Duplicate from jpacifico/Chocolatine-3B-Instruct-DPO-v1.2

Co-authored-by: Jonathan Pacifico <jpacifico@users.noreply.huggingface.co>

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README.md

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+---
+library_name: transformers
+license: mit
+language:
+- fr
+- en
+tags:
+- french
+- chocolatine
+datasets:
+- jpacifico/french-orca-dpo-pairs-revised
+pipeline_tag: text-generation
+---
+
+### Chocolatine-3B-Instruct-DPO-v1.2
+
+Best version of Chocolatine-3B for French.  
+*The model supports 128K context length*.  
+
+DPO fine-tuned of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) (3.82B params)  
+using the [jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datasets/jpacifico/french-orca-dpo-pairs-revised) rlhf dataset.  
+Training in French also improves the model in English, surpassing the performances of its base model.  
+
+
+### MT-Bench-French
+
+Chocolatine-3B-Instruct-DPO-v1.2 is outperforming Phi-3-medium-4k-instruct (14B) and its base model Phi-3.5-mini-instruct on [MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french), used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench) and GPT-4-Turbo as LLM-judge.   
+
+```
+########## First turn ##########
+                                             score
+model                                 turn        
+gpt-4o-mini                           1     9.2875
+Chocolatine-14B-Instruct-4k-DPO       1     8.6375
+Chocolatine-14B-Instruct-DPO-v1.2     1     8.6125
+Phi-3.5-mini-instruct                 1     8.5250
+Chocolatine-3B-Instruct-DPO-v1.2      1     8.3750
+Phi-3-medium-4k-instruct              1     8.2250
+gpt-3.5-turbo                         1     8.1375
+Chocolatine-3B-Instruct-DPO-Revised   1     7.9875
+Daredevil-8B                          1     7.8875
+Meta-Llama-3.1-8B-Instruct            1     7.0500
+vigostral-7b-chat                     1     6.7875
+Mistral-7B-Instruct-v0.3              1     6.7500
+gemma-2-2b-it                         1     6.4500
+French-Alpaca-7B-Instruct_beta        1     5.6875
+vigogne-2-7b-chat                     1     5.6625
+
+########## Second turn ##########
+                                               score
+model                                 turn          
+gpt-4o-mini                           2     8.912500
+Chocolatine-14B-Instruct-DPO-v1.2     2     8.337500
+Chocolatine-3B-Instruct-DPO-Revised   2     7.937500
+Chocolatine-3B-Instruct-DPO-v1.2      2     7.862500
+Phi-3-medium-4k-instruct              2     7.750000
+Chocolatine-14B-Instruct-4k-DPO       2     7.737500
+gpt-3.5-turbo                         2     7.679167
+Phi-3.5-mini-instruct                 2     7.575000
+Daredevil-8B                          2     7.087500
+Meta-Llama-3.1-8B-Instruct            2     6.787500
+Mistral-7B-Instruct-v0.3              2     6.500000
+vigostral-7b-chat                     2     6.162500
+gemma-2-2b-it                         2     6.100000
+French-Alpaca-7B-Instruct_beta        2     5.487395
+vigogne-2-7b-chat                     2     2.775000
+
+########## Average ##########
+                                          score
+model                                          
+gpt-4o-mini                            9.100000
+Chocolatine-14B-Instruct-DPO-v1.2      8.475000
+Chocolatine-14B-Instruct-4k-DPO        8.187500
+Chocolatine-3B-Instruct-DPO-v1.2       8.118750
+Phi-3.5-mini-instruct                  8.050000
+Phi-3-medium-4k-instruct               7.987500
+Chocolatine-3B-Instruct-DPO-Revised    7.962500
+gpt-3.5-turbo                          7.908333
+Daredevil-8B                           7.487500
+Meta-Llama-3.1-8B-Instruct             6.918750
+Mistral-7B-Instruct-v0.3               6.625000
+vigostral-7b-chat                      6.475000
+gemma-2-2b-it                          6.275000
+French-Alpaca-7B-Instruct_beta         5.587866
+vigogne-2-7b-chat                      4.218750
+```
+
+### Usage
+
+You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_3B_inference_test_colab.ipynb) 
+
+You can also run Chocolatine using the following code:
+
+```python
+import transformers
+from transformers import AutoTokenizer
+
+# Format prompt
+message = [
+    {"role": "system", "content": "You are a helpful assistant chatbot."},
+    {"role": "user", "content": "What is a Large Language Model?"}
+]
+tokenizer = AutoTokenizer.from_pretrained(new_model)
+prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
+
+# Create pipeline
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=new_model,
+    tokenizer=tokenizer
+)
+
+# Generate text
+sequences = pipeline(
+    prompt,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    num_return_sequences=1,
+    max_length=200,
+)
+print(sequences[0]['generated_text'])
+```
+
+* **4-bit quantized version** is available here : [jpacifico/Chocolatine-3B-Instruct-DPO-v1.2-Q4_K_M-GGUF](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-v1.2-Q4_K_M-GGUF)
+
+### 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
+- **Model type:** LLM 
+- **Language(s) (NLP):** French, English
+- **License:** MIT

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