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---
library_name: transformers
tags:
- chocolatine
license: apache-2.0
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:** Apache-2.0 |