What are the hardware requirements for Nemo Subseq Perseq E1.5?

Nemo Subseq Perseq E1.5 requires approximately 24.5 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Nemo Subseq Perseq E1.5 and how large is it?

Nemo Subseq Perseq E1.5 is developed by kalomaze, 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 Nemo Subseq Perseq E1.5?

Nemo Subseq Perseq E1.5 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Nemo Subseq Perseq E1.5 by kalomaze — VRAM 24.5GB, 128K context

Name: Nemo Subseq Perseq E1.5
Author: kalomaze

Nemo Subseq Perseq E1.5 is an open-source language model by kalomaze. Features: 12.2b LLM, VRAM: 24.5GB, Context: 128K, LLM Explorer Score: 0.18.

Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/kalomaze/nemo-subseq-perseq-e1.5

Nemo Subseq Perseq E1.5 Parameters and Internals

LLM Name	Nemo Subseq Perseq E1.5
Repository 🤗	https://huggingface.co/kalomaze/nemo-subseq-perseq-e1.5
Model Size	12.2b
Required VRAM	24.5 GB
Updated	2025-09-23
Maintainer	kalomaze
Model Type	mistral
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 0.0 GB
Model Architecture	MistralForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.51.1
Tokenizer Class	PreTrainedTokenizer
Padding Token	<pad>
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Nemo Subseq Perseq E1.5 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

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...aptain Eris Violet GRPO V0.420	1000K / 24.5 GB	23	24
...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	1000K / 24.5 GB	37	3
Violet Twilight V0.2	1000K / 24.5 GB	66	49
Tess Embed Fix Maybe	1000K / 24.5 GB	13	0
Funny Nemo Embedding Testing	1000K / 24.5 GB	5	1
12BTRU	1000K / 24.5 GB	21	0
Another Strange Nemo Model	1000K / 24.5 GB	6	0
A Strange Nemo Model	1000K / 24.5 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than kalomaze/nemo-subseq-perseq-e1.5.