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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-3B-Instruct-DPO-Revised |
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DPO fine-tuned of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) (3.82B 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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### Benchmarks |
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Chocolatine is the best-performing 3B model 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.** |**27.63**| |
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|IFEval (0-Shot) |56.23| |
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|BBH (3-Shot) |37.16| |
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|MATH Lvl 5 (4-Shot)|14.5| |
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|GPQA (0-shot) |9.62| |
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|MuSR (0-shot) |15.1| |
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|MMLU-PRO (5-shot) |33.21| |
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### MT-Bench-French |
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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, |
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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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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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Daredevil-8B-abliterated 1 7.8375 |
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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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French-Alpaca-7B-Instruct_beta 1 5.6875 |
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vigogne-2-7b-chat 1 5.6625 |
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vigogne-2-7b-instruct 1 5.1375 |
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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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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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Daredevil-8B-abliterated 2 6.873418 |
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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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French-Alpaca-7B-Instruct_beta 2 5.487395 |
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vigogne-2-7b-chat 2 2.775000 |
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vigogne-2-7b-instruct 2 2.240506 |
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########## Average ########## |
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score |
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model |
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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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Daredevil-8B-abliterated 7.358491 |
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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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French-Alpaca-7B-Instruct_beta 5.587866 |
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vigogne-2-7b-chat 4.218750 |
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vigogne-2-7b-instruct 3.698113 |
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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_3B_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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* **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) |
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* **Ollama**: [jpacifico/chocolatine-3b](https://ollama.com/jpacifico/chocolatine-3b) |
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```bash |
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ollama run jpacifico/chocolatine-3b |
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``` |
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Ollama *Modelfile* example : |
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```bash |
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FROM ./chocolatine-3b-instruct-dpo-revised-q4_k_m.gguf |
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TEMPLATE """{{ if .System }}<|system|> |
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{{ .System }}<|end|> |
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{{ end }}{{ if .Prompt }}<|user|> |
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{{ .Prompt }}<|end|> |
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{{ end }}<|assistant|> |
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{{ .Response }}<|end|> |
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""" |
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PARAMETER stop """{"stop": ["<|end|>","<|user|>","<|assistant|>"]}""" |
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SYSTEM """You are a friendly assistant called Chocolatine.""" |
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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 |
