155 lines
5.6 KiB
Markdown
155 lines
5.6 KiB
Markdown
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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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- phi4
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license: mit
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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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base_model:
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- microsoft/phi-4
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---
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### Chocolatine-14B-Instruct-DPO-v1.3
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DPO fine-tuning of [microsoft/Phi-4](https://huggingface.co/microsoft/Phi-4) (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's overall capabilities, surpassing the performances of its base model.
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Window context = up to 16k tokens
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### OpenLLM Leaderboard
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Could this be the biggest performance boost ever seen from LLM fine-tuning ? 🤔
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Chocolatine-14B-Instruct-DPO-v1.3 is the best-performing Phi-4 based model on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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for only 1.70kgCo2 (versus > 3kg for other models in the same category and performance)
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[Updated 2025-02-17]
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| Metric |Value|
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|-------------------|----:|
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|**Avg.** |**42.42**|
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|IFEval |70.40|
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|BBH |54.85|
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|MATH Lvl 5 |56.19|
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|GPQA |12.19|
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|MuSR |12.29|
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|MMLU-PRO |48.60|
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### MT-Bench-French
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Chocolatine-14B-Instruct-DPO-v1.3 outperforms its previous Chocolatine versions and its base model Phi-4 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-DPO-v1.3 1 9.0125
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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-4 1 8.3000
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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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########## 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-v1.3 2 8.762500
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Chocolatine-14B-Instruct-DPO-v1.2 2 8.337500
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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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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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########## 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-v1.3 8.825000
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Chocolatine-14B-Instruct-DPO-v1.2 8.475000
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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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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-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:** MIT
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Made with ❤️ in France
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