What are the hardware requirements for Educa Ai Nemo Sft?

Educa Ai Nemo Sft requires approximately 49.3 GB of VRAM and supports a context window of 1000K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Educa Ai Nemo Sft and how large is it?

Educa Ai Nemo Sft is developed by DigitalLearningGmbH, a model with 12.2b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Educa Ai Nemo Sft?

Educa Ai Nemo Sft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Educa Ai Nemo Sft by DigitalLearningGmbH — VRAM 49.3GB, 1000K context

Name: Educa Ai Nemo Sft
Author: DigitalLearningGmbH

Educa Ai Nemo Sft is an open-source language model by DigitalLearningGmbH. Features: 12.2b LLM, VRAM: 49.3GB, Context: 1000K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.16.

Base model:finetune:mistralai/... Base model:mistralai/mistral-n... Conversational De En Endpoints compatible Es Fr Instruct It Ja Mistral Pt Region:us Ru Safetensors Sharded Tensorflow Zh

Model Card on HF 🤗: https://huggingface.co/DigitalLearningGmbH/educa-ai-nemo-sft

Educa Ai Nemo Sft Parameters and Internals

LLM Name	Educa Ai Nemo Sft
Repository 🤗	https://huggingface.co/DigitalLearningGmbH/educa-ai-nemo-sft
Base Model(s)	Mistral Nemo Instruct 2407 mistralai/Mistral-Nemo-Instruct-2407
Model Size	12.2b
Required VRAM	49.3 GB
Updated	2026-09-08
Maintainer	DigitalLearningGmbH
Model Type	mistral
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-10 4.7 GB: 2-of-10 5.0 GB: 3-of-10 4.9 GB: 4-of-10 5.0 GB: 5-of-10 4.9 GB: 6-of-10 5.0 GB: 7-of-10 4.9 GB: 8-of-10 5.0 GB: 9-of-10 4.9 GB: 10-of-10
Supported Languages	en fr de es it pt ru zh ja
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	1024000
Model Max Length	1024000
Transformers Version	4.47.1
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	131072
Torch Data Type	float32

Rank the Educa Ai Nemo Sft capabilities

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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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Note: green Score (e.g. "73.2") means that the model is better than DigitalLearningGmbH/educa-ai-nemo-sft.