ByteForge/Llama_3_8b_Instruct_Text2Sql_FullPrecision_Finetuned

llama3-8b-instruct-text-to-sql

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator dataset.

Training and evaluation data

b-mc2/sql-create-context

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 3
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 6
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 3

Framework versions

• PEFT 0.10.0
• Transformers 4.40.0
• Pytorch 2.2.0+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "ByteForge/Llama_3_8b_Instruct_Text2Sql_FullPrecision_Finetuned"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt="""
CREATE TABLE stadium (
    stadium_id number,
    location text,
    name text,
    capacity number,
    highest number,
    lowest number,
    average number
)

CREATE TABLE singer (
    singer_id number,
    name text,
    country text,
    song_name text,
    song_release_year text,
    age number,
    is_male others
)

CREATE TABLE concert (
    concert_id number,
    concert_name text,
    theme text,
    stadium_id text,
    year text
)

CREATE TABLE singer_in_concert (
    concert_id number,
    singer_id text
)

-- Using valid SQLite, answer the following questions for the tables provided above.

-- What is the maximum, the average, and the minimum capacity of stadiums ? (Generate 1 Sql query. No explaination needed)

answer:
"""

messages = [
    {"role": "system", "content": "You are an text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA."},
    {"role": "user", "content": prompt},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
response = outputs[0]
print(tokenizer.decode(response, skip_special_tokens=True))

#
#system
#You are an text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.
#SCHEMA:
#CREATE TABLE match_season (College VARCHAR, POSITION VARCHAR)
#user
#Which college have both players with position midfielder and players with position defender?
#assistant
#SELECT College FROM match_season WHERE POSITION = "Midfielder" INTERSECT SELECT College FROM match_season WHERE POSITION = "Defender"
#