What are the hardware requirements for CalmaCatCoder Next Mini?

CalmaCatCoder Next Mini requires approximately 0.2 GB of VRAM and supports a context window of 1K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed CalmaCatCoder Next Mini and how large is it?

CalmaCatCoder Next Mini is developed by ViorikaAI-org, a model with 100m 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 CalmaCatCoder Next Mini?

CalmaCatCoder Next Mini is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

CalmaCatCoder Next Mini by ViorikaAI-org — VRAM 0.2GB, 1K context

Name: CalmaCatCoder Next Mini
Author: ViorikaAI-org

CalmaCatCoder Next Mini is an open-source language model by ViorikaAI-org. Features: 100m LLM, VRAM: 0.2GB, Context: 1K, License: other, Quantized, LLM Explorer Score: 0.31.

100m Code Conversational Dataset:huggingfacetb/smol-smo... Dataset:huggingfacetb/smollm-c... Dataset:huggingfacetb/smoltalk En Endpoints compatible Gguf Quantized Qwen3 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/ViorikaAI-org/CalmaCatCoder-Next-mini

CalmaCatCoder Next Mini Parameters and Internals

LLM Name	CalmaCatCoder Next Mini
Repository 🤗	https://huggingface.co/ViorikaAI-org/CalmaCatCoder-Next-mini
Model Size	100m
Required VRAM	0.2 GB
Updated	2026-10-04
Maintainer	ViorikaAI-org
Model Type	qwen3
Model Files	0.2 GB 0.2 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	Qwen3ForCausalLM
License	other
Context Length	1024
Model Max Length	1024
Transformers Version	5.17.0
Tokenizer Class	TokenizersBackend
Vocabulary Size	256

Rank the CalmaCatCoder Next Mini 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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