|
--- |
|
library_name: peft |
|
base_model: codellama/CodeLlama-7b-Instruct-hf |
|
--- |
|
|
|
**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) |
|
|
|
|
|
|
|
**Params** |
|
Training Parameters |
|
| Params | Training Data | Examples | Tokens | LR | |
|
|----------------------------------|---------------------------------|---------------------------------|----------|--------| |
|
| 7B | Pairs synthetic instructions/code | 28907 | 3 031 188 | 1e-5 | |
|
|
|
|
|
**Model Sources** |
|
|
|
|
|
Repository:https://gitlab.com/semantix-labs/generative-ai/lloroConnect |
|
|
|
Dataset Repository: https://gitlab.com/semantix-labs/generative-ai/lloro-datasetsConnect |
|
|
|
|
|
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 | |
|
|
