---
library_name: transformers
license: mit
language:
- fr
- en
tags:
- french
- chocolatine
datasets:
- jpacifico/french-orca-dpo-pairs-revised
pipeline_tag: text-generation
---

### Chocolatine-14B-Instruct-DPO-v1.1

DPO fine-tuned of [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) (14B 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.  
Window context = 4k tokens  

### Benchmarks

The first Chocolatine-14B version is already the best-performing < 50B model in terms of MMLU-PRO on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (august 2024)    
This new version 1.1 is also submitted, results coming soon.  

### MT-Bench

Chocolatine-14B-Instruct-DPO-v1.1 is outperforming Phi-3-medium-4k-instruct and its previous version.  
And also this v1.1 is pretty close from GPT-4o-mini (first turn is amazing!).  

```
########## First turn ##########
                                                     score
model                                         turn        
Chocolatine-14B-Instruct-DPO-v1.1             1     9.1375
gpt-4o-mini                                   1     9.1375
Chocolatine-14B-Instruct-4k-DPO               1     8.7250
Phi-3-medium-4k-instruct                      1     8.7125
Chocolatine-3B-Instruct-DPO-Revised           1     8.4625
Phi-3-mini-4k-instruct                        1     8.4125
gpt-3.5-turbo                                 1     8.2750

########## Second turn ##########
                                                      score
model                                         turn         
gpt-4o-mini                                   2     9.05000
gpt-3.5-turbo                                 2     8.20625
Chocolatine-14B-Instruct-DPO-v1.1             2     8.18750
Chocolatine-14B-Instruct-4k-DPO               2     8.15000
Phi-3-medium-4k-instruct                      2     7.92500
Chocolatine-3B-Instruct-DPO-Revised           2     7.61250
Phi-3-mini-4k-instruct                        2     7.38750

########## Average ##########
                                                  score
model                                                  
gpt-4o-mini                                    9.093750
Chocolatine-14B-Instruct-DPO-v1.1              8.662500
Chocolatine-14B-Instruct-4k-DPO                8.437500
Phi-3-medium-4k-instruct                       8.318750
gpt-3.5-turbo                                  8.240625
Chocolatine-3B-Instruct-DPO-Revised            8.037500
Phi-3-mini-4k-instruct                         7.900000
```

### Usage

You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_14B_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'])
```

### 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