What are the hardware requirements for Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2?

Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2 requires approximately 22.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2 and how large is it?

Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2 is developed by Yvthyvq, a model with 7b 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 Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2?

Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2 by Yvthyvq — VRAM 22.4GB

Name: Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2
Author: Yvthyvq

Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2 is an open-source language model by Yvthyvq. Features: 7b LLM, VRAM: 22.4GB, License: apache-2.0, LLM Explorer Score: 0.31.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-omni-7... Cantonese Continued-pretraining Conversational Cpt En Liujgoj Qwen2.5-omni Qwen2 5 omni Region:us Safetensors Sharded Speech-native Tensorflow Yue Yvthyvq Zh

Model Card on HF 🤗: https://huggingface.co/Yvthyvq/Liujgoj-Cantonese-Qwen2.5-Omni-7B-CPT-V2

Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2 Parameters and Internals

LLM Name	Liujgoj Cantonese Qwen2.5 Omni 7B CPT V2
Repository 🤗	https://huggingface.co/Yvthyvq/Liujgoj-Cantonese-Qwen2.5-Omni-7B-CPT-V2
Base Model(s)	Qwen/Qwen2.5-Omni-7B Qwen/Qwen2.5-Omni-7B
Model Size	7b
Required VRAM	22.4 GB
Updated	2026-09-26
Maintainer	Yvthyvq
Model Type	qwen2_5_omni
Model Files	5.0 GB: 1-of-5 5.0 GB: 2-of-5 5.0 GB: 3-of-5 5.0 GB: 4-of-5 2.4 GB: 5-of-5 0.0 GB
Supported Languages	zh en
Model Architecture	Qwen2_5OmniForConditionalGeneration
License	apache-2.0
Model Max Length	32768
Transformers Version	4.57.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

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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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