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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 LUNA-SOLARkrautLM-Instruct |
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Introducing **LUNA-SOLARkrautLM-Instruct** – a UNA-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** and tamed with **UNA**. |
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# Table of Contents |
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1. [Overview of all LUNA-SOLARkrautLM-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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## Model Details |
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**LUNA-SOLARkrautLM-Instruct** |
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- **Model Type:** LUNA-SOLARkrautLM-Instruct is a UNA Model based on [fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0) and the powerful set of [SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct/) |
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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) [Juanako.AI - UNA](mailto:info@juanako.ai) |
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### Training Dataset: |
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LUNA-SOLARkrautLM-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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<|im_start|>system |
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Du bist LUNA-SOLARkrautLM, ein großes Sprachmodell, das höflich und kompetent antwortet.<|im_end|> |
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<|im_start|>user |
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Wie geht es dir?<|im_end|> |
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<|im_start|>assistant |
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``` |
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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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## Evaluation |
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``` |
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hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto |
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|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |
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|-----|-------|----------|-----:|-----------|-----:|---|-----:| |
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|gsm8k|Yaml |get-answer| 5|exact_match|0.6467|± |0.0132| |
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hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 0, batch_size: auto (64) |
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| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |
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|--------------|-------|------|-----:|------|-----:|---|-----:| |
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|truthfulqa_mc2|Yaml |none | 0|acc |0.7368|± |0.0149| |
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hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 25, batch_size: auto (32) |
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| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |
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|-------------|-------|------|-----:|--------|----:|---|-----:| |
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|arc_challenge|Yaml |none | 25|acc |0.692|± |0.0135| |
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| | |none | 25|acc_norm|0.715|± |0.0132| |
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hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 0, batch_size: auto (64) |
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| Tasks |Version|Filter|n-shot|Metric| Value | |Stderr| |
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|-----------|-------|------|-----:|------|------:|---|-----:| |
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|paws_de |Yaml |none | 0|acc | 0.3965|± |0.0109| |
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|wmt16-en-de|Yaml |none | 0|bleu | 3.5784|± |0.1325| |
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| | |none | 0|ter |64.5707|± |0.4514| |
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| | |none | 0|chrf |45.7068|± |0.3861| |
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|xnli_de |Yaml |none | 0|acc | 0.4129|± |0.0099| |
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hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 10, batch_size: auto (32) |
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| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |
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|---------|-------|------|-----:|--------|-----:|---|-----:| |
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|hellaswag|Yaml |none | 10|acc |0.7131|± |0.0045| |
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| | |none | 10|acc_norm|0.8815|± |0.0032| |
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hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto (64) |
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| Tasks |Version|Filter|n-shot|Metric| Value | |Stderr| |
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|-----------|-------|------|-----:|------|------:|---|-----:| |
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|wmt16-de-en|Yaml |none | 5|bleu |14.9310|± |0.8014| |
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| | |none | 5|ter |46.3206|± |0.4087| |
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| | |none | 5|chrf |60.8637|± |0.4436| |
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|wmt16-en-de|Yaml |none | 5|bleu | 6.2016|± |0.2918| |
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| | |none | 5|ter |63.9997|± |0.4591| |
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| | |none | 5|chrf |51.1399|± |0.3978| |
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|xnli_de |Yaml |none | 5|acc | 0.4703|± |0.0100| |
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hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct,dtype=float16), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto (16) |
