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**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! - Fine-tuning on German-English data with **Spectrum** Fine-Tuning **targeting 25% of the layers.** - Utilized unique German-English Sauerkraut Mix v2 - Implemented bespoke, precision-engineered fine-tuning approach

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Introducing **Llama-3-SauerkrautLM-8b-Instruct** – our Sauerkraut version of the powerful meta-llama/Meta-Llama-3-8B-Instruct! The model **Llama-3-SauerkrautLM-8b-Instruct** is a **joint effort** between **VAGO Solutions** and **Hyperspace.ai.** 1. Overview of all Llama-3-SauerkrautLM-8b-Instruct 2. Model Details - Prompt template - Training procedure 3. Evaluation 5. Disclaimer 6. Contact 7. Collaborations 8. Acknowledgement

Text generation
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SauerkrautLM-ColQwen3-2b-v0.1 achieves **90.24 NDCG@5** on ViDoRe v1, making it the **#1 in the Medium (1-3B) category** among 128-dim models - a significant **+1.01 improvement** over the baseline ColQwen2-v1.0. Traditional OCR-based retrieval **loses layout, tables, and visual context**. Our visual approach: - ✅ **No OCR errors** - Direct visual understanding - ✅ **Layout-aware** - Understands tables, forms, charts - ✅ **End-to-end** - Single model, no pipeline complexity **ViDoRe v1 Tasks:** | Task | Score | |------|-------| | ArxivQA | 91.24 | | DocVQA | 65.06 | | InfoVQA | 93.14 | | ShiftProject | 88.74 | | SyntheticDocQA-AI | 99.63 | | SyntheticDocQA-Energy | 96.91 | | SyntheticDocQA-Gov | 96.08 | | SyntheticDocQA-Health | 99.26 | | TabFQuAD | 90.32 | | TATDQA | 82.06 |

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Introducing **SauerkrautLM-Phi-3-medium** – our Sauerkraut version of the powerful unsloth/Phi-3-medium-4k-instruct! 1. Overview of all SauerkrautLM-Phi-3-medium 2. Model Details - Training procedure 3. Evaluation 5. Disclaimer 6. Contact 7. Collaborations 8. Acknowledgement **SauerkrautLM-Phi-3-medium** - **Model Type:** SauerkrautLM-Phi-3-medium is a finetuned Model based on unsloth/Phi-3-medium-4k-instruct - **Language(s):** German, English - **License:** MIT - **Contact:** VAGO solutions

Text generation
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**Knowledge Distillation** from strong synthetic data (200k samples generated with Qwen/Qwen3-32B-AWQ and scored by a high-performing reranker). **Robust 210M parameter architecture** optimized for multilingual reasoning-focused retrieval without compression trade-offs. With **210 million parameters** – that's **less than 1/33rd the size** of some competing models – SauerkrautLM-Reason-EuroColBERT achieves or exceeds the performance of: - Models with **over 7 billion parameters** (33× larger than ours) - Proprietary API-based solutions from major tech companies - Specialized reasoning models like ReasonIR-8B (38× larger) This balanced architecture provides exceptional performance while remaining deployable on standard infrastructure.

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