--- language: - fr - en license: mit library_name: transformers tags: - french - chocolatine datasets: - jpacifico/french-orca-dpo-pairs-revised pipeline_tag: text-generation model-index: - name: Chocolatine-14B-Instruct-DPO-v1.2 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 68.52 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 49.85 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 17.98 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 10.07 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 12.35 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 41.07 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jpacifico/Chocolatine-14B-Instruct-DPO-v1.2 name: Open LLM Leaderboard --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/Chocolatine-14B-Instruct-DPO-v1.2-GGUF This is quantized version of [jpacifico/Chocolatine-14B-Instruct-DPO-v1.2](https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.2) created using llama.cpp # Original Model Card ### Chocolatine-14B-Instruct-DPO-v1.2 DPO fine-tuned of [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) (14B params) using the [jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datasets/jpacifico/french-orca-dpo-pairs-revised) rlhf dataset. Training in French also improves the model in English, surpassing the performances of its base model. Window context = 4k tokens * **4-bit quantized version** available here : [jpacifico/Chocolatine-14B-Instruct-DPO-v1.2-Q4_K_M-GGUF](https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.2-Q4_K_M-GGUF) ### OpenLLM Leaderboard Chocolatine is the best-performing model in size 13B on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (last update: 2024/10/18) ![image/png](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Assets/chocolatine_14B_leaderboard_20240901.png?raw=false) | Metric |Value| |-------------------|----:| |**Avg.** |**33.3**| |IFEval |68.52| |BBH |49.85| |MATH Lvl 5 |17.98| |GPQA |10.07| |MuSR |12.35| |MMLU-PRO |41.07| ### MT-Bench-French Chocolatine-14B-Instruct-DPO-v1.2 outperforms its previous versions and its base model Phi-3-medium-4k-instruct 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. ``` ########## First turn ########## score model turn gpt-4o-mini 1 9.2875 Chocolatine-14B-Instruct-4k-DPO 1 8.6375 Chocolatine-14B-Instruct-DPO-v1.2 1 8.6125 Phi-3.5-mini-instruct 1 8.5250 Chocolatine-3B-Instruct-DPO-v1.2 1 8.3750 Phi-3-medium-4k-instruct 1 8.2250 gpt-3.5-turbo 1 8.1375 Chocolatine-3B-Instruct-DPO-Revised 1 7.9875 Daredevil-8B 1 7.8875 Meta-Llama-3.1-8B-Instruct 1 7.0500 vigostral-7b-chat 1 6.7875 Mistral-7B-Instruct-v0.3 1 6.7500 gemma-2-2b-it 1 6.4500 French-Alpaca-7B-Instruct_beta 1 5.6875 vigogne-2-7b-chat 1 5.6625 ########## Second turn ########## score model turn gpt-4o-mini 2 8.912500 Chocolatine-14B-Instruct-DPO-v1.2 2 8.337500 Chocolatine-3B-Instruct-DPO-Revised 2 7.937500 Chocolatine-3B-Instruct-DPO-v1.2 2 7.862500 Phi-3-medium-4k-instruct 2 7.750000 Chocolatine-14B-Instruct-4k-DPO 2 7.737500 gpt-3.5-turbo 2 7.679167 Phi-3.5-mini-instruct 2 7.575000 Daredevil-8B 2 7.087500 Meta-Llama-3.1-8B-Instruct 2 6.787500 Mistral-7B-Instruct-v0.3 2 6.500000 vigostral-7b-chat 2 6.162500 gemma-2-2b-it 2 6.100000 French-Alpaca-7B-Instruct_beta 2 5.487395 vigogne-2-7b-chat 2 2.775000 ########## Average ########## score model gpt-4o-mini 9.100000 Chocolatine-14B-Instruct-DPO-v1.2 8.475000 Chocolatine-14B-Instruct-4k-DPO 8.187500 Chocolatine-3B-Instruct-DPO-v1.2 8.118750 Phi-3.5-mini-instruct 8.050000 Phi-3-medium-4k-instruct 7.987500 Chocolatine-3B-Instruct-DPO-Revised 7.962500 gpt-3.5-turbo 7.908333 Daredevil-8B 7.487500 Meta-Llama-3.1-8B-Instruct 6.918750 Mistral-7B-Instruct-v0.3 6.625000 vigostral-7b-chat 6.475000 gemma-2-2b-it 6.275000 French-Alpaca-7B-Instruct_beta 5.587866 vigogne-2-7b-chat 4.218750 ``` ### Usage You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_14B_inference_test_colab.ipynb) You can also run Chocolatine using the following code: ```python import transformers from transformers import AutoTokenizer # Format prompt message = [ {"role": "system", "content": "You are a helpful assistant chatbot."}, {"role": "user", "content": "What is a Large Language Model?"} ] tokenizer = AutoTokenizer.from_pretrained(new_model) prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) # Create pipeline pipeline = transformers.pipeline( "text-generation", model=new_model, tokenizer=tokenizer ) # Generate text sequences = pipeline( prompt, do_sample=True, temperature=0.7, top_p=0.9, num_return_sequences=1, max_length=200, ) print(sequences[0]['generated_text']) ``` ### Limitations The Chocolatine model is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanism. - **Developed by:** Jonathan Pacifico, 2024 - **Model type:** LLM - **Language(s) (NLP):** French, English - **License:** MIT # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jpacifico__Chocolatine-14B-Instruct-DPO-v1.2) | Metric |Value| |-------------------|----:| |Avg. |33.30| |IFEval (0-Shot) |68.52| |BBH (3-Shot) |49.85| |MATH Lvl 5 (4-Shot)|17.98| |GPQA (0-shot) |10.07| |MuSR (0-shot) |12.35| |MMLU-PRO (5-shot) |41.07|