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Model: pawlaszc/DigitalForensicsText2SQLite
Source: Original Platform

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+forensic-sqlite-llama-3.2-3b-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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MODEL_CARD.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- sql
+- forensics
+- text-to-sql
+- llama
+- fine-tuned
+base_model: unsloth/Llama-3.2-3B-Instruct
+datasets:
+- pawlaszc/mobile-forensics-sql
+metrics:
+- accuracy
+model-index:
+- name: ForensicSQL-Llama-3.2-3B
+  results:
+  - task:
+      type: text-to-sql
+      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
+    - type: accuracy
+      value: 87.5
+      name: Medium Queries Accuracy
+    - type: accuracy
+      value: 88.9
+      name: Hard Queries Accuracy
+---
+
+# ForensicSQL-Llama-3.2-3B
+
+## Model Description
+
+**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
+investigating LLM fine-tuning for forensic database analysis, and is integrated into
+[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.
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Base Model** | meta-llama/Llama-3.2-3B-Instruct |
+| **Fine-tuning Method** | Full fine-tune (bf16) |
+| **Training Dataset** | SQLiteDS — 800 training examples, 191 forensic artifact categories |
+| **Training Framework** | Hugging Face Transformers |
+| **Best Val Loss** | 0.3043 (7 epochs) |
+| **Model Size (bf16)** | ~6 GB |
+| **Hardware Required** | 16 GB unified memory (Apple M-series) or equivalent GPU |
+
+## Performance
+
+### Overall Results (fixed dataset, n=100, best configuration)
+
+| 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% |
+| Improvement over base | +56 pp |
+
+### Accuracy by Query Difficulty
+
+| Difficulty | Accuracy | n | 95% CI | vs. GPT-4o |
+|---|---|---|---|---|
+| Easy (single-table) | **95.1%** | 39/41 | [83.9%, 98.7%] | 0.0 pp |
+| Medium (joins, aggregation) | **87.5%** | 28/32 | [71.9%, 95.0%] | 0.0 pp |
+| Hard (CTEs, window functions) | **88.9%** | 24/27 | [71.9%, 96.1%] | −3.7 pp |
+
+ForSQLiteLM matches GPT-4o exactly on Easy and Medium queries. The remaining gap
+is concentrated on Hard queries (complex CTEs, window functions, multi-table joins).
+
+### Accuracy by Forensic Domain
+
+| 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
+3 pp higher overall accuracy. Interestingly, including the app name helps iOS
+CoreData schemas (+4 pp) but hurts Hard queries (−7.4 pp); the primary
+configuration without app name is recommended for general use.
+
+### Post-Processing Pipeline Contribution
+
+| Component | Queries saved |
+|---|---|
+| Execution feedback (retry) | 7 |
+| Alias normalization | 18 |
+| Column corrections (Levenshtein) | 2 |
+
+### Training Progression
+
+| Configuration | Val Loss | Accuracy | Δ |
+|---|---|---|---|
+| 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
+
+### Primary Use Cases
+- Mobile forensics investigations: automated SQL query drafting against seized device databases
+- Integration into forensic tools (FQLite, Autopsy, ALEAPP/iLEAPP workflows)
+- Research in domain-specific Text-to-SQL
+- Educational use for learning forensic database analysis
+
+### Important: This Model is a Drafting Assistant
+
+> **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.)
+
+## How to Use
+
+### Quick Start (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "pawlaszc/ForensicSQL-Llama-3.2-3B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+model.eval()
+
+schema = """
+CREATE TABLE message (
+    ROWID INTEGER PRIMARY KEY,
+    text TEXT,
+    handle_id INTEGER,
+    date INTEGER,
+    is_from_me INTEGER,
+    cache_has_attachments INTEGER
+);
+CREATE TABLE handle (
+    ROWID INTEGER PRIMARY KEY,
+    id TEXT,
+    service TEXT
+);
+"""
+
+request = "Find all messages received in the last 7 days that contain attachments"
+
+# Note: do NOT use apply_chat_template — use plain-text prompt
+prompt = f"""Generate a valid SQLite query for this forensic database request.
+
+Database Schema:
+{schema}
+
+Request: {request}
+
+SQLite Query:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=300,
+        do_sample=False,        # greedy decoding — do not change
+    )
+
+input_length = inputs['input_ids'].shape[1]
+sql = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
+print(sql.strip())
+```
+
+> **Important:** Use plain-text tokenization (do **not** call `apply_chat_template`).
+> The model was trained and evaluated with a plain-text prompt format.
