metadata
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 itself based on multiple fine tunings; initialy based on the foundation model Qwen/Qwen2-72B-Instruct
using the 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:
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