README.md

metadata

library_name: transformers
base_model: codellama/CodeLlama-7b-Instruct-hf
license: apache-2.0
datasets:
  - semantixai/Test-Dataset-Lloro
language:
  - pt
tags:
  - code
  - analytics
  - analise-dados
  - portugues-BR

Lloro 7B

Lloro, developed by Semantix Research Labs , is a language Model that was trained to effectively perform Portuguese Data Analysis. It is a fine-tuned version of codellama/CodeLlama-7b-Instruct-hf, that was trained on synthetic datasets . The fine-tuning process was performed using the QLORA metodology on a GPU V100 with 16 GB of RAM.

Model description

Model type: A 7B parameter fine-tuned on synthetic datasets.

Language(s) (NLP): Primarily Portuguese, but the model is capable to understand English as well

Finetuned from model: codellama/CodeLlama-7b-Instruct-hf

What is Lloro's intended use(s)?

Lloro is built for data analysis in Portuguese contexts .

Input : Text

Output : Text (Code)

Usage

Using Transformers

#Import required libraries
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer
)

#Load Model
model_name = "semantixai/LloroV2"
base_model = AutoModelForCausalLM.from_pretrained(
        model_name,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map="auto",
    )

#Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


#Define Prompt
user_prompt = "Desenvolva um algoritmo em Python para calcular a média e a mediana dos preços de vendas por tipo de material do produto."
system = "Provide answers in Python without explanations, only the code"
prompt_template = f"[INST] <<SYS>>\\n{system}\\n<</SYS>>\\n\\n{user_prompt}[/INST]"

#Call the model
input_ids = tokenizer([prompt_template], return_tensors="pt")["input_ids"].to("cuda")

            
outputs = base_model.generate(
    input_ids,
    do_sample=True,
    top_p=0.95,
    max_new_tokens=1024,
    temperature=0.1,
    )

#Decode and retrieve Output
output_text = tokenizer.batch_decode(outputs, skip_prompt=True, skip_special_tokens=False)
display(output_text)

Using an OpenAI compatible inference server (like vLLM)

from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:8000/v1",
)
user_prompt = "Desenvolva um algoritmo em Python para calcular a média e a mediana dos preços de vendas por tipo de material do produto."
completion = client.chat.completions.create(temperature=0.1,frequency_penalty=0.1,model="semantixai/LloroV2",messages=[{"role":"system","content":"Provide answers in Python without explanations, only the code"},{"role":"user","content":user_prompt}])

Params Training Parameters

Params	Training Data	Examples	Tokens	LR
7B	Pairs synthetic instructions/code	28907	3 031 188	1e-5

Model Sources

Test Dataset Repository: https://huggingface.co/datasets/semantixai/Test-Dataset-Lloro

Model Dates Lloro was trained between November 2023 and January 2024.

Performance

Modelo	LLM as Judge	Code Bleu Score	Rouge-L	CodeBert- Precision	CodeBert-Recall	CodeBert-F1	CodeBert-F3
GPT 3.5	91.22%	0.2745	0.2189	0.7502	0.7146	0.7303	0.7175
Instruct -Base	97.40%	0.2487	0.1146	0.6997	0.6473	0.6713	0.6518
Instruct -FT	97.76%	0.3264	0.3602	0.7942	0.8178	0.8042	0.8147

Training Infos: The following hyperparameters were used during training:

Parameter	Value
learning_rate	1e-5
weight_decay	0.0001
train_batch_size	1
eval_batch_size	1
seed	42
optimizer	Adam - paged_adamw_32bit
lr_scheduler_type	cosine
lr_scheduler_warmup_ratio	0.03
num_epochs	5.0

QLoRA hyperparameters The following parameters related with the Quantized Low-Rank Adaptation and Quantization were used during training:

Parameter	Value
lora_r	16
lora_alpha	64
lora_dropout	0.1
storage_dtype	"nf4"
compute_dtype	"float16"

Experiments

Model	Epochs	Overfitting	Final Epochs	Training Hours	CO2 Emission (Kg)
Code Llama Instruct	1	No	1	8.1	1.337
Code Llama Instruct	5	Yes	3	45.6	9.12

Framework versions

Library	Version
bitsandbytes	0.40.2
Datasets	2.14.3
Pytorch	2.0.1
Tokenizers	0.14.1
Transformers	4.34.0