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---
library_name: transformers
license: mit
language:
- fr
- en
tags:
- french
- chocolatine
datasets:
- jpacifico/french-orca-dpo-pairs-revised
pipeline_tag: text-generation
---
# QuantFactory/Chocolatine-3B-Instruct-DPO-Revised-GGUF
This is quantized version of [jpacifico/Chocolatine-3B-Instruct-DPO-Revised](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised) created using llama.cpp
# Original Model Card
### Chocolatine-3B-Instruct-DPO-Revised
DPO fine-tuned of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) (3.82B params)
using the [jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datasets/jpacifico/french-orca-dpo-pairs-revised) rlhf dataset.
Chocolatine is a general model and can itself be finetuned to be specialized for specific use cases.
Window context = 4k tokens
### Benchmarks
The best 3B model on [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (july 2024)
5th best < 30B params (average benchmarks).
### MT-Bench-French
Chocolatine-3B-Instruct-DPO-Revised is outperforming GPT-3.5-Turbo on [MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french) by Bofeng Huang,
used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench)
```
########## First turn ##########
score
model turn
gpt-3.5-turbo 1 8.1375
Chocolatine-3B-Instruct-DPO-Revised 1 7.9875
Daredevil-8B 1 7.8875
Daredevil-8B-abliterated 1 7.8375
Chocolatine-3B-Instruct-DPO-v1.0 1 7.6875
NeuralDaredevil-8B-abliterated 1 7.6250
Phi-3-mini-4k-instruct 1 7.2125
Meta-Llama-3-8B-Instruct 1 7.1625
vigostral-7b-chat 1 6.7875
Mistral-7B-Instruct-v0.3 1 6.7500
Mistral-7B-Instruct-v0.2 1 6.2875
French-Alpaca-7B-Instruct_beta 1 5.6875
vigogne-2-7b-chat 1 5.6625
vigogne-2-7b-instruct 1 5.1375
########## Second turn ##########
score
model turn
Chocolatine-3B-Instruct-DPO-Revised 2 7.937500
gpt-3.5-turbo 2 7.679167
Chocolatine-3B-Instruct-DPO-v1.0 2 7.612500
NeuralDaredevil-8B-abliterated 2 7.125000
Daredevil-8B 2 7.087500
Daredevil-8B-abliterated 2 6.873418
Meta-Llama-3-8B-Instruct 2 6.800000
Mistral-7B-Instruct-v0.2 2 6.512500
Mistral-7B-Instruct-v0.3 2 6.500000
Phi-3-mini-4k-instruct 2 6.487500
vigostral-7b-chat 2 6.162500
French-Alpaca-7B-Instruct_beta 2 5.487395
vigogne-2-7b-chat 2 2.775000
vigogne-2-7b-instruct 2 2.240506
########## Average ##########
score
model
Chocolatine-3B-Instruct-DPO-Revised 7.962500
gpt-3.5-turbo 7.908333
Chocolatine-3B-Instruct-DPO-v1.0 7.650000
Daredevil-8B 7.487500
NeuralDaredevil-8B-abliterated 7.375000
Daredevil-8B-abliterated 7.358491
Meta-Llama-3-8B-Instruct 6.981250
Phi-3-mini-4k-instruct 6.850000
Mistral-7B-Instruct-v0.3 6.625000
vigostral-7b-chat 6.475000
Mistral-7B-Instruct-v0.2 6.400000
French-Alpaca-7B-Instruct_beta 5.587866
vigogne-2-7b-chat 4.218750
vigogne-2-7b-instruct 3.698113
```
### Usage
You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_3B_inference_test_colab.ipynb)
You can also run Chocolatine using the following code:
```python
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
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