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README.md

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 ---
 license: apache-2.0
+language:
+- en
+- de
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- mistral
+- finetune
+- chatml
+- augmentation
+- german
 ---
+
+![SauerkrautLM](https://vago-solutions.de/wp-content/uploads/2023/11/hero.png "SauerkrautLM-7b-HerO")
+## VAGO solutions SauerkrautLM-7b-HerO
+Introducing **SauerkrautLM-7b-HerO** – the pinnacle of German language model technology! 
+Crafted through the **merging** of **[Teknium's OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)** and **[Open-Orca's Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)** and **uniquely fine-tuned with the Sauerkraut dataset.** 
+SauerkrautLM-7b-HerO represents a breakthrough in language modeling, achieving an optimal balance between extensive German data and essential international sources. 
+This ensures the model not only excels in understanding the nuances of the German language but also retains its global capabilities.
+Harnessing the innovative power of the **gradient SLERP method from MergeKit**, we've achieved a groundbreaking fusion of two of the most best performing 7B models based on the Mistral framework.
+This merge  has allowed us to combine the best features of both models, creating an unparalleled synergy. 
+Coupled with the German Sauerkraut dataset, which consists of a mix of augmented and translated data, we have successfully taught the English-speaking merged model the intricacies of the German language.
+This was achieved *without the typical loss of core competencies often associated with fine-tuning in another language of models previously trained mainly in English.*
+Our approach ensures that the model retains its original strengths while acquiring a profound understanding of German, **setting a new benchmark in bilingual language model proficiency.**
+
+# Table of Contents
+1. [Overview of all Her0 models](#all-hero-models)
+2. [Model Details](#model-details)
+   - [Prompt template](#prompt-template)
+   - [Training Dataset](#training-dataset)
+   - [Merge Procedure](#merge-procedure) 
+3. [Evaluation](#evaluation)
+    - [GPT4ALL](#gpt4all)
+    - [Language Model evaluation Harness](#language-model-evaluation-harness)
+    - [BigBench](#big-bench)
+    - [MMLU](#mmlu)
+    - [TruthfulQA](#truthfulqa)
+    - [MT-Bench (German)](#mt-bench-german)
+    - [MT-Bench (English)](#mt-bench-english)
+    - [Additional German Benchmark results](#additional-german-benchmark-results)
+5. [Disclaimer](#disclaimer)
+6. [Contact](#contact)
+7. [Collaborations](#collaborations)
+8. [Acknowledgement](#acknowledgement)
+
+
+## All HerO Models
+
+| Model | HF    | GPTQ  | GGUF  | AWQ  |
+|-------|-------|-------|-------|-------|
+| SauerkrautLM-7b-HerO   | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) | coming soon | coming soon | coming soon |
+
+## Model Details
+**SauerkrautLM-7b-HerO**
+- **Model Type:** SauerkrautLM-7b-HerO is an auto-regressive language model based on the transformer architecture 
+- **Language(s):** English, German
+- **License:** APACHE 2.0
+- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de)
+
+### Training Dataset:
+
+SauerkrautLM-7b-HerO was trained with mix of German data augmentation and translated data. 
+We found, that only a simple translation of training data can lead to unnatural German phrasings. 
+Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data. 
+
+### Merge Procedure:
+
+SauerkrautLM-7b-HerO was merged on 1 A100 with [mergekit](https://github.com/cg123/mergekit).
+The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
+We applied the gradient SLURP method.
+
+
+
+### Prompt Template:
+```
+<|im_start|>system
+Du bist Sauerkraut-HerO, ein großes Sprachmodell, das höflich und kompetent antwortet. Schreibe deine Gedanken Schritt für Schritt auf, um Probleme sinnvoll zu lösen.
+<|im_end|>
+<|im_start|>user
+Wie geht es dir?<|im_end|>
+<|im_start|>assistant
+Mir geht es gut!<|im_end|>
+<|im_start|>user
+Bitte erkläre mir, wie die Zusammenführung von Modellen durch bestehende Spitzenmodelle profitieren kann.<|im_end|>
+<|im_start|>assistant
+```
+## Evaluation
+
+### GPT4ALL:
+*Compared to relevant German Closed and Open Source models*
+![GPT4ALL diagram](https://vago-solutions.de/wp-content/uploads/2023/11/GPT4All.png "SauerkrautLM-7b-HerO GPT4ALL Diagram")
+
+![GPT4ALL table](https://vago-solutions.de/wp-content/uploads/2023/11/GPT4All-Tabelle.png "SauerkrautLM-7b-HerO GPT4ALL Table")
+
+### Language Model evaluation Harness:
+*Compared to Aleph Alpha Luminous Models*
+![Harness](https://vago-solutions.de/wp-content/uploads/2023/11/Luminous-comparison.png "SauerkrautLM-7b-HerO Harness")
+
+**performed with newest Language Model Evaluation Harness*
+### Big Bench:
+![BBH](https://vago-solutions.de/wp-content/uploads/2023/11/BigBench.png "SauerkrautLM-7b-HerO BBH")
+**performed with newest Language Model Evaluation Harness*
+
+### MMLU:
+*Compared to Big Boy LLMs (Grok0,Grok1,GPT3.5,GPT4)*
+![MMLU](https://vago-solutions.de/wp-content/uploads/2023/11/MMLU-Benchmark.png "SauerkrautLM-7b-HerO MMLU")
+### TruthfulQA:
+*Compared to OpenAI Models (GPT3.5,GPT4)*
+![TruthfulQA](https://vago-solutions.de/wp-content/uploads/2023/11/Truthfulqa-Benchmark.png "SauerkrautLM-7b-HerO TruthfulQA")
+
+### MT-Bench (German):
+![MT-Bench German Diagram](https://vago-solutions.de/wp-content/uploads/2023/11/MT-Bench-German.png "SauerkrautLM-7b-HerO MT-Bench German Diagram")
+```
+########## First turn ########## 
+                                                           score 
+model                                              turn          
