Create README.md

README.md

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+
+---
+license: apache-2.0
+base_model: saadxsalman/SS-350M-SQL-Strict
+library_name: llama.cpp
+tags:
+- text-to-sql
+- gguf
+- lfm
+- liquid-ai
+- edge-llm
+- database
+- slm
+datasets:
+- gretelai/synthetic_text_to_sql
+language:
+- en
+---
+
+# SS-350M-SQL-Strict-GGUF
+
+This repository contains the GGUF quantization of [SS-350M-SQL-Strict](https://huggingface.co/saadxsalman/SS-350M-SQL-Strict). 
+
+## Model Summary
+**SS-350M-SQL-Strict-GGUF** is a specialized, ultra-lightweight Small Language Model (SLM) optimized for **Text-to-SQL translation** on edge devices. Built upon the **LiquidAI LFM2.5-350M** architecture, this model is engineered for "Strict" output: it generates **only** raw SQL code, eliminating conversational filler, explanations, or Markdown formatting.
+
+## Technical Specifications
+- **Architecture:** Liquid Foundation Model (LFM) 2.5
+- **Parameters:** 350 Million
+- **Quantization:** Q8_0 (8-bit)
+- **Model Size:** ~370 MB
+- **Context Length:** 32,768 tokens
+- **Inference Engine:** Optimized for `llama.cpp`
+
+## Key Features
+- **Zero Filler:** Returns raw SQL queries immediately (no "Sure, here is your code").
+- **High Speed:** Leverages LFM's linear-complexity architecture for near-instantaneous generation on CPUs.
+- **Low Footprint:** Runs comfortably on devices with < 1GB RAM, making it ideal for mobile or embedded database interfaces.
+
+---
+
+## Prompting Specification (ChatML)
+To ensure the "Strict" behavior and prevent hallucinations, you **must** follow the ChatML prompt format.
+
+### Template
+```text
+<|im_start|>system
+You are a SQL translation engine. Return ONLY raw SQL. Schema: {YOUR_SCHEMA}<|im_end|>
+<|im_start|>user
+{YOUR_QUESTION}<|im_end|>
+<|im_start|>assistant
+```
+
+### Example Input
+**System:** `Table 'employees' (id, name, department, salary)`  
+**User:** `Find the total salary of the 'Sales' department.`
+
+### Example Output
+```sql
+SELECT SUM(salary) FROM employees WHERE department = 'Sales';
+```
+
+---
+
+## Local Deployment with llama.cpp
+
+You can run this model locally using the following command:
+
+```bash
+./llama-cli -m SS-350M-SQL-Strict.Q8_0.gguf \
+  -p "<|im_start|>system\nYou are a SQL engine. Return ONLY raw SQL. Schema: Table 'inventory' (item, quantity)\n<|im_end|>\n<|im_start|>user\nHow many items are in stock?\n<|im_end|>\n<|im_start|>assistant\n" \
+  --temp 0 \
+  -n 128
+```
+
+## Training Logic
+The base model was fine-tuned using **4-bit QLoRA** on the **Gretel Synthetic SQL** dataset. A key differentiator in its training was the use of **Completion-Only Loss masking**, which focused 100% of the model's learning capacity on SQL syntax rather than prompt structure.
+
+## Limitations & Dialect
+- **Dialect:** Defaulted to Standard SQL.
+- **Complexity:** Best suited for schemas with fewer than 20 tables.
+- **Reasoning:** This is a translation engine; it does not "think" step-by-step or explain its logic. If the input is ambiguous, it will attempt the most likely SQL translation.
+
+## Citation
+If you use this model or the underlying LFM architecture, please cite:
+
+```bibtex
+@article{saadsalman2026sqlstrict,
+  author = {Saad Salman},
+  title = {SS-350M-SQL-Strict: Edge-Optimized Text-to-SQL},
+  year = {2026}
+}
+```
+---
+```