---
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
- text-to-sql
- Spider
base_model: VictorDCh/Llama-3-8B-Instruct-MoE-2
datasets:
- generator
- Hexamind/spider-clean-text-to-sql
model-index:
- name: Llama-3-8B-Instruct-MoE-2-spider
  results: []
language:
- en
---

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# Llama-3-8B-Instruct-MoE-2-spider

This model is a fine-tuned version of [VictorDCh/Llama-3-8B-Instruct-MoE-2](https://huggingface.co/VictorDCh/Llama-3-8B-Instruct-MoE-2) on the generator dataset.

## Model description

This is a mixture of experts merged with mergekit from 4 experts (Llama 3 8B Instruct), fine-tuned on the Spider train dataset.

## Intended uses & limitations

This is part of a research project on using open-source models for text-to-SQL use cases.

Some pre-processing of the input data, and post-processing of the results may be necessary to obtain optimal results.

The input prompt uses the following system-user-assistant format:

- system message: """You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA. Only generate SQL.
    SCHEMA:
    {schema}"""
- user message: """Here is the user question in natural language: {question} """
- assistant message (empty when testing): {SQL query}


## Training and evaluation data

The training and testing dataset can be found here: [Cleaned Spider dataset](https://huggingface.co/datasets/Hexamind/spider-clean-text-to-sql).

## Training procedure

One epoch of SFT on the train dataset as explained above.

### 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

### Framework versions

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