+> Use `do_sample=False` (greedy decoding) for reproducible results.
+
+### Python Helper Class
+
+```python
+class ForensicSQLGenerator:
+    def __init__(self, model_name="pawlaszc/ForensicSQL-Llama-3.2-3B"):
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        import torch
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+        self.model.eval()
+
+    def generate_sql(self, schema: str, request: str) -> str:
+        prompt = (
+            "Generate a valid SQLite query for this forensic database request.\n\n"
+            f"Database Schema:\n{schema}\n\n"
+            f"Request: {request}\n\n"
+            "SQLite Query:\n"
+        )
+        inputs = self.tokenizer(
+            prompt, return_tensors="pt", truncation=True, max_length=2048
+        )
+        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+        input_length = inputs["input_ids"].shape[1]
+
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs, max_new_tokens=300, do_sample=False
+            )
+
+        sql = self.tokenizer.decode(
+            outputs[0][input_length:], skip_special_tokens=True
+        )
+        # Return first statement only, normalized
+        return sql.strip().split("\n")[0].strip().rstrip(";") + ";"
+
+
+# Usage
+generator = ForensicSQLGenerator()
+sql = generator.generate_sql(schema, "Find all unread messages from the last 24 hours")
+print(sql)
+```
+
+### With Ollama / llama.cpp (GGUF)
+
+```bash
+# With llama.cpp
+./llama-cli -m forensic-sql-q4_k_m.gguf \
+  --temp 0 \
+  -p "Generate a valid SQLite query for this forensic database request.
+
+Database Schema:
+CREATE TABLE sms (_id INTEGER PRIMARY KEY, address TEXT, body TEXT, date INTEGER);
+
+Request: Find all messages sent after midnight
+
+SQLite Query:"
+
+# With Ollama — create a Modelfile
+cat > Modelfile << 'EOF'
+FROM ./forensic-sql-q4_k_m.gguf
+PARAMETER temperature 0
+PARAMETER num_predict 300
+EOF
+
+ollama create forensic-sql -f Modelfile
+ollama run forensic-sql
+```
+
+## Training Details
+
+### Dataset — SQLiteDS
+
+- **Total examples:** 1,000 (800 train / 100 val / 100 test), fixed random seed 42
+- **Forensic artifact categories:** 191
+- **Reference query validation:** All 1,000 reference queries validated for execution
+  correctness against in-memory SQLite; 50 queries (5%) corrected before final training
+- **Augmentation:** 3.4× expansion via instruction paraphrasing, WHERE clause reordering,
+  and LIMIT injection — augmented examples confined to training split only
+- **Dataset:** [pawlaszc/mobile-forensics-sql](https://huggingface.co/datasets/pawlaszc/mobile-forensics-sql)
+- **License:** CC BY 4.0
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Training method | Full fine-tune (no LoRA) |
+| Precision | bfloat16 |
+| Epochs | 7 |
+| Learning rate | 2e-5 (peak) |
+| LR scheduler | Cosine with warmup |
+| Batch size | 1 + gradient accumulation 4 |
+| Max sequence length | 2048 |
+| Optimizer | AdamW |
+| Hardware | Apple M-series, 16 GB unified memory |
+| 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,
+   and window functions are the primary remaining challenge.
+3. **Finance & Crypto (81.8%, n=11):** Small test set; confidence intervals are
+   wide. Interpret with caution.
+4. **~1 in 11 error rate:** Approximately 9% of generated queries will contain
+   errors. Expert review of all outputs is required before use in investigations.
+
+### When Human Review is Especially Important
+- Complex multi-table queries with CTEs or window functions
+- Case-critical or court-admissible investigations
+- Any query that will be used to draw conclusions about a suspect
+- Queries involving rare or unusual forensic artifact schemas
+
+## Evaluation
+
+- **Test set:** 100 examples, held-out, seed=42, non-augmented
+- **Metric:** Execution accuracy — query is correct iff it executes without error
+  AND returns a result set identical to the reference query
+- **Reference validation:** All reference queries validated for execution correctness
+  before evaluation; 5 broken queries in the test set were corrected
+- **Evaluation script:** Available in the dataset repository on Zenodo ([DOI])
+
+## Citation
+
+If you use this model or the SQLiteDS dataset in your research, please cite:
+
+```bibtex
+@article{pawlaszczyk2026forsqlitelm,
+  author  = {Dirk Pawlaszczyk},
+  title   = {AI-Based Automated SQL Query Generation for SQLite Databases
+             in Mobile Forensics},
+  journal = {Forensic Science International: Digital Investigation},
+  year    = {2026},
+  note    = {FSIDI-D-26-00029}
+}
+```
+
+## License
+
+Apache 2.0 — following the base Llama 3.2 license terms.
