Rj18
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text-to-sql-tinyllama-lora

@@ -2,208 +2,66 @@
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 library_name: peft
 pipeline_tag: text-generation
 tags:
 - base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
 - lora
 - sft
 - transformers
 - trl
 ---
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-### Framework versions
-- PEFT 0.18.1

 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 library_name: peft
 pipeline_tag: text-generation
+license: mit
+language:
+- en
+datasets:
+- spider
 tags:
 - base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
 - lora
 - sft
 - transformers
 - trl
+- text-to-sql
+- sql
+- natural-language-processing
+metrics:
+- loss
 ---
+# Text-to-SQL TinyLlama LoRA Adapter
+A fine-tuned LoRA adapter that converts **natural language questions into SQL queries**. Built on top of [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using Supervised Fine-Tuning (SFT) on the Spider benchmark dataset.
 ## Model Details
 ### Model Description
+This is a **LoRA (Low-Rank Adaptation) adapter** fine-tuned to generate SQL queries from natural language questions. Only 0.10% of the base model's parameters were trained, making it extremely lightweight (4.5 MB) while still achieving strong results.
+- **Developed by:** [Rj18](https://huggingface.co/Rj18)
+- **Model type:** Causal Language Model (LoRA Adapter)
+- **Language(s):** English
+- **License:** MIT
+- **Fine-tuned from:** [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
+### Model Sources
+- **Repository:** [https://github.com/18-RAJAT/Interactive-Production-text2sql-Pipeline](https://github.com/18-RAJAT/Interactive-Production-text2sql-Pipeline)
+## How to Use
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load base model and tokenizer
+base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+adapter = "Rj18/text-to-sql-tinyllama-lora"
+tokenizer = AutoTokenizer.from_pretrained(adapter)
+model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16)
+model = PeftModel.from_pretrained(model, adapter)
+model.eval()
+# Generate SQL
+question = "How many employees are in each department?"
+prompt = f"[INST] Generate SQL for the following question.\nQuestion: {question} [/INST]\n"
+inputs = tokenizer(prompt, return_tensors="pt")
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.1)
+sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(sql)