Tiny Llava 4 CPU πŸ›


πŸš€ Model Overview

tiny-llava-open-elm-aimv2 is a lightweight image-text-to-text model that combines OpenELM 270M - INSTRUCT as the LLM backbone and AIMv2-Large-Patch14-224-distilled (309M) as the vision encoder. The model has been fine-tuned using LoRA (Low-Rank Adaptation) for efficient training. It was developed using the TinyLLaVA Factory codebase, which provides a modular framework for lightweight multi-modal models.

The model is designed to run efficiently on CPU, making it ideal for resource-constrained environments. It is trained and evaluated on POPE and TextVQA benchmarks. The total model size is 0.6B parameters.


πŸ“Š Performance

Model Name VQAv2 GQA SQA TextVQA MM-VET POPE MME MMMU
LLaVA-1.5-7B 78.5 62.0 66.8 58.2 30.5 85.9 1510.7 -
bczhou/TinyLLaVA-3.1B 79.9 62.0 69.1 59.1 32.0 86.4 1464.9 -
tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B 78.4 61.6 64.4 53.6 26.9 86.4 1339.0 31.7
tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B 80.1 62.1 73.0 60.3 37.5 87.2 1466.4 38.4
cpu4dream/llava-small-OpenELM-AIMv2-0.6B - - - 39.68 - 83.93 - -

πŸ”— References

Downloads last month
6
Inference API
Unable to determine this model's library. Check the docs .

Model tree for cpu4dream/llava-small-OpenELM-AIMv2-0.6B

Base model

apple/OpenELM
Finetuned
(2)
this model

Datasets used to train cpu4dream/llava-small-OpenELM-AIMv2-0.6B

Space using cpu4dream/llava-small-OpenELM-AIMv2-0.6B 1