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| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |
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|---------------------------------------|-------|------|-----:|------|-----:|---|-----:| |
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|mmlu |N/A |none | 0|acc |0.6461|± |0.1215| |
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| - humanities |N/A |none | 5|acc |0.5960|± |0.1200| |
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| - formal_logic |Yaml |none | 5|acc |0.4683|± |0.0446| |
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| - high_school_european_history |Yaml |none | 5|acc |0.8121|± |0.0305| |
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| - high_school_us_history |Yaml |none | 5|acc |0.8480|± |0.0252| |
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| - high_school_world_history |Yaml |none | 5|acc |0.8312|± |0.0244| |
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| - international_law |Yaml |none | 5|acc |0.7851|± |0.0375| |
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| - jurisprudence |Yaml |none | 5|acc |0.7685|± |0.0408| |
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| - logical_fallacies |Yaml |none | 5|acc |0.7423|± |0.0344| |
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| - moral_disputes |Yaml |none | 5|acc |0.7283|± |0.0239| |
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| - moral_scenarios |Yaml |none | 5|acc |0.3899|± |0.0163| |
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| - philosophy |Yaml |none | 5|acc |0.7074|± |0.0258| |
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| - prehistory |Yaml |none | 5|acc |0.7716|± |0.0234| |
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| - professional_law |Yaml |none | 5|acc |0.4824|± |0.0128| |
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| - world_religions |Yaml |none | 5|acc |0.7661|± |0.0325| |
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| - other |N/A |none | 5|acc |0.7097|± |0.0900| |
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| - business_ethics |Yaml |none | 5|acc |0.7700|± |0.0423| |
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| - clinical_knowledge |Yaml |none | 5|acc |0.6792|± |0.0287| |
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| - college_medicine |Yaml |none | 5|acc |0.6647|± |0.0360| |
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| - global_facts |Yaml |none | 5|acc |0.3600|± |0.0482| |
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| - human_aging |Yaml |none | 5|acc |0.6861|± |0.0311| |
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| - management |Yaml |none | 5|acc |0.8350|± |0.0368| |
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| - marketing |Yaml |none | 5|acc |0.8504|± |0.0234| |
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| - medical_genetics |Yaml |none | 5|acc |0.6700|± |0.0473| |
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| - miscellaneous |Yaml |none | 5|acc |0.7893|± |0.0146| |
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| - nutrition |Yaml |none | 5|acc |0.7549|± |0.0246| |
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| - professional_accounting |Yaml |none | 5|acc |0.5213|± |0.0298| |
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| - professional_medicine |Yaml |none | 5|acc |0.7353|± |0.0268| |
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| - virology |Yaml |none | 5|acc |0.5783|± |0.0384| |
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| - social_sciences |N/A |none | 5|acc |0.7501|± |0.0684| |
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| - econometrics |Yaml |none | 5|acc |0.5175|± |0.0470| |
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| - high_school_geography |Yaml |none | 5|acc |0.8485|± |0.0255| |
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| - high_school_government_and_politics|Yaml |none | 5|acc |0.8912|± |0.0225| |
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| - high_school_macroeconomics |Yaml |none | 5|acc |0.6615|± |0.0240| |
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| - high_school_microeconomics |Yaml |none | 5|acc |0.7311|± |0.0288| |
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| - high_school_psychology |Yaml |none | 5|acc |0.8385|± |0.0158| |
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| - human_sexuality |Yaml |none | 5|acc |0.7023|± |0.0401| |
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| - professional_psychology |Yaml |none | 5|acc |0.6683|± |0.0190| |
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| - public_relations |Yaml |none | 5|acc |0.6909|± |0.0443| |
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| - security_studies |Yaml |none | 5|acc |0.7633|± |0.0272| |
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| - sociology |Yaml |none | 5|acc |0.8358|± |0.0262| |
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| - us_foreign_policy |Yaml |none | 5|acc |0.8800|± |0.0327| |
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| - stem |N/A |none | 5|acc |0.5569|± |0.1360| |
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| - abstract_algebra |Yaml |none | 5|acc |0.3800|± |0.0488| |
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| - anatomy |Yaml |none | 5|acc |0.6148|± |0.0420| |
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| - astronomy |Yaml |none | 5|acc |0.7237|± |0.0364| |
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| - college_biology |Yaml |none | 5|acc |0.7708|± |0.0351| |
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| - college_chemistry |Yaml |none | 5|acc |0.4600|± |0.0501| |
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| - college_computer_science |Yaml |none | 5|acc |0.5400|± |0.0501| |
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| - college_mathematics |Yaml |none | 5|acc |0.2700|± |0.0446| |
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| - college_physics |Yaml |none | 5|acc |0.3333|± |0.0469| |
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| - computer_security |Yaml |none | 5|acc |0.7300|± |0.0446| |
