tags:
- text-generation-inference
- transformers
- sft
- chocolatine
license: mit
language:
- fr
datasets:
- jpacifico/sft-set-administratif-def-6996
Description model
Chocolatine-3B version specialized in French administrative language, supervised fine-tuning of microsoft/Phi-3.5-mini-instruct
based on the official lexicon
published by the French Ministère de la Fonction Publique et de la Réforme de l'Etat.
Data & Training
The dataset gathers 2362 administrative terms constituting the basis of the simulation of prompt-answer pairs.
The GPT-4o model deployed on Azure OpenAI was used to carry out the building of the dataset in several phases:
- Extraction of the lexicon pages converted into jpg images
- Reformulation of the definitions to make them more readable and natural to be used by an LLM in order to ensure high quality data.
- Generation of questions from the terms and definitions
- Generation of answers in three successive rounds taking into account the previous generations to ensure variety.
Fine tuning (SFT) done efficiently with Unsloth,
with which I saved processing time on a single T4 GPU (Compute instance from Azure ML).
Usage
The recommended usage is by loading the low-rank adapter using unsloth:
from unsloth import FastLanguageModel
model_name = "jpacifico/chocolatine-admin-3B-sft-v0.2"
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_name,
max_seq_length = 2048,
dtype = None,
load_in_4bit = True,
)
FastLanguageModel.for_inference(model)
Limitations
The Chocolatine model is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanism.
- Developed by: Jonathan Pacifico, 2024
- License: MIT
- Finetuned from model : microsoft/Phi-3.5-mini-instruct