---
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](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.  
Training in French also improves the model in English, surpassing the performances of its base model.  
Window context = 4k tokens  

### Benchmarks

Chocolatine is the **best-performing 3B model** on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (july 2024)  
5th best < 30B params (average benchmarks).   

![image/png](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Assets/openllm_choco3b_revised.png?raw=false)


|      Metric       |Value|
|-------------------|----:|
|Avg.               |27.63|
|IFEval (0-Shot)    |56.23|
|BBH (3-Shot)       |37.16|
|MATH Lvl 5 (4-Shot)|14.5|
|GPQA (0-shot)      |9.62|
|MuSR (0-shot)      |15.1|
|MMLU-PRO (5-shot)  |33.21|


### 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'])
```

* **4-bit quantized version** is available here : [jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q4_K_M-GGUF](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q4_K_M-GGUF)

* **Ollama**: [jpacifico/chocolatine-3b](https://ollama.com/jpacifico/chocolatine-3b)

```bash
ollama run jpacifico/chocolatine-3b
```

Ollama *Modelfile* example :

```bash
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."""
```

### 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