What are the hardware requirements for UlizaLlama Q4 K M Gguf?

UlizaLlama Q4 K M Gguf requires approximately 4.2 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 UlizaLlama Q4 K M Gguf and how large is it?

UlizaLlama Q4 K M Gguf is developed by de-coder. 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 UlizaLlama Q4 K M Gguf?

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

UlizaLlama Q4 K M Gguf by de-coder — VRAM 4.2GB, 4K context

Name: UlizaLlama Q4 K M Gguf
Author: de-coder

UlizaLlama Q4 K M Gguf is an open-source language model by de-coder. Features: LLM, VRAM: 4.2GB, Context: 4K, License: mit, Quantized, LLM Explorer Score: 0.13.

Art En Endpoints compatible Gguf Llama Q4 Quantized Region:us Sw

Model Card on HF 🤗: https://huggingface.co/de-coder/UlizaLlama_Q4_K_M-gguf

UlizaLlama Q4 K M Gguf Parameters and Internals

Model Type

bilingual, 4-bit quantized

Use Cases

Applications:

Domain-specific tasks, such as healthcare, agriculture, education

Primary Use Cases:

Chatbots, Language learning applications, Information services in Swahili-speaking regions, Edge devices and mobile applications

Additional Notes

This quantized model is ideal for deployment in resource-constrained environments.

Supported Languages

Swahili (Bilingual Proficiency), English (Bilingual Proficiency)

LLM Name	UlizaLlama Q4 K M Gguf
Repository 🤗	https://huggingface.co/de-coder/UlizaLlama_Q4_K_M-gguf
Required VRAM	4.2 GB
Updated	2026-07-17
Maintainer	de-coder
Model Type	llama
Model Files	4.2 GB
Supported Languages	sw en
GGUF Quantization	Yes
Quantization Type	gguf\|q4\|q4_k
Model Architecture	LlamaForCausalLM
License	mit
Context Length	4096
Model Max Length	4096
Transformers Version	4.34.0
Vocabulary Size	20000
Torch Data Type	float16

Rank the UlizaLlama Q4 K M Gguf 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 de-coder/UlizaLlama_Q4_K_M-gguf.