What are the hardware requirements for GPT NEO 1.3B Wiki?

GPT NEO 1.3B Wiki requires approximately 5.3 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GPT NEO 1.3B Wiki and how large is it?

GPT NEO 1.3B Wiki is developed by Kimargin, a model with 1.3b 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 GPT NEO 1.3B Wiki?

GPT NEO 1.3B Wiki is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GPT NEO 1.3B Wiki by Kimargin — VRAM 5.3GB, 2K context

Name: GPT NEO 1.3B Wiki
Author: Kimargin

GPT NEO 1.3B Wiki is an open-source language model by Kimargin. Features: 1.3b LLM, VRAM: 5.3GB, Context: 2K, License: apache-2.0, Fine-Tuned, LLM Explorer Score: 0.16.

Base model:eleutherai/gpt-neo-... Base model:finetune:eleutherai... Dataset:legacy-datasets/wikipe... En Endpoints compatible Finetuned Gpt neo Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Kimargin/GPT-NEO-1.3B-wiki

GPT NEO 1.3B Wiki Parameters and Internals

LLM Name	GPT NEO 1.3B Wiki
Repository 🤗	https://huggingface.co/Kimargin/GPT-NEO-1.3B-wiki
Base Model(s)	EleutherAI/gpt-neo-1.3B EleutherAI/gpt-neo-1.3B
Model Size	1.3b
Required VRAM	5.3 GB
Updated	2026-08-07
Maintainer	Kimargin
Model Type	gpt_neo
Model Files	5.0 GB: 1-of-2 0.3 GB: 2-of-2
Supported Languages	en
Model Architecture	GPTNeoForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.45.2
Tokenizer Class	GPT2Tokenizer
Padding Token	[PAD]
Vocabulary Size	50258
Torch Data Type	float32
Activation Function	gelu_new
Errors	replace

Rank the GPT NEO 1.3B Wiki 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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