README.md

---
license: mit
base_model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised
pipeline_tag: text-generation
inference: false
model_creator: jpacifico
model_name: Chocolatine-3B-Instruct-DPO-Revised
model_type: phi3
language:
  - fr
  - en
datasets:
- jpacifico/french-orca-dpo-pairs-revised
library_name: transformers
quantized_by: ThiloteE
tags:
  - text-generation-inference
  - transformers
  - GGUF
  - GPT4All-community
  - GPT4All
  - conversational
  - french
  - chocolatine


---

> [!NOTE]
>This is a model that is assumed to perform well, but may require more testing and user feedback. Be aware, only models featured within the GUI of GPT4All, are curated and officially supported by Nomic. Use at your own risk.


# About

<!-- ### quantize_version: 3 -->
<!-- ### convert_type: hf -->


- Static quants of https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised at commit [fa3e742](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised/commit/fa3e742dd80b3f38127fb62f5fc66eaf468fb95c)
- Quantized by [ThiloteE](https://huggingface.co/ThiloteE) with llama.cpp commit [e09a800](https://github.com/ggerganov/llama.cpp/commit/e09a800f9a9b19c73aa78e03b4c4be8ed988f3e6)

These quants were created with a customized configuration that have been proven to not cause visible end of string (eos) tokens during inference with [GPT4All](https://www.nomic.ai/gpt4all).
The config.json, generation_config.json and  tokenizer_config.json differ from the original configuration as can be found in the original model's repository at the time of creation of these quants.


# Prompt Template (for GPT4All)

Example System Prompt:
```
<|system|>
Vous trouverez ci-dessous une instruction décrivant une tâche. Rédigez une réponse qui réponde de manière appropriée à la demande.<|end|>

```

Chat Template:
```
<|user|>
%1<|end|>
<|assistant|>
%2<|end|>

```

# Context Length

`4096`

Use a lower value during inference, if you do not have enough RAM or VRAM.

# Provided Quants


| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/GPT4All-Community/Chocolatine-3B-Instruct-DPO-Revised-GGUF/resolve/main/Chocolatine-3B-Instruct-DPO-Revised-Q4_0.gguf?download=true) | Q4_0 | 2.44 | fast, recommended |




# About GGUF

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/DiscoLM_German_7b_v1-GGUF) for
more details, including on how to concatenate multi-part files.

Here is a handy graph by ikawrakow comparing some quant types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

# Thanks

I thank Mradermacher and TheBloke for Inspiration to this model card and their contributions to open source. Also 3Simplex for lots of help along the way.
Shoutout to the GPT4All and llama.cpp communities :-)


------

<!-- footer end -->
<!-- original-model-card start -->


------
------

# Original Model card:

<!---
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) (august 2024)   

![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


<!-- original-model-card end -->
<!-- end -->