What are the hardware requirements for MiniCPM5 2B Claude Fable5?

MiniCPM5 2B Claude Fable5 requires approximately 5.1 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed MiniCPM5 2B Claude Fable5 and how large is it?

MiniCPM5 2B Claude Fable5 is developed by SauravMahalik, a model with 2b 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 MiniCPM5 2B Claude Fable5?

MiniCPM5 2B Claude Fable5 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MiniCPM5 2B Claude Fable5 by SauravMahalik — VRAM 5.1GB, 128K context

Name: MiniCPM5 2B Claude Fable5
Author: SauravMahalik

MiniCPM5 2B Claude Fable5 is an open-source language model by SauravMahalik. Features: 2b LLM, VRAM: 5.1GB, Context: 128K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.32.

Agent-traces Base model:adapter:openbmb/min... Base model:openbmb/minicpm5-2b Chat Conversational Dataset:saidutta69/fable-5-pre... En Endpoints compatible Fable-5 Instruct Instruction-following Llama Lora Minicpm Region:us Safetensors Sft Sharded Tensorflow Tool-use Zh

Model Card on HF 🤗: https://huggingface.co/SauravMahalik/MiniCPM5-2B-Claude-Fable5

MiniCPM5 2B Claude Fable5 Parameters and Internals

LLM Name	MiniCPM5 2B Claude Fable5
Repository 🤗	https://huggingface.co/SauravMahalik/MiniCPM5-2B-Claude-Fable5
Base Model(s)	MiniCPM5 2B openbmb/MiniCPM5-2B
Model Size	2b
Required VRAM	5.1 GB
Updated	2026-09-15
Maintainer	SauravMahalik
Model Type	llama
Instruction-Based	Yes
Model Files	4.0 GB: 1-of-2 1.1 GB: 2-of-2
Supported Languages	en zh
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.57.1
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	</s>
Vocabulary Size	130560

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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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