---
base_model: codellama/CodeLlama-7b-Instruct-hf
license: llama2
datasets:
- semantixai/LloroV3
language:
- pt
tags:
- 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**

<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 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)

**V3 Release**
- Context Lenght increased to 2048.
- Fine-tuning dataset increased to 74222 examples.

**Usage**

Using Transformers

```python
#Import required libraries
import torch
)

#Load Model
model_name = "semantixai/Lloro"
base_model = AutoModelForCausalLM.from_pretrained(
        model_name,
        return_dict=True,
    input_ids,
    do_sample=True,
    top_p=0.95,
    max_new_tokens=2048,
    temperature=0.1,
    )

```

Using an OpenAI compatible inference server (like [vLLM](https://docs.vllm.ai/en/latest/index.html))

```python
from openai import OpenAI

    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**

| Package       | Version   |
|---------------|-----------|
| Datasets      | 2.14.3    |
| Pytorch       | 2.0.1     |
| Tokenizers    | 0.14.1    |
| Transformers  | 4.34.0    |