library_name: transformers
base_model: codellama/CodeLlama-7b-Instruct-hf
license: llama2
datasets:
- semantixai/LloroV3
language:
- pt
tags:
- code
- analytics
- analise-dados
- portugues-BR
co2_eq_emissions:
emissions: 1320
source: >-
Lacoste, Alexandre, et al. “Quantifying the Carbon Emissions of Machine
Learning.” ArXiv (Cornell University), 21 Oct. 2019,
https://doi.org/10.48550/arxiv.1910.09700.
training_type: fine-tuning
geographical_location: Council Bluffs, Iowa, USA.
hardware_used: 1 A100 40GB GPU
Lloro 7B
Lloro, developed by Semantix Research Labs , is a language Model that was trained to effectively perform Portuguese Data Analysis in Python. 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 A100 with 40 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/Lloro",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 | 74222 | 9 351 532 | 2e-4 |
Model Sources
Test Dataset Repository: https://huggingface.co/datasets/semantixai/LloroV3
Model Dates: Lloro was trained between February 2024 and April 2024.
Performance
| Modelo | LLM as Judge | Code Bleu Score | Rouge-L | CodeBert- Precision | CodeBert-Recall | CodeBert-F1 | CodeBert-F3 |
|---|---|---|---|---|---|---|---|
| GPT 3.5 | 94.29% | 0.3538 | 0.3756 | 0.8099 | 0.8176 | 0.8128 | 0.8164 |
| Instruct -Base | 88.77% | 0.3666 | 0.3351 | 0.8244 | 0.8025 | 0.8121 | 0.8052 |
| Instruct -FT | 97.95% | 0.5967 | 0.6717 | 0.9090 | 0.9182 | 0.9131 | 0.9171 |
Training Infos: The following hyperparameters were used during training:
| Parameter | Value |
|---|---|
| learning_rate | 2e-4 |
| weight_decay | 0.0001 |
| train_batch_size | 7 |
| eval_batch_size | 7 |
| seed | 42 |
| optimizer | Adam - paged_adamw_32bit |
| lr_scheduler_type | cosine |
| lr_scheduler_warmup_ratio | 0.06 |
| num_epochs | 4.0 |
QLoRA hyperparameters The following parameters related with the Quantized Low-Rank Adaptation and Quantization were used during training:
| Parameter | Value |
|---|---|
| lora_r | 64 |
| lora_alpha | 256 |
| lora_dropout | 0.1 |
| storage_dtype | "nf4" |
| compute_dtype | "bfloat16" |
Experiments
| Model | Epochs | Overfitting | Final Epochs | Training Hours | CO2 Emission (Kg) |
|---|---|---|---|---|---|
| Code Llama Instruct | 1 | No | 1 | 3.01 | 0.43 |
| Code Llama Instruct | 4 | Yes | 3 | 9.25 | 1.32 |
Framework versions
| Library | Version |
|---|---|
| bitsandbytes | 0.40.2 |
| Datasets | 2.14.3 |
| Pytorch | 2.0.1 |
| Tokenizers | 0.14.1 |
| Transformers | 4.34.0 |