---
license: llama3
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- generator
model-index:
- name: Llama-3-8B-Instruct-2-spider-3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Llama-3-8B-Instruct-2-spider-3

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

prompt format: """You are a helpful assistant who answers questions about database tables by responding with SQL queries. Users will provide you with a set of tables represented as CREATE TABLE statements followed by INSERT statements containing information about the data types contained in each of the tables. Then users will ask a question. Answer the user question by writing a SQL statement with the help of the provided SQL tables."""

user_message = """Here is the schema of the tables you will be working with:

{schema}

-- {question} SELECT"""

dataset: VictorDCh/spider-clean-text-to-sql-3

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1

### Training results



### Framework versions

- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2