What are the hardware requirements for Mai Replier 9B V2p1?

Mai Replier 9B V2p1 requires approximately 0.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mai Replier 9B V2p1 and how large is it?

Mai Replier 9B V2p1 is developed by tcmofashi, a model with 9b 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 Mai Replier 9B V2p1?

Mai Replier 9B V2p1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mai Replier 9B V2p1 by tcmofashi — VRAM 0.5GB

Name: Mai Replier 9B V2p1
Author: tcmofashi

Mai Replier 9B V2p1 is an open-source language model by tcmofashi. Features: 9b LLM, VRAM: 0.5GB, License: apache-2.0, LLM Explorer Score: 0.28.

Chatbot Chinese Conversational Lora Peft Qq-group-chat Region:us Roleplay Safetensors Zh

Model Card on HF 🤗: https://huggingface.co/tcmofashi/mai_replier-9b-v2p1

Mai Replier 9B V2p1 Parameters and Internals

LLM Name	Mai Replier 9B V2p1
Repository 🤗	https://huggingface.co/tcmofashi/mai_replier-9b-v2p1
Base Model(s)	Qwen/Qwen3-9B Qwen/Qwen3-9B
Model Size	9b
Required VRAM	0.5 GB
Updated	2026-10-08
Maintainer	tcmofashi
Model Files	0.5 GB
Supported Languages	zh
Model Architecture	AutoModel
License	apache-2.0
Model Max Length	262144
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	v_proj\|down_proj\|gate_proj\|k_proj\|up_proj\|q_proj\|o_proj
LoRA Alpha	32
LoRA Dropout	0.1
R Param	64
Errors	replace

Rank the Mai Replier 9B V2p1 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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