cpu4dream/llava-small-OpenELM-AIMv2-0.6B-auto

Tiny Llava 4 CPU 🐛

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🚀 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.

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Usage

Execute the following test code:

from transformers import AutoTokenizer, AutoModelForCausalLM
hf_path = 'cpu4dream/llava-small-OpenELM-AIMv2-0.6B-auto'
model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
model.cuda()
config = model.config
tokenizer = AutoTokenizer.from_pretrained(hf_path, use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
prompt="What are these?"
image_url="http://images.cocodataset.org/test-stuff2017/000000000001.jpg"
output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, tokenizer=tokenizer)
print('model output:', output_text)
print('runing time:', genertaion_time)

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📊 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	-	-

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🔗 References

• OpenELM
• AIMv2-Large-Patch14-224
• TinyLLaVA Factory GitHub