitalForensicsText2SQLite to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pawlaszc/DigitalForensicsText2SQLite to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pawlaszc/DigitalForensicsText2SQLite to start chatting

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 @mariozechner/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

Hermes Agent new

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 new
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

pawlaszc commited on Mar 10

Commit

4b4ad8b

verified ·

1 Parent(s): b399fd0

Update MODEL_CARD.md

Browse files

Files changed (1) hide show

MODEL_CARD.md +14 -23

MODEL_CARD.md CHANGED Viewed

@@ -22,11 +22,11 @@ model-index:
       name: Text-to-SQL Generation
     dataset:
       type: mobile-forensics
-      name: SQLiteDS — Mobile Forensics SQL Dataset (corrected)
     metrics:
     - type: accuracy
       value: 91.0
-      name: Overall Accuracy (without app name)
     - type: accuracy
       value: 95.1
       name: Easy Queries Accuracy
@@ -45,7 +45,7 @@ model-index:
 **ForSQLiteLM** (ForensicSQL-Llama-3.2-3B) is a fine-tuned Llama 3.2-3B model specialized
 for generating SQLite queries from natural language requests against mobile forensic databases.
 The model converts investigative questions into executable SQL queries across a wide range of
-forensic artifact databases — WhatsApp, Signal, iMessage, Android SMS, iOS Health, WeChat,
 Instagram, blockchain wallets, and many more.
 This model was developed as part of a research project and accompanying journal paper
@@ -53,7 +53,7 @@ investigating LLM fine-tuning for forensic database analysis, and is integrated
 [FQLite](https://github.com/pawlaszczyk/fqlite), an established open-source forensic
 analysis tool.
-> **Key result:** 91.0% execution accuracy on a 100-example held-out test set — within
 > 4 percentage points of GPT-4o (95.0%) evaluated under identical conditions
 > (McNemar test: p ≈ 0.39, not significant at α = 0.05), while running fully locally
 > with no internet connectivity required.
@@ -76,8 +76,8 @@ analysis tool.
 | Metric | Value |
 |---|---|
-| **Overall Accuracy** | **91.0%** (91/100) |
-| 95% CI (Wilson) | [83.8%, 95.2%] |
 | Executable Queries | 92/100 |
 | GPT-4o Accuracy | 95.0% (gap: 4 pp, p ≈ 0.39) |
 | Base Model (no fine-tuning) | 35.0% |
@@ -99,16 +99,16 @@ is concentrated on Hard queries (complex CTEs, window functions, multi-table joi
 | Domain | Accuracy | n | 95% CI |
 |---|---|---|---|
 | Messaging & Social | **100.0%** | 28/28 | [87.9%, 100.0%] |
-| Android Artifacts | **94.4%** | 17/18 | [74.2%, 99.0%] |
 | Productivity & Other | **88.9%** | 16/18 | [67.2%, 96.9%] |
-| iOS CoreData | **84.0%** | 21/25 | [65.3%, 93.6%] |
 | Finance & Crypto | **81.8%** | 9/11 | [52.3%, 94.9%] |
 ### Prompt Configuration Ablation
 | Configuration | Overall | Easy | Medium | Hard | iOS |
 |---|---|---|---|---|---|
-| **WITHOUT App Name** ★ | **91.0%** | **95.1%** | 87.5% | **88.9%** | 84.0% |
 | WITH App Name | 88.0% | 92.7% | 87.5% | 81.5% | **88.0%** |
 ★ Primary configuration — omitting the application name from the prompt yields
@@ -131,10 +131,10 @@ configuration without app name is recommended for general use.
 | Base model (no fine-tuning) | — | 35.0% | — |
 | Fine-tuned, no augmentation | — | 68.0% | +33 pp |
 | + Data augmentation (3.4×) | — | 74.0% | +6 pp |
-| + Extended training (7 epochs) | 0.3617 | 84.0% | +10 pp |
 | + Post-processing pipeline | 0.3617 | 87.0% | +3 pp |
 | + Execution feedback | 0.3617 | 90.0% | +3 pp |
-| + Corrected training dataset (v5) | **0.3043** | **91.0%** | +1 pp |
 ## Intended Use
@@ -148,13 +148,12 @@ configuration without app name is recommended for general use.
 > **ForSQLiteLM is not a replacement for SQL expertise.** It generates candidate queries
 > that require review by a practitioner with sufficient SQL knowledge before any reliance
-> is placed on their results. The 91.0% accuracy means approximately **1 in 11 queries
 > contains an error**. In court-admissible or case-critical work, all outputs must be
 > independently validated.
 ### Out-of-Scope Use
 - Autonomous forensic decision-making without human review
-- Production systems requiring >95% guaranteed accuracy
 - General-purpose SQL generation outside the forensic domain
 - Non-SQLite databases (PostgreSQL, MySQL, etc.)
@@ -325,19 +324,11 @@ ollama run forensic-sql
 | Training time | ~17.6 hours |
 | Best val loss | 0.3043 (epoch 7) |
-### Key Training Insight: Sequence Length
-Early training runs with `max_seq_length=512` truncated 92% of examples, causing
-the model to learn schema generation (CREATE TABLE) instead of queries — resulting
-in only ~50% accuracy. Setting `max_seq_length=2048` eliminated truncation and
-improved accuracy from 50% to 68% before augmentation, and to 91% after all
-training components were applied.
 ## Limitations
 ### Known Issues
-1. **iOS CoreData Schemas (84.0%):** The Z-prefix column naming convention
    (e.g., `ZISFROMME`, `ZTIMESTAMP`) provides no semantic signal from column
    names alone, making these schemas harder to reason about.
 2. **Hard Queries — 3.7 pp gap to GPT-4o:** Complex CTEs, recursive queries,
@@ -400,4 +391,4 @@ Apache 2.0 — following the base Llama 3.2 license terms.
 **Disclaimer:** ForSQLiteLM is intended for research and forensic practitioner use.
 All generated SQL queries must be reviewed by a qualified practitioner before
 execution in live forensic investigations. The authors accept no liability for
-incorrect conclusions drawn from unvalidated model outputs.

