What are the hardware requirements for Maqa Llama 4bit GGUF?

Maqa Llama 4bit GGUF requires approximately 4.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Maqa Llama 4bit GGUF and how large is it?

Maqa Llama 4bit GGUF is developed by AliAbdelrasheed. 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 Maqa Llama 4bit GGUF?

Maqa Llama 4bit GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Maqa Llama 4bit GGUF by AliAbdelrasheed — VRAM 4.9GB

Name: Maqa Llama 4bit GGUF
Author: AliAbdelrasheed

Maqa Llama 4bit GGUF is an open-source language model by AliAbdelrasheed. Features: LLM, VRAM: 4.9GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.13.

4bit Conversational Endpoints compatible Gguf Llama Quantized Region:us

Model Card on HF 🤗: https://huggingface.co/AliAbdelrasheed/maqa_llama_4bit_GGUF

Maqa Llama 4bit GGUF Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth, llama, gguf

Additional Notes

The model was trained 2x faster using Unsloth and Huggingface's TRL library.

Supported Languages

en (proficient)

LLM Name	Maqa Llama 4bit GGUF
Repository 🤗	https://huggingface.co/AliAbdelrasheed/maqa_llama_4bit_GGUF
Base Model(s)	Maqa Llama AliAbdelrasheed/maqa_llama
Required VRAM	4.9 GB
Updated	2026-08-21
Maintainer	AliAbdelrasheed
Model Type	llama
Model Files	16.1 GB 4.9 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Model Architecture	AutoModel
License	apache-2.0

Rank the Maqa Llama 4bit GGUF 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 AliAbdelrasheed/maqa_llama_4bit_GGUF.