What are the hardware requirements for NemoR?

NemoR requires approximately 24.5 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 NemoR and how large is it?

NemoR is developed by Daemontatox, 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 NemoR?

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

NemoR by Daemontatox — VRAM 24.5GB, 1000K context

Name: NemoR
Author: Daemontatox

NemoR is an open-source language model by Daemontatox. Features: 12.2b LLM, VRAM: 24.5GB, Context: 1000K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.21.

4bit Autotrain compatible Base model:finetune:mistralai/... Base model:mistralai/mistral-n... Conversational Endpoints compatible Instruct Mistral Pytorch Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Daemontatox/NemoR

NemoR Parameters and Internals

LLM Name	NemoR
Repository 🤗	https://huggingface.co/Daemontatox/NemoR
Base Model(s)	Mistral Nemo Instruct 2407 mistralai/Mistral-Nemo-Instruct-2407
Model Size	12.2b
Required VRAM	24.5 GB
Updated	2025-01-04
Maintainer	Daemontatox
Model Type	mistral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-5 4.9 GB: 2-of-5 4.9 GB: 3-of-5 4.9 GB: 4-of-5 4.9 GB: 5-of-5 4.9 GB: 1-of-5 4.9 GB: 2-of-5 4.9 GB: 3-of-5 4.9 GB: 4-of-5 4.9 GB: 5-of-5
Quantization Type	4bit
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	1024000
Model Max Length	1024000
Transformers Version	4.47.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	131072
Torch Data Type	float16

Rank the NemoR capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to NemoR

Best Alternatives	Context / RAM	Downloads	Likes
RAISED Mistral Nemo SFT	128K / 24.5 GB	12	0
...istral Nemo Instruct 2407 MPOA	1000K / 24.5 GB	37	3
...ish Mistral Nemo Instruct 2407	1000K / 24.5 GB	77	2
Mistral Nemo Kurdish Instruct	1000K / 24.5 GB	132	3
Mistral Nemo Kurdish	1000K / 24.5 GB	37	3
Violet Twilight V0.2	1000K / 24.5 GB	66	49
Kalypso	1000K / 24.5 GB	45	24
Crimson Dawn V0.2	1000K / 24.5 GB	17	13
Azure Dusk V0.2	1000K / 24.5 GB	43	10
...al Nemo Japanese Instruct 2408	1000K / 24.5 GB	2358	49

Note: green Score (e.g. "73.2") means that the model is better than Daemontatox/NemoR.