What are the hardware requirements for NQLSG Qwen2.5 14B MegaFusion V7?

NQLSG Qwen2.5 14B MegaFusion V7 requires approximately 29.7 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 NQLSG Qwen2.5 14B MegaFusion V7 and how large is it?

NQLSG Qwen2.5 14B MegaFusion V7 is developed by Lunzima, a model with 14b 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 NQLSG Qwen2.5 14B MegaFusion V7?

NQLSG Qwen2.5 14B MegaFusion V7 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

NQLSG Qwen2.5 14B MegaFusion V7 by Lunzima — VRAM 29.7GB, 32K context

Name: NQLSG Qwen2.5 14B MegaFusion V7
Author: Lunzima

NQLSG Qwen2.5 14B MegaFusion V7 is an open-source language model by Lunzima. Features: 14b LLM, VRAM: 29.7GB, Context: 32K, Merged, LLM Explorer Score: 0.28.

Merged Model Arxiv:2408.07990 Base model:lunzima/nqlsg-qwen2... Base model:suayptalha/lamarckv... Conversational Endpoints compatible Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v7

NQLSG Qwen2.5 14B MegaFusion V7 Parameters and Internals

LLM Name	NQLSG Qwen2.5 14B MegaFusion V7
Repository 🤗	https://huggingface.co/Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v7
Base Model(s)	Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v6 Lamarckvergence 14B Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v6 suayptalha/Lamarckvergence-14B
Merged Model	Yes
Model Size	14b
Required VRAM	29.7 GB
Updated	2026-06-05
Maintainer	Lunzima
Model Type	qwen2
Model Files	4.9 GB: 1-of-6 5.0 GB: 2-of-6 5.0 GB: 3-of-6 5.0 GB: 4-of-6 5.0 GB: 5-of-6 4.8 GB: 6-of-6
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.48.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151665
Torch Data Type	bfloat16
Errors	replace

Rank the NQLSG Qwen2.5 14B MegaFusion V7 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

Best Alternatives to NQLSG Qwen2.5 14B MegaFusion V7

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 14B Instruct 1M	986K / 29.6 GB	10388	342
T3Q Qwen2.5 14B V1.0 E3	986K / 29.7 GB	852	28
ZYH LLM Qwen2.5 14B V4	986K / 29.7 GB	27	8
Qwen2.5 14B 1M YOYO V3	986K / 29.7 GB	254	4
Qwen2.5 14B YOYO V4	986K / 29.7 GB	20	5
Qwen2.5 14B YOYO Latest V2	986K / 29.7 GB	129	0
ZYH LLM Qwen2.5 14B V3	986K / 29.7 GB	23	8
...14B Instruct 1M GRPO Reasoning	986K / 29.7 GB	11	4
Etr1o V1.2	986K / 29.7 GB	11	0
Impish QWEN 14B 1M	986K / 29.7 GB	56	26

Note: green Score (e.g. "73.2") means that the model is better than Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v7.