What are the hardware requirements for Qwen3 1.7B MATH GDPO?

Qwen3 1.7B MATH GDPO requires approximately 3.4 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3 1.7B MATH GDPO and how large is it?

Qwen3 1.7B MATH GDPO is developed by wzx111, 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 MATH GDPO?

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

Qwen3 1.7B MATH GDPO by wzx111 — VRAM 3.4GB, 40K context

Name: Qwen3 1.7B MATH GDPO
Author: wzx111

Qwen3 1.7B MATH GDPO is an open-source language model by wzx111. Features: 1.7b LLM, VRAM: 3.4GB, Context: 40K, LLM Explorer Score: 0.18.

Arxiv:2402.03300 Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-1.7b Conversational Dataset:watermelonhjg/math-lig... Endpoints compatible Gdpo Generated from trainer Open-r1 Qwen3 Region:us Safetensors Trl

Model Card on HF 🤗: https://huggingface.co/wzx111/Qwen3-1.7B-MATH-GDPO

Qwen3 1.7B MATH GDPO Parameters and Internals

LLM Name	Qwen3 1.7B MATH GDPO
Repository 🤗	https://huggingface.co/wzx111/Qwen3-1.7B-MATH-GDPO
Model Name	Qwen3-1.7B-MATH-GDPO
Base Model(s)	Qwen/Qwen3-1.7B Qwen/Qwen3-1.7B
Model Size	1.7b
Required VRAM	3.4 GB
Updated	2026-06-04
Maintainer	wzx111
Model Type	qwen3
Model Files	3.4 GB 0.0 GB
Model Architecture	Qwen3ForCausalLM
Context Length	40960
Model Max Length	40960
Transformers Version	4.52.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen3 1.7B MATH GDPO capabilities

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Note: green Score (e.g. "73.2") means that the model is better than wzx111/Qwen3-1.7B-MATH-GDPO.