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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-4k-DPO |
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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 (MMLU). |
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Window context = 4k tokens |
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### Benchmarks |
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Chocolatine-14B is the best-performing < 30B model in terms of MMLU-PRO on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (august 2024) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |29.83| |
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|IFEval (0-Shot) |46.89| |
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|BBH (3-Shot) |48.02| |
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|MATH Lvl 5 (4-Shot)|14.88| |
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|GPQA (0-shot) |12.19| |
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|MuSR (0-shot) |15.15| |
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|**MMLU-PRO (5-shot)** |**41.82**| |
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### MT-Bench-French |
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Chocolatine-14B-Instruct-4k-DPO is outperforming GPT-3.5-Turbo and Phi-3-medium-4k-instruct on |
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[MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french), used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench) |
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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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Chocolatine-14B-Instruct-4k-DPO 1 8.6375 |
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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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Chocolatine-3B-Instruct-DPO-v1.0 1 7.6875 |
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NeuralDaredevil-8B-abliterated 1 7.6250 |
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Phi-3-mini-4k-instruct 1 7.2125 |
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Meta-Llama-3-8B-Instruct 1 7.1625 |
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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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Mistral-7B-Instruct-v0.2 1 6.2875 |
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########## Second turn ########## |
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score |
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model turn |
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Chocolatine-3B-Instruct-DPO-Revised 2 7.937500 |
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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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Chocolatine-3B-Instruct-DPO-v1.0 2 7.612500 |
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NeuralDaredevil-8B-abliterated 2 7.125000 |
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Daredevil-8B 2 7.087500 |
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Meta-Llama-3-8B-Instruct 2 6.800000 |
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Mistral-7B-Instruct-v0.2 2 6.512500 |
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Mistral-7B-Instruct-v0.3 2 6.500000 |
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Phi-3-mini-4k-instruct 2 6.487500 |
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vigostral-7b-chat 2 6.162500 |
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########## Average ########## |
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score |
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model |
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Chocolatine-14B-Instruct-4k-DPO 8.187500 |
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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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Chocolatine-3B-Instruct-DPO-v1.0 7.650000 |
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Daredevil-8B 7.487500 |
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NeuralDaredevil-8B-abliterated 7.375000 |
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Meta-Llama-3-8B-Instruct 6.981250 |
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Phi-3-mini-4k-instruct 6.850000 |
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Mistral-7B-Instruct-v0.3 6.625000 |
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vigostral-7b-chat 6.475000 |
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Mistral-7B-Instruct-v0.2 6.400000 |
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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 |
