What are the hardware requirements for Nano Aha Moment 3B?

Nano Aha Moment 3B requires approximately 6.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Nano Aha Moment 3B and how large is it?

Nano Aha Moment 3B is developed by McGill-NLP, a model with 3b 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 Nano Aha Moment 3B?

Nano Aha Moment 3B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Nano Aha Moment 3B by McGill-NLP — VRAM 6.2GB, 32K context

Name: Nano Aha Moment 3B
Author: McGill-NLP

Nano Aha Moment 3B is an open-source language model by McGill-NLP. Features: 3b LLM, VRAM: 6.2GB, Context: 32K, LLM Explorer Score: 0.17.

Conversational Endpoints compatible Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/McGill-NLP/nano-aha-moment-3b

Nano Aha Moment 3B Parameters and Internals

LLM Name	Nano Aha Moment 3B
Repository 🤗	https://huggingface.co/McGill-NLP/nano-aha-moment-3b
Model Size	3b
Required VRAM	6.2 GB
Updated	2026-08-05
Maintainer	McGill-NLP
Model Type	qwen2
Model Files	5.0 GB: 1-of-2 1.2 GB: 2-of-2
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.48.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Nano Aha Moment 3B capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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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Note: green Score (e.g. "73.2") means that the model is better than McGill-NLP/nano-aha-moment-3b.