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| - conceptual_physics |Yaml |none | 5|acc |0.6213|± |0.0317| |
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| - electrical_engineering |Yaml |none | 5|acc |0.6276|± |0.0403| |
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| - elementary_mathematics |Yaml |none | 5|acc |0.4788|± |0.0257| |
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| - high_school_biology |Yaml |none | 5|acc |0.8065|± |0.0225| |
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| - high_school_chemistry |Yaml |none | 5|acc |0.5123|± |0.0352| |
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| - high_school_computer_science |Yaml |none | 5|acc |0.7000|± |0.0461| |
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| - high_school_mathematics |Yaml |none | 5|acc |0.3889|± |0.0297| |
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| - high_school_physics |Yaml |none | 5|acc |0.3576|± |0.0391| |
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| - high_school_statistics |Yaml |none | 5|acc |0.5926|± |0.0335| |
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| - machine_learning |Yaml |none | 5|acc |0.4554|± |0.0473| |
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| Groups |Version|Filter|n-shot|Metric|Value | |Stderr| |
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|------------------|-------|------|-----:|------|-----:|---|-----:| |
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|mmlu |N/A |none | 0|acc |0.6461|± |0.1215| |
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| - humanities |N/A |none | 5|acc |0.5960|± |0.1200| |
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| - other |N/A |none | 5|acc |0.7097|± |0.0900| |
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| - social_sciences|N/A |none | 5|acc |0.7501|± |0.0684| |
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| - stem |N/A |none | 5|acc |0.5569|± |0.1360| |
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``` |
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### MT-Bench |
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``` |
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########## Average ########## |
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score |
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model |
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gpt-4 8.990625 |
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gpt-3.5-turbo 7.943750 |
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claude-instant-v1 7.905660 |
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claude-v1 7.900000 |
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UNA-SOLAR-10.7B-Instruct-v1.0 7.521875 |
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LUNA-SOLARkrautLM-Instruct 7.462500 |
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vicuna-33b-v1.3 7.121875 |
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wizardlm-30b 7.009375 |
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Llama-2-70b-chat 6.856250 |
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Llama-2-13b-chat 6.650000 |
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guanaco-33b 6.528125 |
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tulu-30b 6.434375 |
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guanaco-65b 6.409375 |
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oasst-sft-7-llama-30b 6.409375 |
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palm-2-chat-bison-001 6.400000 |
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mpt-30b-chat 6.393750 |
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vicuna-13b-v1.3 6.387500 |
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wizardlm-13b 6.353125 |
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Llama-2-7b-chat 6.268750 |
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vicuna-7b-v1.3 5.996875 |
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baize-v2-13b 5.750000 |
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nous-hermes-13b 5.553459 |
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mpt-7b-chat 5.459119 |
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gpt4all-13b-snoozy 5.452830 |
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koala-13b 5.350000 |
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mpt-30b-instruct 5.218750 |
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falcon-40b-instruct 5.168750 |
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h2ogpt-oasst-open-llama-13b 4.625000 |
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alpaca-13b 4.531250 |
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chatglm-6b 4.500000 |
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oasst-sft-4-pythia-12b 4.318750 |
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rwkv-4-raven-14b 3.984375 |
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dolly-v2-12b 3.275000 |
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fastchat-t5-3b 3.040625 |
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stablelm-tuned-alpha-7b 2.753125 |
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llama-13b 2.606250 |
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``` |
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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](https://huggingface.co/VAGOsolutions), 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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[Juanako.AI](https://huggingface.co/fblgitis) also seeking support and investment for our startup, we also are open for collaborating with other labs to make awesome models like this one. |
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## Acknowledgement |
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Big Hug to [VAGO Solutions](https://huggingface.co/VAGOsolutions), we merely used our transformers library on their code and dataset, nothing else. This won't be possible without them, thanks! |
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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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