What are the hardware requirements for Llava 1.5 7B 4bit?

Llava 1.5 7B 4bit requires approximately 4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llava 1.5 7B 4bit and how large is it?

Llava 1.5 7B 4bit is developed by mlx-community, 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 Llava 1.5 7B 4bit?

Llava 1.5 7B 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llava 1.5 7B 4bit by mlx-community — VRAM 4GB

Name: Llava 1.5 7B 4bit
Author: mlx-community

Llava 1.5 7B 4bit is an open-source language model by mlx-community. Features: 7b LLM, VRAM: 4GB, License: llama2, Quantized, Instruction-Based, LLM Explorer Score: 0.17.

4bit Conversational Dataset:liuhaotian/llava-instr... En Image-text-to-text Instruct Llava Mlx Quantized Region:us Safetensors Vision

Model Card on HF 🤗: https://huggingface.co/mlx-community/llava-1.5-7b-4bit

Llava 1.5 7B 4bit Parameters and Internals

LLM Name	Llava 1.5 7B 4bit
Repository 🤗	https://huggingface.co/mlx-community/llava-1.5-7b-4bit
Model Size	7b
Required VRAM	4 GB
Updated	2026-08-08
Maintainer	mlx-community
Model Type	llava
Instruction-Based	Yes
Model Files	4.0 GB
Supported Languages	en
Quantization Type	4bit
Model Architecture	LlavaForConditionalGeneration
License	llama2
Transformers Version	4.36.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	<pad>
Vocabulary Size	32064
Torch Data Type	float16

Rank the Llava 1.5 7B 4bit 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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