Update README.md

README.md

@@ -8,7 +8,47 @@ model-index:
     workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_half_borda
   results: []
 ---
+# Suzume ORPO
 
+<p align="center">
+  <img width=500 src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kWQSu02YfgYdUQqv4s5lq.png" alt="Suzume with Mitsu - a Japanese tree sparrow with honey on it"/>
+</p>
+
+[[Paper]](https://arxiv.org/abs/2405.18952) [[Dataset]](https://huggingface.co/datasets/lightblue/mitsu)
+
+This is Suzume ORPO, an ORPO trained fine-tune of the [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) model using our [lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu) dataset.
+
+We have trained several versions of this model using ORPO and so recommend that you use the best performing model from our tests, [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half).
+
+Note that this model has a non-commerical license as we used the Command R and Command R+ models to generate our training data for this model ([lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu)).
+
+We are currently working on a developing a commerically usable model, so stay tuned for that!
+
+# Model results
+
+We compare the MT-Bench scores across 6 languages for our 4 ORPO trained models, as well as some baselines:
+
+* [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) - The foundation model that our models are ultimately built upon
+* [Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) - The highest performing open model on the Chatbot arena that is of a similar size to ours
+* gpt-3.5-turbo - A fairly high quality (although not state-of-the-art) proprietary LLM
+* [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) - The base model which we train our ORPO finetunes from
+
+| **MT-Bench language** | **meta-llama/Meta-Llama-3-8B-Instruct** | **Nexusflow/Starling-LM-7B-beta** | **gpt-3.5-turbo** | **lightblue/suzume-llama-3-8B-multilingual** | **lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full** | **lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75** | **lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half** | **lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25** |
+|-----------------------|-----------------------------------------|-----------------------------------|-------------------|----------------------------------------------|--------------------------------------------------------------|---------------------------------------------------------------|--------------------------------------------------------------|---------------------------------------------------------------|
+| **Chinese 🇨🇳**        | NaN                                     | 6.97                              | 7.55              | 7.11                                         | 7.65                                                         | **7.77**                                                      | 7.74                                                         | 7.44                                                          |
+| **English 🇺🇸**        | 7.98                                    | 7.92                              | **8.26**          | 7.73                                         | 7.98                                                         | 7.94                                                          | 7.98                                                         | 8.22                                                          |
+| **French 🇫🇷**         | NaN                                     | 7.29                              | 7.74              | 7.66                                         | **7.84**                                                     | 7.46                                                          | 7.78                                                         | 7.81                                                          |
+| **German 🇩🇪**         | NaN                                     | 6.99                              | 7.68              | 7.26                                         | 7.28                                                         | 7.64                                                          | 7.7                                                          | **7.71**                                                      |
+| **Japanese 🇯🇵**       | NaN                                     | 6.22                              | **7.84**          | 6.56                                         | 7.2                                                          | 7.12                                                          | 7.34                                                         | 7.04                                                          |
+| **Russian 🇷🇺**        | NaN                                     | 8.28                              | 7.94              | 8.19                                         | 8.3                                                          | 8.74                                                          | **8.94**                                                     | 8.81                                                          |
+
+We can see noticable improvement on most languages compared to the base model. We also find that our ORPO models achieve the highest score out of all the models we evaluated for a number of languages.
+
+# Training data
+
+We trained this model using the [lightblue/mitsu_full_borda](https://huggingface.co/datasets/lightblue/mitsu_full_borda) dataset.
+
+# Training configuration
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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