Chocolatine-14B-Instruct-DPO-v1.1
DPO fine-tuned of microsoft/Phi-3-medium-4k-instruct (14B params)
using the jpacifico/french-orca-dpo-pairs-revised rlhf dataset.
Training in French also improves the model in English, surpassing the performances of its base model.
Window context = 4k tokens
Benchmarks
The first Chocolatine-14B version is already the best-performing < 50B model in terms of MMLU-PRO on the OpenLLM Leaderboard (august 2024)
This new version 1.1 is also submitted, results coming soon.
MT-Bench
Chocolatine-14B-Instruct-DPO-v1.1 is outperforming Phi-3-medium-4k-instruct and its previous version.
And also this v1.1 is pretty close from GPT-4o-mini (first turn is amazing!).
########## First turn ##########
score
model turn
Chocolatine-14B-Instruct-DPO-v1.1 1 9.1375
gpt-4o-mini 1 9.1375
Chocolatine-14B-Instruct-4k-DPO 1 8.7250
Phi-3-medium-4k-instruct 1 8.7125
Chocolatine-3B-Instruct-DPO-Revised 1 8.4625
Phi-3-mini-4k-instruct 1 8.4125
gpt-3.5-turbo 1 8.2750
########## Second turn ##########
score
model turn
gpt-4o-mini 2 9.05000
gpt-3.5-turbo 2 8.20625
Chocolatine-14B-Instruct-DPO-v1.1 2 8.18750
Chocolatine-14B-Instruct-4k-DPO 2 8.15000
Phi-3-medium-4k-instruct 2 7.92500
Chocolatine-3B-Instruct-DPO-Revised 2 7.61250
Phi-3-mini-4k-instruct 2 7.38750
########## Average ##########
score
model
gpt-4o-mini 9.093750
Chocolatine-14B-Instruct-DPO-v1.1 8.662500
Chocolatine-14B-Instruct-4k-DPO 8.437500
Phi-3-medium-4k-instruct 8.318750
gpt-3.5-turbo 8.240625
Chocolatine-3B-Instruct-DPO-Revised 8.037500
Phi-3-mini-4k-instruct 7.900000
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 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: MIT