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Model: jpacifico/Chocolatine-2-14B-Instruct-v2.0.3 Source: Original Platform
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README.md
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README.md
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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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### LLM Leaderboard FR
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[Updated 2025-10-20]
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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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### COLE Benchmark (Laval University)
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[Updated 2025-10-20]
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Chocolatine-2-14B-Instruct-v2.0.3 stands out as the *strongest open-weights models* with a Composite Score of **45.05%**
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[Paper here with benchmark results ](https://arxiv.org/pdf/2510.05046)
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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 comes close to 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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### OpenLLM Leaderboard (Archived)
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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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### 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
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