Implementation:Open compass VLMEvalKit VILA

Field	Value
source	VLMEvalKit
domain	Vision, Model_Architecture

Overview

VLM adapter for the VILA model enabling benchmark evaluation in VLMEvalKit.

Description

VILA inherits from BaseModel and wraps the VILA model for use within the VLMEvalKit evaluation framework. It initializes the model and tokenizer/processor from a HuggingFace model path (default: Efficient-Large-Model/Llama-3-VILA1.5-8b) and provides the generate_inner method for inference. Requires VILA library installation and is supported only with transformers==4.36.2.

Usage

Register in vlmeval/config.py via supported_VLM and invoke through the standard evaluation pipeline.

Code Reference

Source: vlmeval/vlm/vila.py, Lines: L1-224
Import: from vlmeval.vlm.vila import VILA

Signature:

class VILA(BaseModel):
    INSTALL_REQ = True
    INTERLEAVE = True
    def __init__(self, model_path='Efficient-Large-Model/Llama-3-VILA1.5-8b', **kwargs): ...
    def generate_inner(self, message, dataset=None): ...

I/O Contract

Direction	Description
Inputs	message — list of dicts with type (text/image) and value; dataset — optional dataset name for custom prompting
Outputs	generate_inner() returns str (model response text)

Usage Examples

from vlmeval.vlm.vila import VILA
model = VILA(model_path='path/to/model')
response = model.generate_inner(message)

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Principle

Implementation

Heuristic

Environment