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Model: VAGOsolutions/SauerkrautLM-SOLAR-Instruct Source: Original Platform
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---
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license: cc-by-nc-4.0
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language:
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- en
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- de
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- finetune
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- dpo
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- Instruct
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- augmentation
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- german
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datasets:
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- argilla/distilabel-math-preference-dpo
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---
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## VAGO solutions SauerkrautLM-SOLAR-Instruct
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Introducing **SauerkrautLM-SOLAR-Instruct** – our Sauerkraut version of the powerful [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) !
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Aligned with **DPO**
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# Table of Contents
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1. [Overview of all SauerkrautLM-SOLAR-Instruct models](#all-sauerkrautlm-solar-instruct-models)
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2. [Model Details](#model-details)
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- [Prompt template](#prompt-template)
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- [Training Dataset](#training-dataset)
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- [Data Contamination Test](#data-contamination-test-results)
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3. [Evaluation](#evaluation)
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5. [Disclaimer](#disclaimer)
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6. [Contact](#contact)
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7. [Collaborations](#collaborations)
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8. [Acknowledgement](#acknowledgement)
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## All SauerkrautLM-SOLAR-Instruct Models
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| Model | HF | GPTQ | GGUF | AWQ |
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|-------|-------|-------|-------|-------|
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| SauerkrautLM-SOLAR-Instruct | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct/) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-SOLAR-Instruct-GPTQ) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-SOLAR-Instruct-GGUF) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-SOLAR-Instruct-AWQ) |
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## Model Details
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**SauerkrautLM-SOLAR-Instruct**
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- **Model Type:** SauerkrautLM-SOLAR-Instruct is a finetuned Model based on [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
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- **Language(s):** English, German
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- **License:** cc-by-nc-4.0
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- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de)
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### Training Dataset:
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SauerkrautLM-SOLAR-Instruct was trained with mix of German data augmentation and translated data.
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Aligned through **DPO** with our **new German SauerkrautLM-DPO dataset** based on parts of the SFT SauerkrautLM dataset
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as chosen answers and [Sauerkraut-7b-HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) as rejected answers. Added with additional **translated Parts of the [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)** (Our dataset do not contain any TruthfulQA prompts - check Data Contamination Test Results) and **[argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo).**
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We found, that only a simple translation of training data can lead to unnatural German phrasings.
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Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
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We improved the German language skills on this model. Nevertheless, certain formulations may occur that are not entirely correct.
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### Data Contamination Test Results
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Some models on the HuggingFace leaderboard had problems with wrong data getting mixed in.
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We checked our SauerkrautLM-DPO dataset with a special test [1] on this model as target model and upstage/SOLAR-10.7B-Instruct-v1.0 as reference model.
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The HuggingFace team used the same methods [2, 3].
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Our results, with `result < 0.1, %:` being well below 0.9, indicate that our dataset is free from contamination.
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*The data contamination test results of HellaSwag and Winograde will be added once [1] supports them.*
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| Dataset | ARC | MMLU | TruthfulQA | GSM8K |
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|------------------------------|-------|-------|-------|-------|
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| **SauerkrautLM-DPO**| result < 0.1, %: 0.0 |result < 0.1, %: 0.09 | result < 0.1, %: 0.13 | result < 0.1, %: 0.16 |
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[1] https://github.com/swj0419/detect-pretrain-code-contamination
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[2] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/474#657f2245365456e362412a06
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[3] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/265#657b6debf81f6b44b8966230
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### Prompt Template:
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```
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### System:\nDu sprichst grammatikalisch korrektes Deutsch auf höchstem Muttersprachler Niveau.\n### User:\n{user}\n\n### Assistant:\n{assistant}
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```
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*Prompt Example on Temp 0.5
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```
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### User:
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Hello, how are you?
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### Assistant:
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Hi there! I am an AI language model, so I don't have personal feelings or emotions in the traditional sense. However, I can assure you that my systems and processes are functioning well at this moment, allowing me to provide helpful responses for your queries.
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How may I assist you today?
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```
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*Prompt Example on Temp 0.5
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## Evaluation
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 74.21 |
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| ARC (25-shot) | 70.82 |
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| HellaSwag (10-shot) | 88.63 |
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| MMLU (5-shot) | 66.2|
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| TruthfulQA (0-shot) | 71.95 |
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| Winogrande (5-shot) | 83.5 |
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| GSM8K (5-shot) | 64.14 |
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## Disclaimer
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We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
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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.
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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.
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## Contact
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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.
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## Collaborations
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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.
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## Acknowledgement
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Many thanks to [argilla](https://huggingface.co/datasets/argilla) and [Huggingface](https://huggingface.co) for providing such valuable datasets to the Open-Source community. And of course a big thanks to [upstage](https://huggingface.co/upstage) for providing the open source community with their latest technology!
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Many thanks to [TheBloke](https://huggingface.co/TheBloke) for super fast quantifying all of our models.
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|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
45
tokenizer_config.json
Normal file
45
tokenizer_config.json
Normal file
@@ -0,0 +1,45 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{% if message['content']%}{{'### System:\n' + message['content']+'\n\n'}}{% endif %}{% elif message['role'] == 'user' %}{{'### User:\n' + message['content']+'\n\n'}}{% elif message['role'] == 'assistant' %}{{'### Assistant:\n' + message['content']}}{% endif %}{% if loop.last and add_generation_prompt %}{{ '### Assistant:\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "</s>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"trust_remote_code": false,
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": true,
|
||||
"use_fast": true
|
||||
}
|
||||
Reference in New Issue
Block a user