---
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**

<img src="https://cdn-uploads.huggingface.co/production/uploads/653176dc69fffcfe1543860a/h0kNd9OTEu1QdGNjHKXoq.png" width="300" alt="Lloro-7b Logo"/>


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
```python
#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](https://docs.vllm.ai/en/latest/index.html))
```python
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    |