What are the hardware requirements for Qwen2.5 Math 7B Lean4 Numina E2e?

Qwen2.5 Math 7B Lean4 Numina E2e requires approximately 15.2 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 Math 7B Lean4 Numina E2e and how large is it?

Qwen2.5 Math 7B Lean4 Numina E2e is developed by AkameV6p5, a model with 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 Qwen2.5 Math 7B Lean4 Numina E2e?

Qwen2.5 Math 7B Lean4 Numina E2e is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 Math 7B Lean4 Numina E2e by AkameV6p5 — VRAM 15.2GB, 4K context

Name: Qwen2.5 Math 7B Lean4 Numina E2e
Author: AkameV6p5

Qwen2.5 Math 7B Lean4 Numina E2e is an open-source language model by AkameV6p5. Features: 7b LLM, VRAM: 15.2GB, Context: 4K, License: apache-2.0, Quantized, LLM Explorer Score: 0.31.

4bit En Endpoints compatible Quantized Qwen2 Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/AkameV6p5/qwen2.5-math-7b-lean4-numina-e2e

Qwen2.5 Math 7B Lean4 Numina E2e Parameters and Internals

LLM Name	Qwen2.5 Math 7B Lean4 Numina E2e
Repository 🤗	https://huggingface.co/AkameV6p5/qwen2.5-math-7b-lean4-numina-e2e
Base Model(s)	unsloth/qwen2.5-math-7b-bnb-4bit unsloth/qwen2.5-math-7b-bnb-4bit
Model Size	7b
Required VRAM	15.2 GB
Updated	2026-08-15
Maintainer	AkameV6p5
Model Type	qwen2
Model Files	4.9 GB: 1-of-4 4.9 GB: 2-of-4 4.3 GB: 3-of-4 1.1 GB: 4-of-4
Supported Languages	en
Quantization Type	4bit
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|PAD_TOKEN\|>
Vocabulary Size	152064
Torch Data Type	float16
Errors	replace

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