+SauerkrautLM-70b-v1                                1     7.25000 
+SauerkrautLM-7b-HerO  <---                         1     6.96875 
+SauerkrautLM-7b-v1-mistral                         1     6.30625 
+leo-hessianai-13b-chat                             1     6.18750 
+SauerkrautLM-13b-v1                                1     6.16250 
+leo-mistral-hessianai-7b-chat                      1     6.15625 
+Llama-2-70b-chat-hf                                1     6.03750 
+vicuna-13b-v1.5                                    1     5.80000 
+SauerkrautLM-7b-v1                                 1     5.65000 
+leo-hessianai-7b-chat                              1     5.52500 
+vicuna-7b-v1.5                                     1     5.42500 
+Mistral-7B-v0.1                                    1     5.37500 
+SauerkrautLM-3b-v1                                 1     3.17500 
+Llama-2-7b                                         1     1.28750 
+open_llama_3b_v2                                   1     1.68750 
+
+########## Second turn ########## 
+                                                           score 
+model                                              turn          
+SauerkrautLM-70b-v1                                2     6.83125 
+SauerkrautLM-7b-HerO  <---                         2     6.30625 
+vicuna-13b-v1.5                                    2     5.63125 
+SauerkrautLM-13b-v1                                2     5.34375 
+SauerkrautLM-7b-v1-mistral                         2     5.26250 
+leo-mistral-hessianai-7b-chat                      2     4.99375 
+SauerkrautLM-7b-v1                                 2     4.73750 
+leo-hessianai-13b-chat                             2     4.71250 
+vicuna-7b-v1.5                                     2     4.67500 
+Llama-2-70b-chat-hf                                2     4.66250 
+Mistral-7B-v0.1                                    2     4.53750 
+leo-hessianai-7b-chat                              2     2.65000 
+SauerkrautLM-3b-v1                                 2     1.98750 
+open_llama_3b_v2                                   2     1.22500 
+Llama-2-7b                                         2     1.07500 
+
+########## Average ########## 
+                                                       score 
+model                                                        
+SauerkrautLM-70b-v1                                 7.040625 
+SauerkrautLM-7b-HerO   <---                         6.637500
+SauerkrautLM-7b-v1-mistral                          5.784375 
+SauerkrautLM-13b-v1                                 5.753125 
+vicuna-13b-v1.5                                     5.715625 
+leo-mistral-hessianai-7b-chat                       5.575000 
+leo-hessianai-13b-chat                              5.450000 
+Llama-2-70b-chat-hf                                 5.350000 
+SauerkrautLM-v1-7b                                  5.193750 
+vicuna-7b-v1.5                                      5.050000 
+Mistral-7B-v0.1                                     4.956250 
+leo-hessianai-7b-chat                               4.087500 
+SauerkrautLM-3b-v1                                  2.581250 
+open_llama_3b_v2                                    1.456250 
+Llama-2-7b                                          1.181250 
+```
+**performed with the newest FastChat Version*
+### MT-Bench (English):
+![MT-Bench English Diagram](https://vago-solutions.de/wp-content/uploads/2023/11/MT-Bench-English.png "SauerkrautLM-7b-HerO MT-Bench English Diagram")
+```
+########## First turn ########## 
+                                                           score 
+model                                              turn          
+OpenHermes-2.5-Mistral-7B                          1     8.21875 
+SauerkrautLM-7b-HerO    <---                       1     8.03125 
+Mistral-7B-OpenOrca                                1     7.65625 
+neural-chat-7b-v3-1                                1     7.22500 
+
+########## Second turn ########## 
+                                                          score 
+model                                              turn          
+OpenHermes-2.5-Mistral-7B                          2     7.1000 
+SauerkrautLM-7b-HerO  <---                         2     6.7875 
+neural-chat-7b-v3-1                                2     6.4000 
+Mistral-7B-OpenOrca                                2     6.1750 
+ 
+########## Average ########## 
+                                                       score 
+model                                                         
+OpenHermes-2.5-Mistral-7B                           7.659375 
+SauerkrautLM-7b-HerO  <---                          7.409375 
+Mistral-7B-OpenOrca                                 6.915625 
+neural-chat-7b-v3-1                                 6.812500 
+```
+**performed with the newest FastChat Version*
+
+### Additional German Benchmark results:
+![GermanBenchmarks](https://vago-solutions.de/wp-content/uploads/2023/11/German-benchmarks.png "SauerkrautLM-7b-HerO German Benchmarks")
+*performed with newest Language Model Evaluation Harness
+## Disclaimer
+We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
+However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
+Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files.
+ 
+## Contact
+If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:vaziri@vago-solutions.de). We are also grateful for your feedback and suggestions.
+ 
+## Collaborations
+We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
+
+## Acknowledgement
+Many thanks to [OpenOrca](https://huggingface.co/Open-Orca) and [teknium](https://huggingface.co/teknium) for providing such valuable models to the Open-Source community.
+
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)