---
library_name: transformers
tags:
- code
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---

# Model Card for Model ID

This model is trained on generating SQL code from user prompts.


## Model Details

### Model Description

This model is traiend for generating SQL code from user prompts. The prompt structure is based on this format. 
###Question ###Context[SQL code of your table ] ###Answer: 
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:** Ali Bidaran
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Gemma 2B

### Model Sources [optional]




### Direct Use

```python 

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer

model_id = "Gemma2_SQLGEN"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0})
tokenizer.padding_side = 'right'
)
from peft import LoraConfig, PeftModel, get_peft_model
from trl import SFTTrainer

prompt = "find unique items from  name coloum."
text=f"<s>##Question: {prompt} \n ##Context: CREATE TABLE head (head_id VARCHAR, name VARCHAR) \n ##Answer:"
inputs=tokenizer(text,return_tensors='pt').to('cuda')
outputs=model.generate(**inputs,max_new_tokens=400,do_sample=True,top_p=0.92,top_k=10,temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```