+
+## Acknowledgments
+
+- Base model: Meta's Llama 3.2-3B-Instruct
+- Training framework: Hugging Face Transformers
+- Forensic tool integration: [FQLite](https://github.com/pawlaszczyk/fqlite)
+- Schema sources: iLEAPP, ALEAPP, Autopsy (used under their respective open-source licenses)
+
+## Additional Resources
+
+- **Dataset (Zenodo):** [SQLiteDS — DOI to be added on publication]
+- **Dataset (HuggingFace):** [pawlaszc/mobile-forensics-sql](https://huggingface.co/datasets/pawlaszc/mobile-forensics-sql)
+- **FQLite integration:** [github.com/pawlaszczyk/fqlite](https://github.com/pawlaszczyk/fqlite)
+- **Paper:** FSIDI-D-26-00029 (under review)
+
+---
+
+**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.

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- sql
+- forensics
+- text-to-sql
+- llama
+- fine-tuned
+base_model: unsloth/Llama-3.2-3B-Instruct
+datasets:
+- pawlaszc/mobile-forensics-sql
+metrics:
+- accuracy
+model-index:
+- name: ForensicSQL-Llama-3.2-3B
+  results:
+  - task:
+      type: text-to-sql
+      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
+    - type: accuracy
+      value: 87.5
+      name: Medium Queries Accuracy
+    - type: accuracy
+      value: 88.9
+      name: Hard Queries Accuracy
+---
+
+# ForensicSQL-Llama-3.2-3B
+
+## Model Description
+
+**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
+investigating LLM fine-tuning for forensic database analysis, and is integrated into
+[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.
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Base Model** | meta-llama/Llama-3.2-3B-Instruct |
+| **Fine-tuning Method** | Full fine-tune (bf16) |
+| **Training Dataset** | SQLiteDS — 800 training examples, 191 forensic artifact categories |
+| **Training Framework** | Hugging Face Transformers |
+| **Best Val Loss** | 0.3043 (7 epochs) |
+| **Model Size (bf16)** | ~6 GB |
+| **Hardware Required** | 16 GB unified memory (Apple M-series) or equivalent GPU |
+
+## Performance
+
+### Overall Results (fixed dataset, n=100, best configuration)
+
+| Metric | Value |
+|---|---|
+| **Overall Accuracy** | **93.0%** (93/100) |
+| 95% CI (Wilson) | [86.3%, 96.6%] |
+| Executable Queries | 94/100 |
+| GPT-4o Accuracy | 95.0% (gap: 4 pp, p ≈ 0.39) |
+| Base Model (no fine-tuning) | 35.0% |
+| Improvement over base | +56 pp |
+
+### Accuracy by Query Difficulty
+
+| Difficulty | Accuracy | n | 95% CI | vs. GPT-4o |
+|---|---|---|---|---|
+| Easy (single-table) | **95.1%** | 39/41 | [83.9%, 98.7%] | 0.0 pp |
+| Medium (joins, aggregation) | **87.5%** | 28/32 | [71.9%, 95.0%] | 0.0 pp |
+| Hard (CTEs, window functions) | **88.9%** | 24/27 | [71.9%, 96.1%] | −3.7 pp |
+
+ForSQLiteLM matches GPT-4o exactly on Easy and Medium queries. The remaining gap
+is concentrated on Hard queries (complex CTEs, window functions, multi-table joins).
+
+### Accuracy by Forensic Domain
+
+| 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
+3 pp higher overall accuracy. Interestingly, including the app name helps iOS
+CoreData schemas (+4 pp) but hurts Hard queries (−7.4 pp); the primary
+configuration without app name is recommended for general use.
+
+### Post-Processing Pipeline Contribution
+
+| Component | Queries saved |
+|---|---|
+| Execution feedback (retry) | 7 |
+| Alias normalization | 18 |
+| Column corrections (Levenshtein) | 2 |
+
+### Training Progression
+
+| Configuration | Val Loss | Accuracy | Δ |
+|---|---|---|---|
+| Base model (no fine-tuning) | — | 35.0% | — |
+| Fine-tuned, no augmentation | — | 68.0% | +33 pp |
+| + Data augmentation (2.4×) | — | 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
+
+### Primary Use Cases
+- Mobile forensics investigations: automated SQL query drafting against seized device databases
+- Integration into forensic tools (FQLite, Autopsy, ALEAPP/iLEAPP workflows)
+- Research in domain-specific Text-to-SQL
+- Educational use for learning forensic database analysis
+
+### Important: This Model is a Drafting Assistant
+
+> **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.)
