--- 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](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) based on the official [lexicon](https://www.modernisation.gouv.fr/outils-et-formations/lexique-administratif) published by the French Ministère de la Fonction Publique et de la Réforme de l'Etat. # Data & Training The [dataset](https://huggingface.co/datasets/jpacifico/sft-set-administratif-def-6996) 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: ```python 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