Implementation:Open compass VLMEvalKit RBdashMMChat3 78B Wrapper
| Field | Value |
|---|---|
| source | VLMEvalKit |
| domain | Vision, API_Integration |
Overview
RBdashMMChat3_78B_Wrapper provides a VLMEvalKit API adapter for the RBDash-MM Chat 3.5 78B vision-language model.
Description
RBdashMMChat3_78B_Wrapper inherits from BaseAPI and communicates with an LMDeploy-served RBDash-MM Chat 3.5 78B model endpoint. It uses a custom prompt utility (RBdashMMChat3_78B_PromptUtil) for dataset-specific prompt construction with chain-of-thought reasoning and boxed answer extraction. The wrapper includes a system prompt for structured thinking via think tags.
Usage
Use this adapter when evaluating the RBDash-MM Chat 3.5 78B model variant deployed via an LMDeploy-compatible API server.
Code Reference
- Source:
vlmeval/api/rbdashmm_chat3_5_api.py, Lines: L1-598 - Import:
from vlmeval.api.rbdashmm_chat3_5_api import RBdashMMChat3_78B_Wrapper
Signature:
class RBdashMMChat3_78B_Wrapper(BaseAPI):
def __init__(self, model=None, retry=5, key='sk-123456', verbose=True,
temperature=0.0, timeout=600, api_base=None,
system_prompt=None, max_tokens=20480, **kwargs): ...
def generate_inner(self, inputs, **kwargs): ...
I/O Contract
| Direction | Description |
|---|---|
| Inputs | message — text/image/video content list; model-specific params via kwargs |
| Outputs | generate() returns str prediction; generate_inner() returns (int, str, str) tuple |
Usage Examples
# Example instantiation
model = RBdashMMChat3_78B_Wrapper(model='rbdash-mm-chat3-78b', api_base='http://localhost:23333/v1')
response = model.generate(message)