What are the hardware requirements for Minicpm Mermaid Heretic Trial65?

Minicpm Mermaid Heretic Trial65 requires approximately 3.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Minicpm Mermaid Heretic Trial65 and how large is it?

Minicpm Mermaid Heretic Trial65 is developed by Lucasgitguy-co, a model with 1.6b 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 Minicpm Mermaid Heretic Trial65?

Minicpm Mermaid Heretic Trial65 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Minicpm Mermaid Heretic Trial65 by Lucasgitguy-co — VRAM 3.1GB

Name: Minicpm Mermaid Heretic Trial65
Author: Lucasgitguy-co

Minicpm Mermaid Heretic Trial65 is an open-source language model by Lucasgitguy-co. Features: 1.6b LLM, VRAM: 3.1GB, License: apache-2.0, LLM Explorer Score: 0.29.

Conversational En Endpoints compatible Heretic Image-text-to-text Mermaid Minicpm-v Minicpmv4 7 Region:us Safetensors Vision Vision-language-model

Model Card on HF 🤗: https://huggingface.co/Lucasgitguy-co/minicpm-mermaid-heretic-trial65

Minicpm Mermaid Heretic Trial65 Parameters and Internals

LLM Name	Minicpm Mermaid Heretic Trial65
Repository 🤗	https://huggingface.co/Lucasgitguy-co/minicpm-mermaid-heretic-trial65
Model Name	MiniCPM-V 4.7 1B Mermaid Heretic - Trial 65 BF16
Model Size	1.6b
Required VRAM	3.1 GB
Updated	2026-11-03
Maintainer	Lucasgitguy-co
Model Type	minicpmv4_7
Model Files	3.1 GB
Supported Languages	en
Model Architecture	MiniCPMV4_7ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.18.0
Tokenizer Class	PreTrainedTokenizerFast
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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