jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1
Description model
Chocolatine-3B version specialized in French culinary language, fine-tuning of microsoft/Phi-3.5-mini-instruct. This model is based on 283 specific terms and definitions of French cuisine.
Fine Tuning
For this version of the model I experimented a training method with a double fine-tuning, SFT then DPO. I generated two datasets exclusively for this model, with GPT-4o deployed on Azure OpenAI. The challenge was to achieve a consistent alignment between the two fine-tuning methods. SFT to teach the terms and DPO to reinforce the understanding achieved during the first learning.
Fine tuning done efficiently with Unsloth, with which I saved processing time on a single T4 GPU (AzureML compute instance).
Usage
The recommended usage is by loading the low-rank adapter using unsloth:
from unsloth import FastLanguageModel
from transformers import TextStreamer
import torch
model_name = "jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1"
model, tokenizer = FastLanguageModel.from_pretrained(
model_name,
max_seq_length=2048,
dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
load_in_4bit=False
)
FastLanguageModel.for_inference(model)
model.eval()
def generate_response(user_question: str):
messages = [
{"role": "system", "content": "Tu es un assistant IA spécialisé dans le langage culinaire français. Une question te sera posée. Tu dois générer une réponse précise et concise."},
{"role": "user", "content": "En cuisine "+user_question},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to("cuda")
attention_mask = (inputs != tokenizer.pad_token_id).long()
text_streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.no_grad():
_ = model.generate(
input_ids=inputs,
attention_mask=attention_mask,
max_new_tokens=128,
use_cache=True,
streamer=text_streamer,
do_sample=False,
temperature=0.7,
)Limitations
The Chocolatine model series 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