language:
- en
- de
license: apache-2.0
library_name: transformers
tags:
- mistral
- finetune
- chatml
- augmentation
- german
- merge
pipeline_tag: text-generation
model-index:
- name: SauerkrautLM-7b-HerO
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 63.23
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-7b-HerO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 83.52
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-7b-HerO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.3
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-7b-HerO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 49.22
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-7b-HerO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 78.37
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-7b-HerO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.28
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-7b-HerO
name: Open LLM Leaderboard
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 and Open-Orca's 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
- Overview of all Her0 models
- Model Details
- Evaluation
- Disclaimer
- Contact
- Collaborations
- Acknowledgement
All HerO Models
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 David Golchinfar
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. The merged model contains OpenHermes-2.5-Mistral-7B and Open-Orca/Mistral-7B-OpenOrca. We applied the gradient SLERP 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
Language Model evaluation Harness:
Compared to Aleph Alpha Luminous Models
*performed with newest Language Model Evaluation Harness
Big Bench:
*performed with newest Language Model Evaluation Harness
MMLU:
Compared to Big Boy LLMs (Grok0,Grok1,GPT3.5,GPT4)
TruthfulQA:
Compared to OpenAI Models (GPT3.5,GPT4)
MT-Bench (German):
########## 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):
########## 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:
*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. 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 and teknium for providing such valuable models to the Open-Source community. Many thanks to TheBloke for super fast quantifying all of our models.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 64.49 |
| AI2 Reasoning Challenge (25-Shot) | 63.23 |
| HellaSwag (10-Shot) | 83.52 |
| MMLU (5-Shot) | 63.30 |
| TruthfulQA (0-shot) | 49.22 |
| Winogrande (5-shot) | 78.37 |
| GSM8k (5-shot) | 49.28 |