metadata
language:
- fr
- en
license: apache-2.0
library_name: transformers
tags:
- lucie
- dpo
- llama
- math
datasets:
- jpacifico/french-orca-dpo-pairs-revised
Distilucie-7B-Math-Instruct-DPO-v0.1
Post-training optimization of the model OpenLLM-France/Lucie-7B-Instruct-v1.1
DPO fine-tuning using the dataset argilla/distilabel-math-preference-dpo
Training set to 5 full epochs
Lucie-7B has a context size of 32K tokens
OpenLLM Leaderboard
TBD.
Usage
You can run the model using this Colab notebook
You can also run Distilucie 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
This Distilucie model is a quick demonstration that the Lucie foundation 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