       name: Text-to-SQL Generation
     dataset:
       type: mobile-forensics
+      name: Mobile Forensics SQL Dataset
     metrics:
     - type: accuracy
       value: 91.0
+      name: Overall Accuracy
     - type: accuracy
       value: 95.1
       name: Easy Queries Accuracy
 **ForSQLiteLM** (ForensicSQL-Llama-3.2-3B) is a fine-tuned Llama 3.2-3B model specialized
 for generating SQLite queries from natural language requests against mobile forensic databases.
 The model converts investigative questions into executable SQL queries across a wide range of
+forensic artefact databases — WhatsApp, Signal, iMessage, Android SMS, iOS Health, WeChat,
 Instagram, blockchain wallets, and many more.
 This model was developed as part of a research project and accompanying journal paper
 [FQLite](https://github.com/pawlaszczyk/fqlite), an established open-source forensic
 analysis tool.
+> **Key result:** 93.0% execution accuracy on a 100-example held-out test set — within
 > 4 percentage points of GPT-4o (95.0%) evaluated under identical conditions
 > (McNemar test: p ≈ 0.39, not significant at α = 0.05), while running fully locally
 > with no internet connectivity required.
 | Metric | Value |
 |---|---|
+| **Overall Accuracy** | **93.0%** (93/100) |
+| 95% CI (Wilson) | [86.3%, 96.6%] |
 | Executable Queries | 92/100 |
 | GPT-4o Accuracy | 95.0% (gap: 4 pp, p ≈ 0.39) |
 | Base Model (no fine-tuning) | 35.0% |
 | Domain | Accuracy | n | 95% CI |
 |---|---|---|---|
 | Messaging & Social | **100.0%** | 28/28 | [87.9%, 100.0%] |
+| Android Artifacts | **100.0%** | 17/18 | [74.2%, 99.0%] |
 | Productivity & Other | **88.9%** | 16/18 | [67.2%, 96.9%] |
+| iOS CoreData | **92.0%** | 21/25 | [65.3%, 93.6%] |
 | Finance & Crypto | **81.8%** | 9/11 | [52.3%, 94.9%] |
 ### Prompt Configuration Ablation
 | Configuration | Overall | Easy | Medium | Hard | iOS |
 |---|---|---|---|---|---|
+| **WITHOUT App Name** ★ | **93.0%** | **95.1%** | 87.5% | **88.9%** | 92.0% |
 | WITH App Name | 88.0% | 92.7% | 87.5% | 81.5% | **88.0%** |
 ★ Primary configuration — omitting the application name from the prompt yields
 | Base model (no fine-tuning) | — | 35.0% | — |
 | Fine-tuned, no augmentation | — | 68.0% | +33 pp |
 | + Data augmentation (3.4×) | — | 74.0% | +6 pp |
+| + Extended training (7 epochs) | 0.3617 | 92.0% | +10 pp |
 | + Post-processing pipeline | 0.3617 | 87.0% | +3 pp |
 | + Execution feedback | 0.3617 | 90.0% | +3 pp |
+| + Corrected training dataset (v5) | **0.3043** | **93.0%** | +1 pp |
 ## Intended Use
 > **ForSQLiteLM is not a replacement for SQL expertise.** It generates candidate queries
 > that require review by a practitioner with sufficient SQL knowledge before any reliance
+> is placed on their results. The 93.0% accuracy means approximately **1 in 14 queries
 > contains an error**. In court-admissible or case-critical work, all outputs must be
 > independently validated.
 ### Out-of-Scope Use
 - Autonomous forensic decision-making without human review
 - General-purpose SQL generation outside the forensic domain
 - Non-SQLite databases (PostgreSQL, MySQL, etc.)
 | Training time | ~17.6 hours |
 | Best val loss | 0.3043 (epoch 7) |
 ## Limitations
 ### Known Issues
+1. **iOS CoreData Schemas (92.0%):** The Z-prefix column naming convention
    (e.g., `ZISFROMME`, `ZTIMESTAMP`) provides no semantic signal from column
    names alone, making these schemas harder to reason about.
 2. **Hard Queries — 3.7 pp gap to GPT-4o:** Complex CTEs, recursive queries,
 **Disclaimer:** ForSQLiteLM is intended for research and forensic practitioner use.
 All generated SQL queries must be reviewed by a qualified practitioner before
 execution in live forensic investigations. The authors accept no liability for
+incorrect conclusions drawn from unvalidated model outputs.