jpacifico/Chocolatine-2-14B-Instruct-v2.0b2

Chocolatine-2-14B

DPO fine-tuning experiment of sometimesanotion/Lamarck-14B-v0.7 (14B params)
using the 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

You can also run Chocolatine using the following code:

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