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---
license: llama3.1
language:
- de
- en
- it
- fr
- pt
- es
tags:
- spectrum
model-index:
- name: Llama-3.1-SauerkrautLM-8b-Instruct
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: wis-k/instruction-following-eval
split: train
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 80.17
name: averaged accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=VAGOsolutions%2FLlama-3.1-SauerkrautLM-8b-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: SaylorTwift/bbh
split: test
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 31.0
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=VAGOsolutions%2FLlama-3.1-SauerkrautLM-8b-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: lighteval/MATH-Hard
split: test
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 11.93
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=VAGOsolutions%2FLlama-3.1-SauerkrautLM-8b-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
split: train
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 5.37
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=VAGOsolutions%2FLlama-3.1-SauerkrautLM-8b-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.52
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=VAGOsolutions%2FLlama-3.1-SauerkrautLM-8b-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 32.12
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=VAGOsolutions%2FLlama-3.1-SauerkrautLM-8b-Instruct
name: Open LLM Leaderboard
---
## VAGO solutions Llama-3.1-SauerkrautLM-8b-Instruct
**Fine-tuned Model** - *to showcase the potential of resource-efficient Fine-Tuning of Large Language Models using **Spectrum Fine-Tuning***
Introducing **Llama-3.1-SauerkrautLM-8b-Instruct** – our Sauerkraut version of the powerful [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)!
- 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 Llama-3.1-SauerkrautLM-8b-Instruct](#all-Llama-3.1-SauerkrautLM-8b-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 Llama-3.1-SauerkrautLM-8b-Instruct
| Model | HF | EXL2 | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| Llama-3.1-SauerkrautLM-8b-Instruct | [Link](https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct) | coming soon | coming soon | [Link](https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct-awq) |
## Model Details
**Llama-3.1-SauerkrautLM-8b-Instruct**
- **Model Type:** Llama-3.1-SauerkrautLM-8b-Instruct is a fine-tuned Model based on [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/mistralai/meta-llama/Meta-Llama-3.1-8B-Instruct)
- **Language(s):** German, English
- **License:** llama3.1
- **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 8 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.
**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**
**GPT4ALL**
**TRUTHFULQA**
**OPENLEADERBOARD 2**
## 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 [meta-llama](https://huggingface.co/meta-llama) for providing such a valuable model to the Open-Source community.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/VAGOsolutions__Llama-3.1-SauerkrautLM-8b-Instruct-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=VAGOsolutions%2FLlama-3.1-SauerkrautLM-8b-Instruct&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
| Metric |Value (%)|
|-------------------|--------:|
|**Average** | 28.68|
|IFEval (0-Shot) | 80.17|
|BBH (3-Shot) | 31.00|
|MATH Lvl 5 (4-Shot)| 11.93|
|GPQA (0-shot) | 5.37|
|MuSR (0-shot) | 11.52|
|MMLU-PRO (5-shot) | 32.12|
|