What are the hardware requirements for Lite Oute 1 65M Instruct?

Lite Oute 1 65M Instruct requires approximately 0.3 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 Lite Oute 1 65M Instruct and how large is it?

Lite Oute 1 65M Instruct is developed by OuteAI, a model with 65m 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 Lite Oute 1 65M Instruct?

Lite Oute 1 65M Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Lite Oute 1 65M Instruct by OuteAI — VRAM 0.3GB, 2K context

Name: Lite Oute 1 65M Instruct
Author: OuteAI

Lite Oute 1 65M Instruct is an open-source language model by OuteAI. Features: 65m LLM, VRAM: 0.3GB, Context: 2K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.13.

Conversational Endpoints compatible Instruct Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/OuteAI/Lite-Oute-1-65M-Instruct

Lite Oute 1 65M Instruct Parameters and Internals

LLM Name	Lite Oute 1 65M Instruct
Repository 🤗	https://huggingface.co/OuteAI/Lite-Oute-1-65M-Instruct
Model Size	65m
Required VRAM	0.3 GB
Updated	2026-07-18
Maintainer	OuteAI
Model Type	llama
Instruction-Based	Yes
Model Files	0.3 GB
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.42.4
Tokenizer Class	LlamaTokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	32768
Torch Data Type	float32

Rank the Lite Oute 1 65M Instruct capabilities

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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

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