What are the hardware requirements for Tinyllama Gguf Q4?

Tinyllama Gguf Q4 requires approximately 0.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tinyllama Gguf Q4 and how large is it?

Tinyllama Gguf Q4 is developed by abdymazhit. 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 Tinyllama Gguf Q4?

Tinyllama Gguf Q4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tinyllama Gguf Q4 by abdymazhit — VRAM 0.7GB

Name: Tinyllama Gguf Q4
Author: abdymazhit

Tinyllama Gguf Q4 is an open-source language model by abdymazhit. Features: LLM, VRAM: 0.7GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.13.

4bit Base model:quantized:unsloth/t... Base model:unsloth/tinyllama-b... En Endpoints compatible Gguf Llama Q4 Quantized Region:us Unsloth

Model Card on HF 🤗: https://huggingface.co/abdymazhit/tinyllama-gguf-q4

Tinyllama Gguf Q4 Parameters and Internals

Model Type

text-generation-inference, transformers, gguf

Additional Notes

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

Supported Languages

en (supported)

LLM Name	Tinyllama Gguf Q4
Repository 🤗	https://huggingface.co/abdymazhit/tinyllama-gguf-q4
Base Model(s)	Tinyllama Bnb 4bit unsloth/tinyllama-bnb-4bit
Required VRAM	0.7 GB
Updated	2026-07-29
Maintainer	abdymazhit
Model Type	llama
Model Files	2.2 GB 0.7 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|q4\|4bit
Model Architecture	AutoModel
License	apache-2.0

Rank the Tinyllama Gguf Q4 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 abdymazhit/tinyllama-gguf-q4.