README.md

---
library_name: transformers
tags:
- chocolatine
license: mit
datasets:
- jpacifico/french-orca-dpo-pairs-revised
language:
- fr
- en
---
### Chocolatine-2-14B

DPO fine-tuning experiment of [sometimesanotion/Lamarck-14B-v0.7](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.7) (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  
*Long-context Support up to 128K tokens and can generate up to 8K tokens.*  

### OpenLLM Leaderboard

coming soon  


### MT-Bench

coming soon

### 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 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, 2025  
- **Model type:** LLM 
- **Language(s) (NLP):** French, English  
- **License:** MIT