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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
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/Chocolatine-3B-Instruct-DPO-Revised-GGUF
+This is quantized version of [jpacifico/Chocolatine-3B-Instruct-DPO-Revised](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised) created using llama.cpp
+
+# Original Model Card
+
+
+### 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.  
+Chocolatine is a general model and can itself be finetuned to be specialized for specific use cases.  
+Window context = 4k tokens  
+
+### Benchmarks
+
+The best 3B model on [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (july 2024)  
+5th best < 30B params (average benchmarks).    
+
+### 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) by Bofeng Huang,  
+used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench)  
+
+```
+########## First turn ##########
+                                           score
+model                               turn        
+gpt-3.5-turbo                       1     8.1375
+Chocolatine-3B-Instruct-DPO-Revised 1     7.9875
+Daredevil-8B                        1     7.8875
+Daredevil-8B-abliterated            1     7.8375
+Chocolatine-3B-Instruct-DPO-v1.0    1     7.6875
+NeuralDaredevil-8B-abliterated      1     7.6250
+Phi-3-mini-4k-instruct              1     7.2125
+Meta-Llama-3-8B-Instruct            1     7.1625
+vigostral-7b-chat                   1     6.7875
+Mistral-7B-Instruct-v0.3            1     6.7500
+Mistral-7B-Instruct-v0.2            1     6.2875
+French-Alpaca-7B-Instruct_beta      1     5.6875
+vigogne-2-7b-chat                   1     5.6625
+vigogne-2-7b-instruct               1     5.1375
+
+########## Second turn ##########
+                                             score
+model                               turn          
+Chocolatine-3B-Instruct-DPO-Revised 2     7.937500
+gpt-3.5-turbo                       2     7.679167
+Chocolatine-3B-Instruct-DPO-v1.0    2     7.612500
+NeuralDaredevil-8B-abliterated      2     7.125000
+Daredevil-8B                        2     7.087500
+Daredevil-8B-abliterated            2     6.873418
+Meta-Llama-3-8B-Instruct            2     6.800000
+Mistral-7B-Instruct-v0.2            2     6.512500
+Mistral-7B-Instruct-v0.3            2     6.500000
+Phi-3-mini-4k-instruct              2     6.487500
+vigostral-7b-chat                   2     6.162500
+French-Alpaca-7B-Instruct_beta      2     5.487395
+vigogne-2-7b-chat                   2     2.775000
+vigogne-2-7b-instruct               2     2.240506
+
+########## Average ##########
+                                        score
+model                                        
+Chocolatine-3B-Instruct-DPO-Revised  7.962500
+gpt-3.5-turbo                        7.908333
+Chocolatine-3B-Instruct-DPO-v1.0     7.650000
+Daredevil-8B                         7.487500
+NeuralDaredevil-8B-abliterated       7.375000
+Daredevil-8B-abliterated             7.358491
+Meta-Llama-3-8B-Instruct             6.981250
+Phi-3-mini-4k-instruct               6.850000
+Mistral-7B-Instruct-v0.3             6.625000
+vigostral-7b-chat                    6.475000
+Mistral-7B-Instruct-v0.2             6.400000
+French-Alpaca-7B-Instruct_beta       5.587866
+vigogne-2-7b-chat                    4.218750
+vigogne-2-7b-instruct                3.698113
+```
+
+### 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