Implementation:Open compass VLMEvalKit LongVITA
| Field | Value |
|---|---|
| source | VLMEvalKit |
| domain | Vision, Model_Architecture |
Overview
VLM adapter for the Long-VITA model enabling benchmark evaluation in VLMEvalKit.
Description
LongVITA inherits from BaseModel and wraps the Long-VITA model for use within the VLMEvalKit evaluation framework. It initializes the model and tokenizer/processor from a HuggingFace model path (default: VITA-MLLM/Long-VITA-16K_HF) and provides the generate_inner method for inference. Extends LongVITAWrapper which provides core image and video processing logic with dynamic resolution support.
Usage
Register in vlmeval/config.py via supported_VLM and invoke through the standard evaluation pipeline.
Code Reference
- Source:
vlmeval/vlm/long_vita.py, Lines: L1-817 - Import:
from vlmeval.vlm.long_vita import LongVITA
Signature:
class LongVITA(BaseModel):
INSTALL_REQ = False
INTERLEAVE = True
def __init__(self, model_path='VITA-MLLM/Long-VITA-16K_HF', **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.long_vita import LongVITA
model = LongVITA(model_path='path/to/model')
response = model.generate_inner(message)