Update README.md

README.md

@@ -1,16 +1,52 @@
 ---
-language:
-- fr
-license: mit
 tags:
 - text-generation-inference
 - transformers
-- phi
+- sft
 - chocolatine
+license: mit
+language:
+- fr
 ---
 
-# Uploaded  model
+# 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
+
+Fine tuning done efficiently with Unsloth,
+with which I saved processing time on a single T4 GPU (AzureML compute instance).
+
+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.  
+
+# 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:** jpacifico
+- **Developed by:** Jonathan Pacifico, 2024
 - **License:** MIT
-- **Finetuned from model :** Microsoft/Phi-3.5-mini-instruct
+- **Finetuned from model :** microsoft/Phi-3.5-mini-instruct