Update README.md

README.md

@@ -8,197 +8,124 @@ base_model:
 - meta-llama/Llama-3.2-1B-Instruct
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
 
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
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-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
 
-#### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+# Text-to-SQL Model Usage
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
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-
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
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-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+## Model Details
+- Base Model: `meta-llama/Llama-3.2-1B-Instruct`
+- Fine-tuned Model: `pavan-naik/Llama-3.2-1B-Instruct-Text-to-SQL`
+- Task: Text to SQL Query Generation
+- Framework: PyTorch with 🤗 Transformers and PEFT
+
+## Installation
+
+```bash
+pip install peft transformers bitsandbytes
+```
+
+## Required Imports
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+```
+
+## Loading the Model
+
+### 1. Configure Quantization (Optional)
+```python
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16
+)
+```
+
+### 2. Load Base Model and Tokenizer
+```python
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.2-1B-Instruct",
+    device_map="auto"
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    "pavan-naik/Llama-3.2-1B-Instruct-Text-to-SQL",
+    trust_remote_code=True
+)
+tokenizer.pad_token = tokenizer.eos_token
+```
+
+### 3. Load PEFT Adapter
+```python
+model = PeftModel.from_pretrained(base_model, "pavan-naik/Llama-3.2-1B-Instruct-Text-to-SQL")
+```
+
+## Generating SQL Queries
+
+### Prompt Template
+```python
+sql_prompt_template = """You are a database management system expert, proficient in Structured Query Language (SQL).
+Your job is to write an SQL query that answers the following question, based on the given database schema and any additional information provided.  Use SQLite syntax.
+Please output only SQL (without any explanations).
+### Question: {question}
+### Schema: {context}
+### Completion: """
+```
+
+### Generation Function
+```python
+def generate_sql(question, context, model, tokenizer, max_length=128):
+    prompt = sql_prompt_template.format(question=question, context=context)
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=max_length)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    
+    prompt_length = len(inputs["input_ids"][0])
+    outputs = model.generate(
+        **inputs,
+        max_length=prompt_length + max_length,
+        num_return_sequences=1,
+        temperature=0.7,
+        do_sample=True,
+    )
+    
+    sql_answer = tokenizer.decode(outputs[0][prompt_length:], skip_special_tokens=True).strip()
+    return sql_answer
+```
+
+## Example Usage
+
+```python
+# Define your question and database schema
+question = "For each continent, show the city with the highest population and what percentage of its country's total population it represents"
+context = """
+CREATE TABLE city (city_id INTEGER, name VARCHAR, population INTEGER, country_id INTEGER);
+CREATE TABLE country (country_id INTEGER, name VARCHAR, continent VARCHAR)
+"""
+
+# Generate SQL query
+sql_query = generate_sql(question, context, model, tokenizer)
+print(sql_query)
+```
+
+## Notes
+- The model uses SQLite syntax
+- Adjust `max_length` parameter based on your query complexity
+- Temperature can be modified to control randomness in generation (0.0 for deterministic output)
+- The model performs best with clear schema definitions and well-structured questions
+
+## Requirements
+- Python 3.7+
+- PyTorch
+- Transformers
+- PEFT (Parameter-Efficient Fine-Tuning)
+- bitsandbytes (for quantization)