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--- |
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library_name: transformers |
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tags: |
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- chocolatine |
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- dpo |
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license: apache-2.0 |
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datasets: |
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- jpacifico/french-orca-dpo-pairs-revised |
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language: |
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- fr |
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- en |
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--- |
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### Chocolatine-2-14B-Instruct-v2.0.3 |
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DPO fine-tuning of the merged model [jpacifico/Chocolatine-2-merged-qwen25arch](https://huggingface.co/jpacifico/Chocolatine-2-merged-qwen25arch) (Qwen-2.5-14B architecture) |
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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's overall capabilities. |
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> [!TIP] Window context : up to 128K tokens |
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### OpenLLM Leaderboard |
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Chocolatine-2 is the best-performing 14B fine-tuned model (Ex-aequo with avg. score 41.08) on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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[Updated 2025-02-12] |
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| Metric |Value| |
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|-------------------|----:| |
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|**Avg.** |**41.08**| |
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|IFEval |70.37| |
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|BBH |50.63| |
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|MATH Lvl 5 |40.56| |
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|GPQA |17.23| |
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|MuSR |19.07| |
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|MMLU-PRO |48.60| |
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### LLM Leaderboard FR |
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Top 3 all categories on the French Government [Leaderboard LLM FR](https://huggingface.co/spaces/fr-gouv-coordination-ia/llm_leaderboard_fr#/) |
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 |
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[Updated 2025-02-15] |
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### MT-Bench-French |
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Chocolatine-2 outperforms its previous versions and its base architecture Qwen-2.5 model 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 a LLM-judge. |
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My goal was to achieve GPT-4o-mini's performance on the French language, this version equals the performance of the OpenAI model according to this benchmark |
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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 9.287500 |
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Chocolatine-2-14B-Instruct-v2.0.3 1 9.112500 |
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Qwen2.5-14B-Instruct 1 8.887500 |
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Chocolatine-14B-Instruct-DPO-v1.2 1 8.612500 |
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Phi-3.5-mini-instruct 1 8.525000 |
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Chocolatine-3B-Instruct-DPO-v1.2 1 8.375000 |
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DeepSeek-R1-Distill-Qwen-14B 1 8.375000 |
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phi-4 1 8.300000 |
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Phi-3-medium-4k-instruct 1 8.225000 |
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gpt-3.5-turbo 1 8.137500 |
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Chocolatine-3B-Instruct-DPO-Revised 1 7.987500 |
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Meta-Llama-3.1-8B-Instruct 1 7.050000 |
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vigostral-7b-chat 1 6.787500 |
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Mistral-7B-Instruct-v0.3 1 6.750000 |
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gemma-2-2b-it 1 6.450000 |
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########## Second turn ########## |
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score |
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model turn |
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Chocolatine-2-14B-Instruct-v2.0.3 2 9.050000 |
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gpt-4o-mini 2 8.912500 |
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Qwen2.5-14B-Instruct 2 8.912500 |
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Chocolatine-14B-Instruct-DPO-v1.2 2 8.337500 |
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DeepSeek-R1-Distill-Qwen-14B 2 8.200000 |
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phi-4 2 8.131250 |
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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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gpt-3.5-turbo 2 7.679167 |
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Phi-3.5-mini-instruct 2 7.575000 |
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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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########## 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-2-14B-Instruct-v2.0.3 9.081250 |
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Qwen2.5-14B-Instruct 8.900000 |
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Chocolatine-14B-Instruct-DPO-v1.2 8.475000 |
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DeepSeek-R1-Distill-Qwen-14B 8.287500 |
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phi-4 8.215625 |
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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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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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``` |
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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-2 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-2 model series 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, 2025 |
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- **Model type:** LLM |
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- **Language(s) (NLP):** French, English |
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- **License:** Apache-2.0 |
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Made with ❤️ in France |
