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README.md

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+---
+library_name: transformers
+license: mit
+language:
+- fr
+- en
+tags:
+- french
+- chocolatine
+datasets:
+- jpacifico/french-orca-dpo-pairs-revised
+pipeline_tag: text-generation
+---
+
+### Chocolatine-3B-Instruct-DPO-Revised  
+
+DPO fine-tuned of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) (3.82B 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  
+
+Quantized 4-bit and 8-bit versions are available (see below)  
+A larger version Chocolatine-14B is also available in its latest [version-1.2](https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.2)
+
+### Benchmarks
+
+Chocolatine is the best-performing 3B model on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (august 2024)   
+[Update 2024-08-22] Chocolatine-3B also outperforms Microsoft's new model Phi-3.5-mini-instruct on the average benchmarks of the 3B category.  
+
+![image/png](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Assets/openllm_chocolatine_3B_22082024.png?raw=false)  
+
+
+|      Metric       |Value|
+|-------------------|----:|
+|**Avg.**               |**27.63**|
+|IFEval     |56.23|
+|BBH        |37.16|
+|MATH Lvl 5 |14.5|
+|GPQA       |9.62|
+|MuSR       |15.1|
+|MMLU-PRO   |33.21|
+
+
+### MT-Bench-French
+
+Chocolatine-3B-Instruct-DPO-Revised is outperforming GPT-3.5-Turbo 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.   
+Notably, this latest version of the Chocolatine-3B model is approaching the performance of Phi-3-Medium (14B) in French.
+
+```
+########## First turn ##########
+                                                      score
+model                                         turn         
+gpt-4o-mini                                   1     9.28750
+Chocolatine-14B-Instruct-DPO-v1.2             1     8.61250
+Phi-3-medium-4k-instruct                      1     8.22500
+gpt-3.5-turbo                                 1     8.13750
+Chocolatine-3B-Instruct-DPO-Revised           1     7.98750
+Daredevil-8B                                  1     7.88750
+NeuralDaredevil-8B-abliterated                1     7.62500
+Phi-3-mini-4k-instruct                        1     7.21250
+Meta-Llama-3.1-8B-Instruct                    1     7.05000
+vigostral-7b-chat                             1     6.78750
+Mistral-7B-Instruct-v0.3                      1     6.75000
+gemma-2-2b-it                                 1     6.45000
+French-Alpaca-7B-Instruct_beta                1     5.68750
+vigogne-2-7b-chat                             1     5.66250
+
+########## 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
+Phi-3-medium-4k-instruct                      2     7.750000
+gpt-3.5-turbo                                 2     7.679167
+NeuralDaredevil-8B-abliterated                2     7.125000
+Daredevil-8B                                  2     7.087500
+Meta-Llama-3.1-8B-Instruct                    2     6.787500
+Mistral-7B-Instruct-v0.3                      2     6.500000
+Phi-3-mini-4k-instruct                        2     6.487500
+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
+Phi-3-medium-4k-instruct                       7.987500
+Chocolatine-3B-Instruct-DPO-Revised            7.962500
+gpt-3.5-turbo                                  7.908333
+Daredevil-8B                                   7.487500
+NeuralDaredevil-8B-abliterated                 7.375000
+Meta-Llama-3.1-8B-Instruct                     6.918750
+Phi-3-mini-4k-instruct                         6.850000
+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
+```
+
+
+### Quantized versions
+
+* **4-bit quantized version** is available here : [jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q4_K_M-GGUF](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q4_K_M-GGUF)
+
+* **8-bit quantized version** also available here : [jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q8_0-GGUF](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q8_0-GGUF)
+
+* **Ollama**: [jpacifico/chocolatine-3b](https://ollama.com/jpacifico/chocolatine-3b)
+
+```bash
+ollama run jpacifico/chocolatine-3b
+```
+
+Ollama *Modelfile* example :
+
+```bash
+FROM ./chocolatine-3b-instruct-dpo-revised-q4_k_m.gguf
+TEMPLATE """{{ if .System }}<|system|>
+{{ .System }}<|end|>
+{{ end }}{{ if .Prompt }}<|user|>
+{{ .Prompt }}<|end|>
+{{ end }}<|assistant|>
+{{ .Response }}<|end|>
+"""
+PARAMETER stop """{"stop": ["<|end|>","<|user|>","<|assistant|>"]}"""
+SYSTEM """You are a friendly assistant called Chocolatine."""
+```
+
+### Usage
+
+You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_3B_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

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