What are the hardware requirements for SauerkrautLM SOLAR Instruct?

SauerkrautLM SOLAR Instruct requires approximately 21.4 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SauerkrautLM SOLAR Instruct and how large is it?

SauerkrautLM SOLAR Instruct is developed by VAGOsolutions, a model with 10.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does SauerkrautLM SOLAR Instruct perform on standard benchmarks?

SauerkrautLM SOLAR Instruct has the following published scores: MMLU 66.2. Compare against reference models on this page or on the LLM Explorer leaderboards.

SauerkrautLM SOLAR Instruct by VAGOsolutions — VRAM 21.4GB, 8K context

Name: SauerkrautLM SOLAR Instruct
Author: VAGOsolutions

SauerkrautLM SOLAR Instruct is an open-source language model by VAGOsolutions. Features: 10.7b LLM, VRAM: 21.4GB, Context: 8K, License: cc-by-nc-4.0, Instruction-Based, LLM Explorer Score: 0.18, Arc: 70.8, HellaSwag: 88.6, MMLU: 66.2, GSM8K: 64.1.

Augmentation Conversational Dataset:argilla/distilabel-mat... De Dpo En Endpoints compatible Finetuned German Instruct Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct

SauerkrautLM SOLAR Instruct Benchmarks

ARC: 70.82 vs 96.7 (so35)^-26.8%

HellaSwag: 88.63 vs 95.3 (gpt4)^-7%

MMLU: 66.2 vs 88.3 (so35)^-25%

GSM8K: 64.14 vs 96.4 (so35)^-33.5%

LLME Score: 0.17893

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

SauerkrautLM SOLAR Instruct (VAGOsolutions/SauerkrautLM-SOLAR-Instruct)

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SauerkrautLM SOLAR Instruct Parameters and Internals

Model Type

text generation, multimodal

Use Cases

Areas:

research, education, non-commercial applications

Applications:

language translation, text generation

Primary Use Cases:

text augmentation, language proficiency improvement

Limitations:

Possible uncensored content

Considerations:

Ensure appropriate use for target audience.

Additional Notes

Translation improvements aimed to enhance German language fluency.

Supported Languages

en (English), de (German)

Training Details

Data Sources:

VAGOsolutions/SauerkrautLM-DPO, argilla/distilabel-math-preference-dpo, HuggingFaceH4/ultrafeedback_binarized, SFT SauerkrautLM, Sauerkraut-7b-HerO

Methodology:

German language augmentation and translation alignment to improve German proficiency.

Input Output

Accepted Modalities:

text

Output Format:

formatted text responses

LLM Name	SauerkrautLM SOLAR Instruct
Repository 🤗	https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct
Model Size	10.7b
Required VRAM	21.4 GB
Updated	2026-05-21
Maintainer	VAGOsolutions
Model Type	llama
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-5 5.0 GB: 2-of-5 4.9 GB: 3-of-5 4.9 GB: 4-of-5 1.7 GB: 5-of-5
Supported Languages	en de
Model Architecture	LlamaForCausalLM
License	cc-by-nc-4.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.36.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Quantized Models of the SauerkrautLM SOLAR Instruct

Model	Likes	Downloads	VRAM
...uerkrautLM SOLAR Instruct GGUF	2	306	4 GB
...uerkrautLM SOLAR Instruct GPTQ	2	16	6 GB
...auerkrautLM SOLAR Instruct AWQ	0	10	6 GB

Best Alternatives to SauerkrautLM SOLAR Instruct

Best Alternatives	Context / RAM	Downloads	Likes
SOLAR 10.7B Instruct V1.0 128K	128K / 21.4 GB	96	3
SOLAR 10.7B V1.0 Instruct 16K	16K / 21.4 GB	63	2
MetaModelv2	8K / 21.4 GB	118	0
Kazemi 1.2 Solar	8K / 21.4 GB	0	0
SOLAR 10.7B Instruct V1.0	4K / 21.4 GB	62146	654
Somer2	4K / 21.4 GB	11	0
SOLAR 10.7B Instruct V1.0	4K / 21.4 GB	6	0
Solarmer3	4K / 21.4 GB	8	0
Somer	4K / 21.4 GB	9	0
...arbonVillain En 10.7B V2 Slerp	4K / 21.4 GB	12442	0

Note: green Score (e.g. "73.2") means that the model is better than VAGOsolutions/SauerkrautLM-SOLAR-Instruct.

Rank the SauerkrautLM SOLAR Instruct Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Release v20260328a

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SauerkrautLM SOLAR Instruct by VAGOsolutions