+
+## How to Use
+
+### Quick Start (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "pawlaszc/ForensicSQL-Llama-3.2-3B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+model.eval()
+
+schema = """
+CREATE TABLE message (
+    ROWID INTEGER PRIMARY KEY,
+    text TEXT,
+    handle_id INTEGER,
+    date INTEGER,
+    is_from_me INTEGER,
+    cache_has_attachments INTEGER
+);
+CREATE TABLE handle (
+    ROWID INTEGER PRIMARY KEY,
+    id TEXT,
+    service TEXT
+);
+"""
+
+request = "Find all messages received in the last 7 days that contain attachments"
+
+# Note: do NOT use apply_chat_template — use plain-text prompt
+prompt = f"""Generate a valid SQLite query for this forensic database request.
+
+Database Schema:
+{schema}
+
+Request: {request}
+
+SQLite Query:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=300,
+        do_sample=False,        # greedy decoding — do not change
+    )
+
+input_length = inputs['input_ids'].shape[1]
+sql = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
+print(sql.strip())
+```
+
+> **Important:** Use plain-text tokenization (do **not** call `apply_chat_template`).
+> The model was trained and evaluated with a plain-text prompt format.
+> Use `do_sample=False` (greedy decoding) for reproducible results.
+
+### Python Helper Class
+
+```python
+class ForensicSQLGenerator:
+    def __init__(self, model_name="pawlaszc/ForensicSQL-Llama-3.2-3B"):
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        import torch
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+        self.model.eval()
+
+    def generate_sql(self, schema: str, request: str) -> str:
+        prompt = (
+            "Generate a valid SQLite query for this forensic database request.\n\n"
+            f"Database Schema:\n{schema}\n\n"
+            f"Request: {request}\n\n"
+            "SQLite Query:\n"
+        )
+        inputs = self.tokenizer(
+            prompt, return_tensors="pt", truncation=True, max_length=4096
+        )
+        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+        input_length = inputs["input_ids"].shape[1]
+
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs, max_new_tokens=300, do_sample=False
+            )
+
+        sql = self.tokenizer.decode(
+            outputs[0][input_length:], skip_special_tokens=True
+        )
+        # Return first statement only, normalized
+        return sql.strip().split("\n")[0].strip().rstrip(";") + ";"
+
+
+# Usage
+generator = ForensicSQLGenerator()
+sql = generator.generate_sql(schema, "Find all unread messages from the last 24 hours")
+print(sql)
+```
+
+### With Ollama / llama.cpp (GGUF)
+
+```bash
+# With llama.cpp
+./llama-cli -m forensic-sql-q4_k_m.gguf \
+  --temp 0 \
+  -p "Generate a valid SQLite query for this forensic database request.
+
+Database Schema:
+CREATE TABLE sms (_id INTEGER PRIMARY KEY, address TEXT, body TEXT, date INTEGER);
+
+Request: Find all messages sent after midnight
+
+SQLite Query:"
+
+# With Ollama — create a Modelfile
+cat > Modelfile << 'EOF'
+FROM ./forensic-sql-q4_k_m.gguf
+PARAMETER temperature 0
+PARAMETER num_predict 300
+EOF
+
+ollama create forensic-sql -f Modelfile
+ollama run forensic-sql
+```
+
+## Training Details
+
+### Dataset — SQLiteDS
+
+- **Total examples:** 1,000 (800 train / 100 val / 100 test), fixed random seed 42
+- **Forensic artifact categories:** 191
+- **Reference query validation:** All 1,000 reference queries validated for execution
+  correctness against in-memory SQLite; 50 queries (5%) corrected before final training
+- **Augmentation:** 3.4× expansion via instruction paraphrasing, WHERE clause reordering,
+  and LIMIT injection — augmented examples confined to training split only
+- **Dataset:** [pawlaszc/mobile-forensics-sql](https://huggingface.co/datasets/pawlaszc/mobile-forensics-sql)
+- **License:** CC BY 4.0
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Training method | Full fine-tune (no LoRA) |
+| Precision | bfloat16 |
+| Epochs | 7 |
+| Learning rate | 2e-5 (peak) |
+| LR scheduler | Cosine with warmup |
+| Batch size | 1 + gradient accumulation 4 |
+| Max sequence length | 4096 |
+| Optimizer | AdamW |
+| Hardware | Apple M-series, 16 GB unified memory |
+| 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,
+   and window functions are the primary remaining challenge.
