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README.md

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+
+---
+
+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|
+
+