What are the hardware requirements for Chichu 2.0 500M Instruct?

Chichu 2.0 500M Instruct requires approximately 1 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Chichu 2.0 500M Instruct and how large is it?

Chichu 2.0 500M Instruct is developed by Sebastianpro88, a model with 0.5b 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 Chichu 2.0 500M Instruct?

Chichu 2.0 500M Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Chichu 2.0 500M Instruct by Sebastianpro88 — VRAM 1GB, 32K context

Name: Chichu 2.0 500M Instruct
Author: Sebastianpro88

Chichu 2.0 500M Instruct is an open-source language model by Sebastianpro88. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: other, Instruction-Based, LLM Explorer Score: 0.32.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-0.5b-i... Chat Chichu Code Conversational Distillation En Endpoints compatible Instruct Qwen2 Reasoning Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Sebastianpro88/Chichu-2.0-500M-Instruct

Chichu 2.0 500M Instruct Parameters and Internals

LLM Name	Chichu 2.0 500M Instruct
Repository 🤗	https://huggingface.co/Sebastianpro88/Chichu-2.0-500M-Instruct
Base Model(s)	Qwen/Qwen2.5-0.5B-Instruct Qwen/Qwen2.5-0.5B-Instruct
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-08-11
Maintainer	Sebastianpro88
Model Type	qwen2
Instruction-Based	Yes
Model Files	1.0 GB
Supported Languages	en
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	5.14.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

Rank the Chichu 2.0 500M 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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Qwen2 0.5B Abyme Merge3	128K / 1.3 GB	6	1
Qwen2 0.5B Abyme Merge2	128K / 0.3 GB	10	0
Qwen2.5 0.5B Instruct	64K / 1 GB	16	0
Qwen2.5 0.5B Instruct	32K / 1 GB	6324790	572
Sakthai Context 0.5B Tools	32K / 1 GB	474	0
Qwen R1 0.5B	32K / 1 GB	202	2
Veda Labs 0.5B Instruct Base	32K / 1 GB	30	2
...ext Aware Abstention Qwen 0.5B	32K / 2 GB	48	0
Q SS 0.5B Reasoning Math	32K / 1 GB	12	2
DAC5.4 0.5B	32K / 1 GB	12	0