Add kimi_vl, minicpm_v_4d5, glm_4d1v, ovis2, aria, qwen3d5 infer code

[ZTWHHH]

Description

Add 6 new multimodal model series to the framework. All inference files default to max_new_tokens=2048. Companion test-result PR: #148.

Series	HF model	Tested variants	Modalities
kimi_vl	moonshotai/Kimi-VL-A3B-{Instruct,Thinking}	Both	text, multi-image-interleave
minicpm_v_4d5	openbmb/MiniCPM-V-4_5	All	text, multi-image-interleave
glm_4d1v	THUDM/GLM-4.1V-9B-{Thinking,Base}	Both	text, multi-image-interleave
ovis2	AIDC-AI/Ovis2-{1B,2B,4B,8B}	All registered	text, multi-image-interleave
aria	rhymes-ai/Aria	All	text, multi-image-interleave
qwen3d5	Qwen/Qwen3.5-{0.8B,2B,4B,9B}	All registered	text, multi-image-video-interleave

Qwen3.5 is the official unified vision-language family (Qwen3_5ForConditionalGeneration + Qwen3VLProcessor); see https://qwen.ai/blog?id=qwen3.5 and https://github.com/QwenLM/Qwen3.5.

This PR contains infer/model-code changes only.

Files:

• mmeval/infer/{kimi_vl,minicpm_v_4d5,glm_4d1v,ovis2,aria,qwen3d5}.py
• env_files/{kimi_vl,minicpm_v_4d5,glm_4d1v,ovis2,aria,qwen3d5}_requirements.txt
• mmeval/registry.py

Type of Change

• Bug fix
• New model support
• New dataset support
• Documentation update
• Other (specify):

Testing

• Tested locally
• Added example script (if new model/dataset)

Test command used:

PYTHONPATH=. ENV_DIR=/raid/ztw/envs python mmeval/run.py \
  --model_name_or_path <HF model id> \
  --dataset local@json \
  --infile tests/samples/<modality>.json \
  --img_dir tests/media/448 \
  --out_dir <work_dir> \
  --gpu_per_parallel 1 --parallel_per_task 1 \
  --max_new_tokens 2048

Checklist

• Updated mmeval/registery.py (if new model)
• Updated README (if new model/dataset)
• Code follows existing style

[Copilot]

Pull request overview

Adds six new multimodal model series integrations to simple-mmeval, wiring each series into the registry and providing per-series inference runners plus environment requirement files (defaulting to max_new_tokens=2048).

Changes:

• Register 6 new model series (kimi_vl, minicpm_v_4d5, glm_4d1v, ovis2, aria, qwen3d5) in mmeval/registry.py and map them to env + infer entrypoints.
• Add inference implementations for the 6 series under mmeval/infer/.
• Add per-series environment requirement files under env_files/.

Reviewed changes

Copilot reviewed 13 out of 13 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
mmeval/registry.py	Adds series name→model-name registry entries and env/infer mappings for the 6 new series.
mmeval/infer/kimi_vl.py	Adds inference runner for Kimi-VL-A3B variants (text + multi-image interleave).
mmeval/infer/minicpm_v_4d5.py	Adds inference runner for MiniCPM-V-4_5 (text + multi-image interleave).
mmeval/infer/glm_4d1v.py	Adds inference runner for GLM-4.1V (text + multi-image/video interleave).
mmeval/infer/ovis2.py	Adds inference runner for Ovis2 (text + multi-image interleave).
mmeval/infer/aria.py	Adds inference runner for Aria (text + multi-image interleave).
mmeval/infer/qwen3d5.py	Adds inference runner for Qwen3.5 unified VL family (text + multi-image/video interleave).
env_files/kimi_vl_requirements.txt	Defines per-series Python deps for kimi_vl.
env_files/minicpm_v_4d5_requirements.txt	Defines per-series Python deps for minicpm_v_4d5.
env_files/glm_4d1v_requirements.txt	Defines per-series Python deps for glm_4d1v.
env_files/ovis2_requirements.txt	Defines per-series Python deps for ovis2.
env_files/aria_requirements.txt	Defines per-series Python deps for aria.
env_files/qwen3d5_requirements.txt	Defines per-series Python deps for qwen3d5.

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[ZTWHHH]

Thanks for the suggestion. I considered the reproducibility benefit, but reverted the fixed transformers commit pin to keep the integration closer to the official Qwen3.5 dependency setting (the model card explicitly asks for the latest transformers from main). Since there is no confirmed functional issue requiring a pinned internal commit, I prefer to follow the official setup here.

[ZTWHHH]

Thanks for the review. I checked this against the official Kimi-VL README, which uses the same processor.apply_chat_template(..., return_tensors="pt") → processor(images=image, text=text, ...) pattern; the Kimi-VL processor accepts pre-tokenized ids here. The tests pass end-to-end, so I kept the official pattern to preserve upstream fidelity.