What are the hardware requirements for Qwen To Gemma Math?

Qwen To Gemma Math requires approximately 10.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen To Gemma Math and how large is it?

Qwen To Gemma Math is developed by daksh-neo, a model with 5.1b 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 Qwen To Gemma Math?

Qwen To Gemma Math is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen To Gemma Math by daksh-neo — VRAM 10.2GB

Name: Qwen To Gemma Math
Author: daksh-neo

Qwen To Gemma Math is an open-source language model by daksh-neo. Features: 5.1b LLM, VRAM: 10.2GB, License: apache-2.0, LLM Explorer Score: 0.23.

Base model:adapter:google/gemm... Base model:google/gemma-4-e2b-... Chain-of-thought Conversational Dataset:openai/gsm8k Distillation En Gemma Gemma4 Gsm8k Knowledge-distillation Lora Math Model-index Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/daksh-neo/qwen-to-gemma-math

Qwen To Gemma Math Parameters and Internals

LLM Name	Qwen To Gemma Math
Repository 🤗	https://huggingface.co/daksh-neo/qwen-to-gemma-math
Base Model(s)	Gemma 4 E2B It google/gemma-4-E2B-it
Model Size	5.1b
Required VRAM	10.2 GB
Updated	2026-07-30
Maintainer	daksh-neo
Model Type	gemma4
Model Files	10.2 GB
Supported Languages	en
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.5.0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Qwen To Gemma Math 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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Note: green Score (e.g. "73.2") means that the model is better than daksh-neo/qwen-to-gemma-math.