Add usage examples

usage_example.md

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+# 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%