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---
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 : 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** |