What are the hardware requirements for Escarda 86M Base?

Escarda 86M Base requires approximately 0.4 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Escarda 86M Base and how large is it?

Escarda 86M Base is developed by Quazim0t0, a model with 86m 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 Escarda 86M Base?

Escarda 86M Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Escarda 86M Base by Quazim0t0 — VRAM 0.4GB, 4K context

Name: Escarda 86M Base
Author: Quazim0t0

Escarda 86M Base is an open-source language model by Quazim0t0. Features: 86m LLM, VRAM: 0.4GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.28.

Base-model Custom code Dataset:huggingfacefw/fineweb-... En Experimental Feature-extraction Jepa Mla Region:us Safetensors Small-models Spike whale

Model Card on HF 🤗: https://huggingface.co/Quazim0t0/Escarda-86M-Base

Escarda 86M Base Parameters and Internals

LLM Name	Escarda 86M Base
Repository 🤗	https://huggingface.co/Quazim0t0/Escarda-86M-Base
Model Size	86m
Required VRAM	0.4 GB
Updated	2026-08-08
Maintainer	Quazim0t0
Model Type	spike_whale
Model Files	0.4 GB
Supported Languages	en
Model Architecture	SpikeWhaleLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	5.8.0
Tokenizer Class	SpikeTokenizer
Padding Token	<pad>
Vocabulary Size	16512

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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

Best Alternatives to Escarda 86M Base

Best Alternatives	Context / RAM	Downloads	Likes
Escarda 86M Identity	4K / 0.4 GB	416	1
Byrne 86M	4K / 0.4 GB	103	1