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--- |
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library_name: transformers |
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license: mit |
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language: |
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- fr |
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- en |
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tags: |
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- french |
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- chocolatine |
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datasets: |
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- jpacifico/french-orca-dpo-pairs-revised |
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pipeline_tag: text-generation |
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--- |
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### Chocolatine-14B-Instruct-DPO-v1.2 |
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DPO fine-tuned of [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) (14B params) |
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using the [jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datasets/jpacifico/french-orca-dpo-pairs-revised) rlhf dataset. |
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Training in French also improves the model in English, surpassing the performances of its base model. |
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Window context = 4k tokens |
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* **4-bit quantized version** is 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) |
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### OpenLLM Leaderboard |
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Chocolatine is the best-performing 14B model on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (2024/09/01) |
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and even the number one of the < 22B params models |
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 |
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| Metric |Value| |
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|-------------------|----:| |
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|**Avg.** |**33.3**| |
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|IFEval |68.52| |
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|BBH |49.85| |
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|MATH Lvl 5 |17.98| |
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|GPQA |10.07| |
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|MuSR |12.35| |
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|MMLU-PRO |41.07| |
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### MT-Bench-French |
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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. |
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``` |
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########## First turn ########## |
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score |
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model turn |
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gpt-4o-mini 1 9.2875 |
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Chocolatine-14B-Instruct-4k-DPO 1 8.6375 |
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Chocolatine-14B-Instruct-DPO-v1.2 1 8.6125 |
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Phi-3.5-mini-instruct 1 8.5250 |
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Chocolatine-3B-Instruct-DPO-v1.2 1 8.3750 |
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Phi-3-medium-4k-instruct 1 8.2250 |
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gpt-3.5-turbo 1 8.1375 |
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Chocolatine-3B-Instruct-DPO-Revised 1 7.9875 |
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Daredevil-8B 1 7.8875 |
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Meta-Llama-3.1-8B-Instruct 1 7.0500 |
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vigostral-7b-chat 1 6.7875 |
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Mistral-7B-Instruct-v0.3 1 6.7500 |
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gemma-2-2b-it 1 6.4500 |
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French-Alpaca-7B-Instruct_beta 1 5.6875 |
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vigogne-2-7b-chat 1 5.6625 |
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|
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########## Second turn ########## |
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score |
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model turn |
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gpt-4o-mini 2 8.912500 |
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Chocolatine-14B-Instruct-DPO-v1.2 2 8.337500 |
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Chocolatine-3B-Instruct-DPO-Revised 2 7.937500 |
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Chocolatine-3B-Instruct-DPO-v1.2 2 7.862500 |
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Phi-3-medium-4k-instruct 2 7.750000 |
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Chocolatine-14B-Instruct-4k-DPO 2 7.737500 |
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gpt-3.5-turbo 2 7.679167 |
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Phi-3.5-mini-instruct 2 7.575000 |
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Daredevil-8B 2 7.087500 |
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Meta-Llama-3.1-8B-Instruct 2 6.787500 |
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Mistral-7B-Instruct-v0.3 2 6.500000 |
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vigostral-7b-chat 2 6.162500 |
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gemma-2-2b-it 2 6.100000 |
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French-Alpaca-7B-Instruct_beta 2 5.487395 |
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vigogne-2-7b-chat 2 2.775000 |
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|
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########## Average ########## |
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score |
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model |
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gpt-4o-mini 9.100000 |
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Chocolatine-14B-Instruct-DPO-v1.2 8.475000 |
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Chocolatine-14B-Instruct-4k-DPO 8.187500 |
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Chocolatine-3B-Instruct-DPO-v1.2 8.118750 |
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Phi-3.5-mini-instruct 8.050000 |
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Phi-3-medium-4k-instruct 7.987500 |
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Chocolatine-3B-Instruct-DPO-Revised 7.962500 |
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gpt-3.5-turbo 7.908333 |
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Daredevil-8B 7.487500 |
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Meta-Llama-3.1-8B-Instruct 6.918750 |
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Mistral-7B-Instruct-v0.3 6.625000 |
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vigostral-7b-chat 6.475000 |
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gemma-2-2b-it 6.275000 |
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French-Alpaca-7B-Instruct_beta 5.587866 |
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vigogne-2-7b-chat 4.218750 |
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``` |
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### Usage |
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You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_14B_inference_test_colab.ipynb) |
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You can also run Chocolatine using the following code: |
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```python |
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import transformers |
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from transformers import AutoTokenizer |
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# Format prompt |
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message = [ |
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{"role": "system", "content": "You are a helpful assistant chatbot."}, |
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{"role": "user", "content": "What is a Large Language Model?"} |
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] |
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tokenizer = AutoTokenizer.from_pretrained(new_model) |
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prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) |
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# Create pipeline |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=new_model, |
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tokenizer=tokenizer |
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) |
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# Generate text |
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sequences = pipeline( |
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prompt, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.9, |
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num_return_sequences=1, |
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max_length=200, |
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) |
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print(sequences[0]['generated_text']) |
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``` |
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### Limitations |
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The Chocolatine model is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance. |
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It does not have any moderation mechanism. |
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- **Developed by:** Jonathan Pacifico, 2024 |
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- **Model type:** LLM |
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- **Language(s) (NLP):** French, English |
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- **License:** MIT |
