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))

RichardErkhov/alibidaran_-_Gemma2_SQLGEN-awq

Original model description:

Model Card for Model ID

Model Details

Model Description

Model Sources [optional]

Direct Use

RichardErkhov/
alibidaran_-_Gemma2_SQLGEN-awq