What are the hardware requirements for Spacescience?

Spacescience requires approximately 3.2 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 Spacescience and how large is it?

Spacescience is developed by calix1. 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 Spacescience?

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

Spacescience by calix1 — VRAM 3.2GB, 2K context

Name: Spacescience
Author: calix1

Spacescience is an open-source language model by calix1. Features: LLM, VRAM: 3.2GB, Context: 2K, LLM Explorer Score: 0.13.

Endpoints compatible Opt Pytorch Region:us

Model Card on HF 🤗: https://huggingface.co/calix1/spacescience

Spacescience Parameters and Internals

LLM Name	Spacescience
Repository 🤗	https://huggingface.co/calix1/spacescience
Required VRAM	3.2 GB
Updated	2026-08-08
Maintainer	calix1
Model Type	opt
Model Files	3.2 GB
Model Architecture	OPTForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.35.2
Vocabulary Size	50272
Torch Data Type	float16
Activation Function	relu

Rank the Spacescience 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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