README.md

---

language:
- fr
- en
license: mit
library_name: transformers
tags:
- french
- chocolatine
datasets:
- jpacifico/french-orca-dpo-pairs-revised
pipeline_tag: text-generation
model-index:
- name: Chocolatine-14B-Instruct-DPO-v1.2
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 68.52
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 49.85
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 17.98
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 10.07
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 12.35
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 41.07
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2
      name: Open LLM Leaderboard

---

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# QuantFactory/Chocolatine-14B-Instruct-DPO-v1.2-GGUF
This is quantized version of [jpacifico/Chocolatine-14B-Instruct-DPO-v1.2](https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.2) created using llama.cpp

# Original Model Card


### Chocolatine-14B-Instruct-DPO-v1.2

DPO fine-tuned of [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) (14B 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  

* **4-bit quantized version** available here : [jpacifico/Chocolatine-14B-Instruct-DPO-v1.2-Q4_K_M-GGUF](https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.2-Q4_K_M-GGUF)

### OpenLLM Leaderboard

Chocolatine is the best-performing model in size 13B on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (last update: 2024/10/18)   

![image/png](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Assets/chocolatine_14B_leaderboard_20240901.png?raw=false)  


|      Metric       |Value|
|-------------------|----:|
|**Avg.**               |**33.3**|
|IFEval     |68.52|
|BBH        |49.85|
|MATH Lvl 5 |17.98|
|GPQA       |10.07|
|MuSR       |12.35|
|MMLU-PRO   |41.07|

### MT-Bench-French

Chocolatine-14B-Instruct-DPO-v1.2 outperforms its previous versions and its base model Phi-3-medium-4k-instruct on [MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french), used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench) and GPT-4-Turbo as LLM-judge. 

```
########## First turn ##########
                                             score
model                                 turn        
gpt-4o-mini                           1     9.2875
Chocolatine-14B-Instruct-4k-DPO       1     8.6375
Chocolatine-14B-Instruct-DPO-v1.2     1     8.6125
Phi-3.5-mini-instruct                 1     8.5250
Chocolatine-3B-Instruct-DPO-v1.2      1     8.3750
Phi-3-medium-4k-instruct              1     8.2250
gpt-3.5-turbo                         1     8.1375
Chocolatine-3B-Instruct-DPO-Revised   1     7.9875
Daredevil-8B                          1     7.8875
Meta-Llama-3.1-8B-Instruct            1     7.0500
vigostral-7b-chat                     1     6.7875
Mistral-7B-Instruct-v0.3              1     6.7500
gemma-2-2b-it                         1     6.4500
French-Alpaca-7B-Instruct_beta        1     5.6875
vigogne-2-7b-chat                     1     5.6625

########## 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
Chocolatine-3B-Instruct-DPO-v1.2      2     7.862500
Phi-3-medium-4k-instruct              2     7.750000
Chocolatine-14B-Instruct-4k-DPO       2     7.737500
gpt-3.5-turbo                         2     7.679167
Phi-3.5-mini-instruct                 2     7.575000
Daredevil-8B                          2     7.087500
Meta-Llama-3.1-8B-Instruct            2     6.787500
Mistral-7B-Instruct-v0.3              2     6.500000
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
Chocolatine-14B-Instruct-4k-DPO        8.187500
Chocolatine-3B-Instruct-DPO-v1.2       8.118750
Phi-3.5-mini-instruct                  8.050000
Phi-3-medium-4k-instruct               7.987500
Chocolatine-3B-Instruct-DPO-Revised    7.962500
gpt-3.5-turbo                          7.908333
Daredevil-8B                           7.487500
Meta-Llama-3.1-8B-Instruct             6.918750
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
```

### Usage

You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_14B_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
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jpacifico__Chocolatine-14B-Instruct-DPO-v1.2)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |33.30|
|IFEval (0-Shot)    |68.52|
|BBH (3-Shot)       |49.85|
|MATH Lvl 5 (4-Shot)|17.98|
|GPQA (0-shot)      |10.07|
|MuSR (0-shot)      |12.35|
|MMLU-PRO (5-shot)  |41.07|