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--- |
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tags: |
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- text-generation-inference |
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- transformers |
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- sft |
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- chocolatine |
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license: mit |
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language: |
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- fr |
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--- |
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# Description model |
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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). |
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This model is based on 283 specific terms and definitions of French cuisine. |
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# Fine Tuning |
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For this version of the model I experimented a training method with a double fine-tuning, SFT then DPO. |
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I generated two datasets exclusively for this model, with GPT-4o deployed on Azure OpenAI. |
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The challenge was to achieve a consistent alignment between the two fine-tuning methods. |
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SFT to teach the terms and DPO to reinforce the understanding achieved during the first learning. |
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Fine tuning done efficiently with Unsloth, with which I saved processing time on a single T4 GPU (AzureML compute instance). |
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# Usage |
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The recommended usage is by loading the low-rank adapter using unsloth: |
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```python |
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from unsloth import FastLanguageModel |
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model_name = "jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1" |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name = model_name, |
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max_seq_length = 2048, |
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dtype = None, |
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load_in_4bit = True, |
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) |
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FastLanguageModel.for_inference(model) |
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``` |
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### Limitations |
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The Chocolatine model is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance. |
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It does not have any moderation mechanism. |
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- **Developed by:** Jonathan Pacifico, 2024 |
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- **License:** MIT |
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- **Finetuned from model :** microsoft/Phi-3.5-mini-instruct |
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