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---
tags:
- text-generation-inference
- transformers
- sft
- chocolatine
license: mit
language:
- fr
---
# Description model
Chocolatine-3B version specialized in French culinary language, supervised fine-tuning of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/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:
```python
from unsloth import FastLanguageModel
model_name = "jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1"
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
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