Digita has been fine-tuned (qlora) on a proprietary dataset so as to respond
similarly to the Belgian State Archive customer support. It was essentially
trained using data from the "digit" mailbox from the DiVa section.
Model Details
Model Description
Digita has been trained in the context of the
Arkey project
conjointly held by the Belgian State Archive and UCLouvain. The purpose of this
project is to ease access to the archives held at both institutions for the
general public.
In that context, Digita has been trained as an experiment to tune a chatbot
that would be able to talk and reply to user request in a way that feels
similar to the actual people dealing with the matter at the BSA.
Prototype a chatbot to help the users of the Belgian State Archive in the
context of the Arkey project.
Limitations
This model has been fine tuned on data from the 2022-2024 period. Which means
the majority of the data predates the introduction of the AGATHA platform.
This means that, while some of the info provided by this model can be useful,
it is essentially going to be stale at this point.
The second major limitation I envision for this model stems from the sheer size
of the model: 8b is probably not going to cut it for a majority of cases.
Especially if one envisions to use the quantized versions.
However, I think that this model is a good proof of concept and that it shows
what could be achieved if resources were allocated to perform the same task
on a more up-to-date dataset (or limited to 2024) using a larger model.
This was however not feasible on my personal machine.
Recommendations
If anyone is serious about using this model as a basis to help the BSA personnel
of to deploy an user facing chatbot at the BSA, I would recommend to completely
redo all the finetuning using a larger model and an updated (more recent)
version of the dataset.
How to Get Started with the Model
I do recommend that you use the QLoRA adapter directly using unsloth as this is
the asset that seemed to yield the best result while maintaining a similar
VRAM footprint (about 7G). Here is how you get started:
from unsloth import (
FastLanguageModel,
train_on_responses_only,
)
from unsloth.chat_templates import (
get_chat_template,
)
from transformers import TextStreamer
model, tok = FastLanguageModel.from_pretrained("xaviergillard/digita", load_in_4bit=True)
model = FastLanguageModel.for_inference(model)
Training Details
Training Data
Original training data consists of a curated dataset of response emails sent
by the DiVa cusomer service. The details of this dataset will not be disclosed.
Training Procedure
Preprocessing
Cleaning up the encoding
Conversation restructuration
Conversation classification
Data Augmentation
Filtering
Training Hyperparameters
Training regime: QLoRA adapter based on a 4bit quantization of Llama-3.1-8b-Instruct by unsloth.
