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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
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-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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 ## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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 ---
+license: apache-2.0
+language:
+- de
+- en
+- it
+- fr
+- pt
+- nl
+- ru
+- ar
+- es
+tags:
+- spectrum
 ---
+![SauerkrautLM-Nemo-12b-Instruct]( https://vago-solutions.ai/wp-content/uploads/2024/07/Sauerkraut-Nemo.png "SauerkrautLM-Nemo-12b-Instruct")
+## VAGO solutions SauerkrautLM-Nemo-12b-Instruct quantized by [Florian Zimmermeister](https://huggingface.co/flozi00) for fp8 usage
+**Fine-tuned Model** - *to showcase the potential of resource-efficient Fine-Tuning of Large Language Models using **Spectrum Fine-Tuning***
+Introducing **SauerkrautLM-Nemo-12b-Instruct** – our Sauerkraut version of the powerful [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)!
+- Fine-tuning on German-English data with [**Spectrum**](https://github.com/cognitivecomputations/spectrum) Fine-Tuning **targeting 25% of the layers.**
+- Utilized unique German-English Sauerkraut Mix v2
+- Implemented bespoke, precision-engineered fine-tuning approach
+# Table of Contents
+1. [Overview of all SauerkrautLM-Nemo-12b-Instruct](#all-SauerkrautLM-Nemo-12b-Instruct)
+2. [Model Details](#model-details)
+   - [Training procedure](#training-procedure)
+3. [Evaluation](#evaluation)
+5. [Disclaimer](#disclaimer)
+6. [Contact](#contact)
+7. [Collaborations](#collaborations)
+8. [Acknowledgement](#acknowledgement)
+## All SauerkrautLM-Nemo-12b-Instruct
+| Model | HF    | EXL2  | GGUF  | AWQ  |
+|-------|-------|-------|-------|-------|
+| SauerkrautLM-Nemo-12b-Instruct | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-Nemo-12b-Instruct) | coming soon | coming soon | coming soon |
+## Model Details
+**SauerkrautLM-Nemo-12b-Instruct**
+- **Model Type:** SauerkrautLM-Nemo-12b-Instruct is a fine-tuned Model based on [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
+- **Language(s):** German, English
+- **License:** Apache 2.0
+- **Contact:** [VAGO solutions](https://vago-solutions.ai)
+## Training Procedure
+This model showcases the potential of resource-efficient fine-tuning of large language models using Spectrum Fine-Tuning. Here's a brief on the procedure:
+**Fine-tuning on German-English Data**:
+- Utilized Spectrum Fine-Tuning, targeting 25% of the model's layers
+- Introduced the model to a unique German-English Sauerkraut Mix v2
+- Implemented a bespoke, precision-engineered fine-tuning approach
+**Sauerkraut Mix v2**:
+- Premium Dataset for Language Models, focusing on German and English
+- Meticulously selected, high-quality dataset combinations
+- Cutting-edge synthetic datasets created using proprietary, high-precision generation techniques
+## Objective and Results
+The primary goal of this training was to demonstrate that with Spectrum Fine-Tuning targeting 25% of the layers, a 12 billion parameter model can significantly enhance the capabilities while using a fraction of the resources of the classic fine-tuning approach.
+The model has substantially improved skills in German and English, as demonstrated by impressive benchmarks on the new Hugging Face leaderboard. At the same time, our fine-tuning improved skills in all other languages that Nemo can speak, showing  inter-language effects in LLM performance.
+**Spectrum Fine-Tuning can efficiently enhance a large language model's capabilities in multiple languages while preserving the majority of its previously acquired knowledge.**
 ## Evaluation
+**AGIEVAL**
+![SauerkrautLM-Nemo-12b-Instruct-AGIEVAL]( https://vago-solutions.ai/wp-content/uploads/2024/07/agieval2.png "SauerkrautLM-Nemo-12b-Instruct-AGIEVAL")
+**GPT4ALL**
+![SauerkrautLM-Nemo-12b-Instruct-GPT4ALL]( https://vago-solutions.ai/wp-content/uploads/2024/07/gpt4all2.png "SauerkrautLM-Nemo-12b-Instruct-GPT4ALL")
+**TRUTHFULQA**
+![SauerkrautLM-Nemo-12b-Instruct-TRUTHFULQA]( https://vago-solutions.ai/wp-content/uploads/2024/07/tqa2.png "SauerkrautLM-Nemo-12b-Instruct-TRUTHFULQA")
+**OPENLEADERBOARD 2**
+![SauerkrautLM-Nemo-12b-Instruct-OPENLEADERBOARD]( https://vago-solutions.ai/wp-content/uploads/2024/07/hf2.png "SauerkrautLM-Nemo-12b-Instruct-OPENLEADERBOARD")
+**MMLU 5-Shot**
+![SauerkrautLM-Nemo-12b-Instruct-MMLU]( https://vago-solutions.ai/wp-content/uploads/2024/07/mmlu.png "SauerkrautLM-Nemo-12b-Instruct-MMLU")
+## 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 website. 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 at [VAGO solutions](https://vago-solutions.ai)
+## Acknowledgement
+Many thanks to [Mistral AI](https://huggingface.co/mistralai) for providing such a valuable model to the Open-Source community.

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   "architectures": [
     "MistralForCausalLM"
   ],

 {
+  "_name_or_path": "VAGOsolutions/SauerkrautLM-Nemo-12b-Instruct",
   "architectures": [
     "MistralForCausalLM"
   ],