What are the hardware requirements for Qwen3 1.7B MegaScience?

Qwen3 1.7B MegaScience requires approximately 6.9 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 Qwen3 1.7B MegaScience and how large is it?

Qwen3 1.7B MegaScience is developed by MegaScience, a model with 1.7b 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 Qwen3 1.7B MegaScience?

Qwen3 1.7B MegaScience is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 1.7B MegaScience by MegaScience — VRAM 6.9GB, 32K context

Name: Qwen3 1.7B MegaScience
Author: MegaScience

Qwen3 1.7B MegaScience is an open-source language model by MegaScience. Features: 1.7b LLM, VRAM: 6.9GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.18.

Arxiv:2507.16812 Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-1.7b-bas... Conversational Dataset:megascience/megascienc... En Endpoints compatible Qwen Qwen3 Region:us Safetensors Science Scientific-reasoning Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/MegaScience/Qwen3-1.7B-MegaScience

Qwen3 1.7B MegaScience Parameters and Internals

LLM Name	Qwen3 1.7B MegaScience
Repository 🤗	https://huggingface.co/MegaScience/Qwen3-1.7B-MegaScience
Base Model(s)	Qwen/Qwen3-1.7B-Base Qwen/Qwen3-1.7B-Base
Model Size	1.7b
Required VRAM	6.9 GB
Updated	2026-07-25
Maintainer	MegaScience
Model Type	qwen3
Model Files	5.0 GB: 1-of-2 1.9 GB: 2-of-2
Supported Languages	en
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.52.4
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float32
Errors	replace

Rank the Qwen3 1.7B MegaScience 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 MegaScience/Qwen3-1.7B-MegaScience.