Implementation:Open compass VLMEvalKit Mantis
| Field | Value |
|---|---|
| source | VLMEvalKit |
| domain | Vision, Model_Architecture |
Overview
VLM adapter for the Mantis model enabling benchmark evaluation in VLMEvalKit.
Description
Mantis inherits from BaseModel and wraps the Mantis model for use within the VLMEvalKit evaluation framework. It initializes the model and tokenizer/processor from a HuggingFace model path (default: TIGER-Lab/Mantis-8B-siglip-llama3) and provides the generate_inner method for inference. Supports multiple model architectures including LLaVA, Fuyu, and IDEFICS2 backends.
Usage
Register in vlmeval/config.py via supported_VLM and invoke through the standard evaluation pipeline.
Code Reference
- Source:
vlmeval/vlm/mantis.py, Lines: L1-201 - Import:
from vlmeval.vlm.mantis import Mantis
Signature:
class Mantis(BaseModel):
INSTALL_REQ = True
INTERLEAVE = True
def __init__(self, model_path='TIGER-Lab/Mantis-8B-siglip-llama3', **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.mantis import Mantis
model = Mantis(model_path='path/to/model')
response = model.generate_inner(message)