---
datasets:
- ekshat/text-2-sql-with-context
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- text-2-sql
- text-generation
- text2sql
---
# Introduction
Our Model is fine-tuned on Llama-2 7B model on Text-2-SQL Dataset based on Alpaca format described by Stanford. We have used QLora, Bits&Bytes, Accelerate and Transformers Library to implement PEFT concept.
For more information, please visit [github.com/akshayhedaoo1](https://github.com/akshayhedaoo1/Llama-2-7b-chat-finetune-for-text2sql/tree/Data-Science)


# Inference
```python
!pip install transformers accelerate xformers bitsandbytes

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")

# Loading model in 4 bit precision
model = AutoModelForCausalLM.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql", load_in_4bit=True)

context = "CREATE TABLE head (name VARCHAR, born_state VARCHAR, age VARCHAR)"
question = "List the name, born state and age of the heads of departments ordered by age."

prompt = f"""Below is an context that describes a sql query, paired with an question that provides further information. Write an answer that appropriately completes the request.
### Context:
{context}
### Question:
{question}
### Answer:"""

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(prompt)
print(result[0]['generated_text'])
```


# Model Information
- **model_name = "NousResearch/Llama-2-7b-chat-hf"**

- **dataset_name = "ekshat/text-2-sql-with-context"**


# QLoRA parameters
- **lora_r = 64**

- **lora_alpha = 16**

- **lora_dropout = 0.1**


# BitsAndBytes parameters
- **use_4bit = True**

- **bnb_4bit_compute_dtype = "float16"**

- **bnb_4bit_quant_type = "nf4"**

- **use_nested_quant = False**


# Training Arguments parameters
- **num_train_epochs = 1**

- **fp16 = False**

- **bf16 = False**

- **per_device_train_batch_size = 8**

- **per_device_eval_batch_size = 4**

- **gradient_accumulation_steps = 1**

- **gradient_checkpointing = True**

- **max_grad_norm = 0.3**

- **learning_rate = 2e-4**

- **weight_decay = 0.001**

- **optim = "paged_adamw_32bit"**

- **lr_scheduler_type = "cosine"**

- **max_steps = -1**

- **warmup_ratio = 0.03**

- **group_by_length = True**

- **save_steps = 0**

- **logging_steps = 25**


# SFT parameters
- **max_seq_length = None**

- **packing = False**