sachiin051
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Text2SQL-LoRA

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 ---
-base_model: Qwen/Qwen2.5-Coder-1.5B
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-Coder-1.5B
-- lora
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.21.2

 ---
 library_name: peft
+base_model: Qwen/Qwen2.5-Coder-1.5B
+pipeline_tag: text-to-sql
 tags:
+  - text-to-sql
+  - sql
+  - lora
+  - qwen2.5-coder
+  - fine-tuned
 ---
+# Text2SQL-LoRA
+A parameter-efficient fine-tuned language model designed for Text-to-SQL generation.
+This project fine-tunes **Qwen2.5-Coder-1.5B** using Low-Rank Adaptation (LoRA) to explore natural-language-to-SQL generation.
 ## Model Details
+- **Base Model:** Qwen/Qwen2.5-Coder-1.5B
+- **Fine-tuning Method:** LoRA
+- **LoRA Rank:** 16
+- **LoRA Alpha:** 32
+- **LoRA Dropout:** 0.05
+- **Task:** Natural Language to SQL
+- **Model Format:** PEFT Adapter
+## Intended Use
+This model is intended for:
+- Research and experimentation with Text-to-SQL generation
+- Converting natural-language questions into SQL queries
+- Educational purposes and fine-tuning experiments
+## Model Loading
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+base_model = "Qwen/Qwen2.5-Coder-1.5B"
+adapter_model = "YOUR_HF_USERNAME/Text2SQL-LoRA"
+tokenizer = AutoTokenizer.from_pretrained(adapter_model)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(
+    model,
+    adapter_model
+)
+prompt = "Convert the following question into SQL: Show all employees with salary greater than 50000."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=128,
+    do_sample=False
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+Note: The exact inference prompt should match the prompt format used during training.
+## Training Configuration
+| Parameter | Value |
+|---|---|
+| Base Model | Qwen2.5-Coder-1.5B |
+| LoRA Rank | 16 |
+| LoRA Alpha | 32 |
+| Dropout | 0.05 |
+| Task Type | Causal Language Modeling |
+## Dataset
+Training dataset details will be documented here.
 ## Evaluation
+Evaluation metrics and benchmark results will be added after validation.
+## Limitations
+- Generated SQL may contain syntax errors or incorrect logic.
+- Queries may not match the target database schema.
+- Results depend on the training dataset and prompt format.
+- Generated SQL should be validated before execution against real databases.
+## Acknowledgements
+Built using the Qwen2.5-Coder model and the PEFT library.