README.md

---
library_name: transformers
tags:
- unsloth
- trl
- sft
datasets:
- Clinton/Text-to-sql-v1
language:
- en
base_model:
- unsloth/Meta-Llama-3.1-8B
pipeline_tag: text-generation
license: apache-2.0
---

### Model Description

This model is a fine-tuned version of **`unsloth/Meta-Llama-3.1-8B`** optimized for **Text-to-SQL generation** tasks. The fine-tuning was done using the **Unsloth library** with LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning. The training data consists of the first 5000 rows of the **Clinton/Text-to-sql-v1** dataset.

- **Developed by**: Vedant Rajpurohit  
- **Model type**: Causal Language Model  
- **Language(s)**: English  
- **Fine-tuned from model**: `unsloth/Meta-Llama-3.1-8B`  
- **Model size**: 8.03B parameters  
- **Precision**: BF16  

### 🧠 Model Lineage

This model was fine-tuned by [Vedant Rajpurohit](https://huggingface.co/Vedant3907) using Meta-Llama 3.1-8B and the Clinton/Text-to-sql-v1 dataset.

If you use this model or convert it to GGUF/quantized formats, a mention or backlink is appreciated 💛

For GGUF ports, see:
- [TensorBlock’s GGUF](https://huggingface.co/tensorblock/Vedant3907_Text-to-Sql-llama3.1-8B-GGUF)
- [mradermacher’s i-matrix quant](https://huggingface.co/mradermacher/Text-to-Sql-llama3.1-8B-i1-GGUF)
- [JHJHJHJHJ’s Q5_K_M GGUF](https://huggingface.co/JHJHJHJHJ/Text-to-Sql-llama3.1-8B-Q5_K_M-GGUF)


### Direct Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer from the Hugging Face Hub
model_name = "Vedant3907/Text-to-Sql-llama3.1-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)

model.eval()

# Define your test prompt
sql_prompt = """Below are SQL table schemas paired with instruction that describes a task.
Using valid SQLite, write a response that appropriately completes the request for the provided tables.

### Instruction: What is the 2007 result when the 2010 result was 2r, at the US Open?
### Input: CREATE TABLE table_name_91 ( tournament VARCHAR )
### Response:"""

# Tokenize input
inputs = tokenizer(sql_prompt, return_tensors="pt").to("cuda")

# Generate SQL query
outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    do_sample=True,  # Use sampling for more diverse outputs
)

# Decode and print the generated output
generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Generated SQL Query:")
print(generated_sql)

#SELECT 2007 FROM table_name_91 WHERE 2010 = "2r" AND tournament = "us open"

```

## Bias, Risks, and Limitations

- The model was only trained on first 5000 rows for 250 steps.
- The model may generate incorrect or ambiguous SQL queries for instructions that are unclear or outside the training distribution.

## Training Details

### Dataset
- **Dataset Name**: `Clinton/Text-to-sql-v1`
- **Rows Used**: First 5000 rows of the dataset.

### Training Procedure
The model was fine-tuned using the **Unsloth library** with LoRA adapters, enabling efficient training. Below are the hyperparameters used:

```python
TrainingArguments(
    per_device_train_batch_size = 2,
    gradient_accumulation_steps = 4,
    warmup_steps = 10,  # 4% of 250 steps
    max_steps = 250,
    learning_rate = 1e-4,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 10,
    optim = "adamw_8bit",
    weight_decay = 0.01,
    lr_scheduler_type = "cosine",
    seed = 3407,
    output_dir = "outputs",
    report_to = "none"
)
```


#### Hardware

- Trained on google colab with its T4 GPU