library_name: transformers
license: mit
language:
- fr
- en
tags:
- french
- chocolatine
datasets:
- jpacifico/french-orca-dpo-pairs-revised
pipeline_tag: text-generation
Chocolatine-3B-Instruct-DPO-Revised
DPO fine-tuned of microsoft/Phi-3-mini-4k-instruct (3.82B 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
Quantized 4-bit and 8-bit versions are available (see below)
A larger version Chocolatine-14B is also available in its latest version-1.2
Benchmarks
Chocolatine is the best-performing 3B model on the OpenLLM Leaderboard (august 2024)
[Update 2024-08-22] Chocolatine-3B also outperforms Microsoft's new model Phi-3.5-mini-instruct on the average benchmarks of the 3B category.
| Metric | Value |
|---|---|
| Avg. | 27.63 |
| IFEval | 56.23 |
| BBH | 37.16 |
| MATH Lvl 5 | 14.5 |
| GPQA | 9.62 |
| MuSR | 15.1 |
| MMLU-PRO | 33.21 |
MT-Bench-French
Chocolatine-3B-Instruct-DPO-Revised is outperforming GPT-3.5-Turbo on MT-Bench-French, used with multilingual-mt-bench and GPT-4-Turbo as LLM-judge.
Notably, this latest version of the Chocolatine-3B model is approaching the performance of Phi-3-Medium (14B) in French.
########## First turn ##########
score
model turn
gpt-4o-mini 1 9.28750
Chocolatine-14B-Instruct-DPO-v1.2 1 8.61250
Phi-3-medium-4k-instruct 1 8.22500
gpt-3.5-turbo 1 8.13750
Chocolatine-3B-Instruct-DPO-Revised 1 7.98750
Daredevil-8B 1 7.88750
NeuralDaredevil-8B-abliterated 1 7.62500
Phi-3-mini-4k-instruct 1 7.21250
Meta-Llama-3.1-8B-Instruct 1 7.05000
vigostral-7b-chat 1 6.78750
Mistral-7B-Instruct-v0.3 1 6.75000
gemma-2-2b-it 1 6.45000
French-Alpaca-7B-Instruct_beta 1 5.68750
vigogne-2-7b-chat 1 5.66250
########## Second turn ##########
score
model turn
gpt-4o-mini 2 8.912500
Chocolatine-14B-Instruct-DPO-v1.2 2 8.337500
Chocolatine-3B-Instruct-DPO-Revised 2 7.937500
Phi-3-medium-4k-instruct 2 7.750000
gpt-3.5-turbo 2 7.679167
NeuralDaredevil-8B-abliterated 2 7.125000
Daredevil-8B 2 7.087500
Meta-Llama-3.1-8B-Instruct 2 6.787500
Mistral-7B-Instruct-v0.3 2 6.500000
Phi-3-mini-4k-instruct 2 6.487500
vigostral-7b-chat 2 6.162500
gemma-2-2b-it 2 6.100000
French-Alpaca-7B-Instruct_beta 2 5.487395
vigogne-2-7b-chat 2 2.775000
########## Average ##########
score
model
gpt-4o-mini 9.100000
Chocolatine-14B-Instruct-DPO-v1.2 8.475000
Phi-3-medium-4k-instruct 7.987500
Chocolatine-3B-Instruct-DPO-Revised 7.962500
gpt-3.5-turbo 7.908333
Daredevil-8B 7.487500
NeuralDaredevil-8B-abliterated 7.375000
Meta-Llama-3.1-8B-Instruct 6.918750
Phi-3-mini-4k-instruct 6.850000
Mistral-7B-Instruct-v0.3 6.625000
vigostral-7b-chat 6.475000
gemma-2-2b-it 6.275000
French-Alpaca-7B-Instruct_beta 5.587866
vigogne-2-7b-chat 4.218750
Quantized versions
4-bit quantized version is available here : jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q4_K_M-GGUF
8-bit quantized version also available here : jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q8_0-GGUF
Ollama: jpacifico/chocolatine-3b
ollama run jpacifico/chocolatine-3b
Ollama Modelfile example :
FROM ./chocolatine-3b-instruct-dpo-revised-q4_k_m.gguf
TEMPLATE """{{ if .System }}<|system|>
{{ .System }}<|end|>
{{ end }}{{ if .Prompt }}<|user|>
{{ .Prompt }}<|end|>
{{ end }}<|assistant|>
{{ .Response }}<|end|>
"""
PARAMETER stop """{"stop": ["<|end|>","<|user|>","<|assistant|>"]}"""
SYSTEM """You are a friendly assistant called Chocolatine."""
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