Tags: EvolvingLMMs-Lab/lmms-eval
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[Fix] add more model examples (#644) * Update OpenAI compatibility script for Azure integration - Set environment variables for Azure OpenAI API configuration. - Modify model arguments to use the new GPT-4o model version and enable Azure OpenAI support. - Clean up commented installation instructions for clarity. * Refactor imports and clean up code in various utility files - Consolidated import statements in `plm.py` and `utils.py` for better organization. - Removed redundant blank lines in `eval_utils.py`, `fgqa_utils.py`, `rcap_utils.py`, `rdcap_utils.py`, `rtloc_utils.py`, and `sgqa_utils.py` to enhance readability. - Ensured consistent import structure across utility files for improved maintainability. * Update README and add example scripts for model evaluations - Revised installation instructions to facilitate direct package installation from Git. - Added detailed usage examples for various models including Aria, LLaVA, and Qwen2-VL. - Introduced new example scripts for model evaluations, enhancing user guidance for running specific tasks. - Improved clarity in environmental variable setup and common issues troubleshooting sections. * Update README to reflect new example script locations and remove outdated evaluation instructions - Changed paths for model evaluation scripts to point to the new `examples/models` directory. - Added a note directing users to find more examples in the updated location. - Removed outdated evaluation instructions for LLaVA on multiple datasets to streamline the documentation. * Update README to reflect new script locations and enhance evaluation instructions - Replaced outdated evaluation commands with new script paths in the `examples/models` directory. - Updated sections for evaluating larger models, including the introduction of new scripts for tensor parallel and SGLang evaluations. - Streamlined instructions for model evaluation to improve clarity and usability.
[Fix] Regular Linting - Added missing comma in AVAILABLE_MODELS for consistency. - Reordered import statements in vora.py for better readability. - Simplified input_data generation by condensing method calls into a single line. - Ensured default generation parameters are set correctly in the VoRA class.
[Feat] Add support for evaluation of InternVideo2-Chat && Fix evaluat… …ion for mvbench (#280) * [add] add internvideo2 support && change mvbench to video branch * [add] answer_prompt of internvideo2 * [add] change video type of internvideo2 * [fix] update template of mvbench * [reformat] * [fix] generate_until_multi_round * [Feat] videochat2 support --------- Co-authored-by: heyinan <heyinan@pjlab.org.cn>
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