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README.md

@@ -0,0 +1,324 @@
+---
+language:
+- de
+- en
+license: other
+tags:
+- two stage dpo
+- dpo
+- gguf
+license_name: llama3
+license_link: LICENSE
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+extra_gated_fields:
+  First Name: text
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+  Date of birth: date_picker
+  Country: country
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+  ? By clicking Submit below I accept the terms of the license and acknowledge that
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+  and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
+extra_gated_button_content: Submit
+---
+
+
+![SauerkrautLM](https://vago-solutions.ai/wp-content/uploads/2024/04/Llama3-Pic.png "Llama-3-SauerkrautLM-8b-Instruct")
+## VAGO solutions Llama-3-SauerkrautLM-8b-Instruct
+Introducing **Llama-3-SauerkrautLM-8b-Instruct** – our Sauerkraut version of the powerful [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)!
+
+The model **Llama-3-SauerkrautLM-8b-Instruct** is a **joint effort** between **VAGO Solutions** and **Hyperspace.ai.** 
+
+- Aligned with **DPO**
+
+# Table of Contents
+1. [Overview of all Llama-3-SauerkrautLM-8b-Instruct](#all-Llama-3-SauerkrautLM-8b-Instruct)
+2. [Model Details](#model-details)
+   - [Prompt template](#prompt-template)
+   - [Training procedure](#proceed-of-the-training)
+3. [Evaluation](#evaluation)
+5. [Disclaimer](#disclaimer)
+6. [Contact](#contact)
+7. [Collaborations](#collaborations)
+8. [Acknowledgement](#acknowledgement)
+
+
+## All SauerkrautLM-llama-3-8B-Instruct
+
+| Model | HF    | EXL2  | GGUF  | AWQ  |
+|-------|-------|-------|-------|-------|
+| Llama-3-SauerkrautLM-8b-Instruct | [Link](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct) | [Link](https://huggingface.co/bartowski/Llama-3-SauerkrautLM-8b-Instruct-exl2) | [Link](https://huggingface.co/bartowski/Llama-3-SauerkrautLM-8b-Instruct-GGUF) | coming soon |
+
+## Model Details
+**SauerkrautLM-llama-3-8B-Instruct**
+- **Model Type:** Llama-3-SauerkrautLM-8b-Instruct is a finetuned Model based on [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+- **Language(s):** German, English
+- **License:** [meta-llama](https://llama.meta.com/llama3/license)
+- **Contact:** [VAGO solutions](https://vago-solutions.ai), [Hyperspace.ai](https://hyperspace.computer/)
+
+### Training procedure:
+- We trained this model in a two staged DPO Fine-Tuning for 1 epoch with 70k data and another epoch with 20k data.
+- LaserRMT version coming soon
+
+**We improved the model's capabilities noticably by feeding it with curated German data.**
+
+
+### Prompt Template:
+
+**English:**
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are a helpful AI assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+Input<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+```
+
+**German:**
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+Du bist ein freundlicher und hilfreicher deutscher KI-Assistent.<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+Input<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+```
+### Example output of german language:
+
+```
+
+Prompt: Erkläre mir die Vorteile für das Fine-Tuning von Meta's LLama Modellen.
+
+Antwort:
+Sehr gern!
+
+Fine-tuning von Meta's LLaMA-Modellen ist eine sehr effektive Möglichkeit, um Ihre Anwendung oder Ihr Projekt mit einer hochentwickelten Sprachverarbeitung auszustatten.
+
+Die Vorteile des Fine-Tunings von LLaMA-Modellen sind:
+
+Adaptation an spezielle Aufgaben: Durch das Fine-Tuning können Sie die Modelle auf Ihre speziellen Bedürfnisse und Anforderungen anpassen.
+Dies ermöglicht es Ihnen, die Leistung der Modelle in Ihren Anwendungen zu verbessern.
+
+Verbesserte Genauigkeit: Die fine-tuned Modelle lernen, sich an die spezifischen Merkmale Ihres Datenbestands anzupassen, was zu einer besseren Genauigkeit bei der Textklassifizierung, -generierung, -summarisierung usw. führt.
+
+Anpassung an Branche oder Domain: Wenn Sie beispielsweise ein Medizinprojekt haben, können Sie die Modelle auf medizinische Fachterminologie und -konzepte trainieren, um eine höhere Präzision bei der Analyse medizinischer Texte zu erzielen.
+
+Kosten-Effektivität: Im Vergleich zum Training neuer Modelle von Grund auf kann das Fine-Tuning viel kosteneffektiver sein, da Sie nur einen kleinen Teil des Trainingsdatums benötigen, um die Modelle an Ihre spezifischen Anforderungen anzupassen.
+
+Schnellere Entwicklung: Das Fine-Tuning ermöglicht es Entwicklern, schnell neue Funktionen und Features in ihre Anwendungen einzufügen, ohne dass sie lange warten müssen, bis neue Modelle trainiert werden.
+
+Bessere Interpretierbarkeit: Da die fine-tuned Modelle auf Ihre spezifischen Daten bestimmt wurden, können Sie leichter verstehen, warum bestimmte Entscheidungen getroffen werden, was wichtig ist, wenn es um Transparenz und Verantwortlichkeit geht.
+
+Insgesamt bietet das Fine-Tuning von LLaMA-Modellen eine flexible und effektive Möglichkeit, um Ihre Anwendungen und Projekte durch die Integration von fortschrittlichen Sprachmodellen zu verbessern.
+```
+
+## Evaluation
+
+**Open LLM Leaderboard:**
+
+evaluated with lm-evaluation-benchmark-harness 0.4.2
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | **74.57**  |
+| ARC (25-shot)         | 74.66          |
+| HellaSwag (10-shot)   | 89.60   |
+| MMLU (5-shot)         | 66.55 |
+| TruthfulQA (0-shot)   | 66.32 |
+| Winogrande (5-shot)   | 80.98  |
+| GSM8K (5-shot)        | 69.29        |
+
+
+**MT-Bench English**
+
+```
+########## First turn ########## 
+                                         score 
+model                            turn 
+Llama-3-SauerkrautLM-8b-Instruct 1     8.15625 
+  
+########## Second turn ########## 
+                                       score 
+model                            turn 
+Llama-3-SauerkrautLM-8b-Instruct 2      7.65 
+  
+########## Average ########## 
+                                     score 
+model 
+Llama-3-SauerkrautLM-8b-Instruct  7.903125 *
+
+```
+* due to specific instruction training the english MT-Bench score is slightly lower than the original LLama-3-8B-Instruct
+
+
+**MT-Bench German**
+coming soon
+
+
+## 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.
+ 
+## Contact
+If you are interested in customized LLMs for business applications, please get in contact with us via our websites. We are also grateful for your feedback and suggestions.
+ 
+## Collaborations
+We are also keenly seeking support and investment for our startups, VAGO solutions and Hyperspace 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 at [VAGO solutions](https://vago-solutions.de/#Kontakt), [Hyperspace.computer](https://hyperspace.computer/)
+
+## Acknowledgement
+Many thanks to [Meta](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) for providing such valuable model to the Open-Source community.
+Also many thanks to [bartowski](https://huggingface.co/bartowski) for super fast quantification of our Model in GGUF and EXL format.

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