metadata

base_model: mistralai/Mistral-7B-Instruct-v0.3
datasets:
  - naimsassine/belgian-law-qafrench-dataset
library_name: peft
license: apache-2.0
tags:
  - trl
  - sft
  - generated_from_trainer
model-index:
  - name: mistralinstruct-7b-sft-lora
    results: []

mistralinstruct-7b-sft-lora

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on the Belgian Law QnA dataset. It achieves the following results on the evaluation set:

Loss: 1.2937

Model description

The goal of this model was to experiment how far we can push a model by fine tuning it on a french QnA dataset on Belgian Law. The goal here is to see if we can get a small size LLM to become good enough in terms of Legal Expertise in a specific country

Intended uses & limitations

Legal Question Answering (Belgian Law/French)

Training and evaluation data

SFT-LORA Big thanks to Niels Rogge's notebook that helped me through the process https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Mistral/Supervised_fine_tuning_(SFT)_of_an_LLM_using_Hugging_Face_tooling.ipynb

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 128
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.3021	0.9778	10	1.2937

Framework versions

PEFT 0.12.0
Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1