+3. **Finance & Crypto (81.8%, n=11):** Small test set; confidence intervals are
+   wide. Interpret with caution.
+4. **~1 in 11 error rate:** Approximately 9% of generated queries will contain
+   errors. Expert review of all outputs is required before use in investigations.
+
+### When Human Review is Especially Important
+- Complex multi-table queries with CTEs or window functions
+- Case-critical or court-admissible investigations
+- Any query that will be used to draw conclusions about a suspect
+- Queries involving rare or unusual forensic artifact schemas
+
+## Evaluation
+
+- **Test set:** 100 examples, held-out, seed=42, non-augmented
+- **Metric:** Execution accuracy — query is correct iff it executes without error
+  AND returns a result set identical to the reference query
+- **Reference validation:** All reference queries validated for execution correctness
+  before evaluation; 5 broken queries in the test set were corrected
+- **Evaluation script:** Available in the dataset repository on Zenodo ([DOI])
+
+## Citation
+
+If you use this model or the SQLiteDS dataset in your research, please cite:
+
+```bibtex
+@article{pawlaszczyk2026forsqlitelm,
+  author  = {Dirk Pawlaszczyk},
+  title   = {AI-Based Automated SQL Query Generation for SQLite Databases
+             in Mobile Forensics},
+  journal = {Forensic Science International: Digital Investigation},
+  year    = {2026},
+  note    = {FSIDI-D-26-00029}
+}
+```
+
+## License
+
+Apache 2.0 — following the base Llama 3.2 license terms.
+
+## Acknowledgments
+
+- Base model: Meta's Llama 3.2-3B-Instruct
+- Training framework: Hugging Face Transformers
+- Forensic tool integration: [FQLite](https://github.com/pawlaszczyk/fqlite)
+- Schema sources: iLEAPP, ALEAPP, Autopsy (used under their respective open-source licenses)
+
+## Additional Resources
+
+- **Dataset (Zenodo):** [SQLiteDS — DOI to be added on publication]
+- **Dataset (HuggingFace):** [pawlaszc/mobile-forensics-sql](https://huggingface.co/datasets/pawlaszc/mobile-forensics-sql)
+- **FQLite integration:** [github.com/pawlaszczyk/fqlite](https://github.com/pawlaszczyk/fqlite)
+- **Paper:** FSIDI-D-26-00029 (under review)
+
+---
+
+**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.

chat_template.jinja

@@ -0,0 +1,93 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.3",
+  "unsloth_fixed": true,
+  "use_cache": true,
+  "vocab_size": 128256
+}

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generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.3"
+}

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
+size 17209920

usage_example.md

@@ -0,0 +1,76 @@
+# Quick Start Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    "pawlaszc/DigitalForensicsText2SQLite",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("pawlaszc/DigitalForensicsText2SQLite")
+
+# Example schema
+schema = """
+CREATE TABLE messages (
+    _id INTEGER PRIMARY KEY,
+    address TEXT,
+    body TEXT,
+    date INTEGER,
+    read INTEGER
+);
+"""
+
+# Example request
+request = "Find all unread messages from yesterday"
+
+# Generate SQL
+prompt = f"""Generate a valid SQLite query for this forensic database request.
+
+Database Schema:
+{schema}
+
+Request: {request}
+
+SQLite Query:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+
+# Extract generated SQL
+input_length = inputs['input_ids'].shape[1]
+generated_tokens = outputs[0][input_length:]
+sql = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+print(sql.strip())
+```
+
+## GGUF Usage (llama.cpp)
+
+```bash
+# Download GGUF file (Q4_K_M recommended)
+wget https://huggingface.co/pawlaszc/DigitalForensicsText2SQLite/resolve/main/forensic-sql-q4_k_m.gguf
+
+# Run with llama.cpp
+./llama-cli -m forensic-sql-q4_k_m.gguf -p "Your prompt here"
+```
+
+## Available Files
+
+- **Full model (FP16):** ~6 GB - Best quality
+- **Q4_K_M.gguf:** ~2.3 GB - Recommended (95% quality, 2.5× faster)
+- **Q5_K_M.gguf:** ~2.8 GB - Higher quality (97% quality)
+- **Q8_0.gguf:** ~3.8 GB - Highest quality (99% quality)
+
+## Performance
+
+- Overall: 79% accuracy
+- Easy queries: 94.3%
+- Medium queries: 80.6%
+- Hard queries: 61.8%