language:
- es
license: apache-2.0
library_name: transformers, pe
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-7b
Model Card for Model ID
Model Details
Model Description
This model is a fine-tuned version of google/gemma-7b on the generator dataset.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Developed by: Hacendado and QA-legal-refugees team
Language(s) (NLP): [Spanish]
Finetuned from model [optional]: google/gemma-7b
Uses
Direct Use
The primary objective of this model is to facilitate question answering (QA) tasks pertaining to Spanish refugee legislation. With its refined understanding of the nuances and intricacies of this legal domain
Out-of-Scope Use
Misuse includes any application that promotes unethical practices, misinterprets refugee law, or uses the model for malicious purposes. The model is not designed to replace professional legal advice.
Bias, Risks, and Limitations
The model, while powerful, has limitations inherent to AI, including biases present in the training data. It may not cover all nuances of refugee regulations or adapt to changes in law without updates.
Training Details
Training Data
The dataset used was instruct-legal-refugiados-es We wanted to make a conversation model so we investigated the base model prompt in order to make conversational base on chatml format
we identified the special tokens so the model could understand the different roles in the conversation
Example
<bos><|im_start|>system
You are Gemma.<|im_end|>
<|im_start|>user
Hello, how are you?<|im_end|>
<|im_start|>assistant
I'm doing great. How can I help you today?<|im_end|>\n<eos>
so we used Phil Schmid's gemma chatml tokenizer to adapt our dataset for training
Training Procedure
The training was done using RTX 4090 from Vast.ai with PeRF and Lora
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 66
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3