README.md

---
language:
- fr
- en
license: apache-2.0
library_name: transformers
tags:
- chocolatine
datasets:
- jpacifico/french-orca-dpo-pairs-revised
pipeline_tag: text-generation
---

### Chocolatine-78B-Instruct-DPO-v1.3

DPO fine-tuned of [dfurman/CalmeRys-78B-Orpo-v0.1](https://huggingface.co/dfurman/CalmeRys-78B-Orpo-v0.1) itself based on multiple fine tunings; initialy based on the foundation model [Qwen/Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct)  
using the [jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datasets/jpacifico/french-orca-dpo-pairs-revised) rlhf dataset.  

My goal here is to verify whether the French DPO fine-tuning I developed for my Chocolatine model series can be applied with equal performance to model sizes > 70B params,  
especially if it can be combined with several previous fine-tunings.  


### OpenLLM Leaderboard

Coming soon.  

### Usage

You can 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 series 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:** Apache 2.0

Made with ❤️ in France