Add files using upload-large-folder tool

.gitattributes

@@ -65,3 +65,6 @@ benchmarks/Medical_Multimodal_Evaluation_Data/images/ADI-ELGHPIYS.tif filter=lfs
 benchmarks/Medical_Multimodal_Evaluation_Data/images/LYM-EMNVSPDD.tif filter=lfs diff=lfs merge=lfs -text
 benchmarks/Medical_Multimodal_Evaluation_Data/images/MUS-GHCNLWQY.tif filter=lfs diff=lfs merge=lfs -text
 benchmarks/Medical_Multimodal_Evaluation_Data/images/Prime_FULL_Prime_FULL_01-040_W36_OS_fundus_W36.tif filter=lfs diff=lfs merge=lfs -text
+benchmarks/Medical_Multimodal_Evaluation_Data/images/LYM-GENSPYEH.tif filter=lfs diff=lfs merge=lfs -text
+benchmarks/Medical_Multimodal_Evaluation_Data/images/Prime_FULL_Prime_FULL_01-014_W92_OS_fundus_W92.tif filter=lfs diff=lfs merge=lfs -text
+benchmarks/Medical_Multimodal_Evaluation_Data/images/Prime_FULL_Prime_FULL_02-031_W52_OD_fundus_OD_W52.tif filter=lfs diff=lfs merge=lfs -text

MedEvalKit/LLaVA-NeXT/.dockerignore

@@ -0,0 +1,21 @@
+# The .dockerignore file excludes files from the container build process.
+#
+# https://docs.docker.com/engine/reference/builder/#dockerignore-file
+
+# Exclude Git files
+.git
+.github
+.gitignore
+
+# Exclude Python cache files
+__pycache__
+.mypy_cache
+.pytest_cache
+.ruff_cache
+
+# Exclude Python virtual environment
+/venv
+
+# Exclude some weights
+/openai
+/liuhaotian

MedEvalKit/LLaVA-NeXT/.editorconfig

@@ -0,0 +1,18 @@
+root = true
+
+# Unix-style newlines with a newline ending every file
+[*]
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+charset = utf-8
+
+# 4 space indentation
+[*.{py,json}]
+indent_style = space
+indent_size = 4
+
+# 2 space indentation
+[*.{md,sh,yaml,yml}]
+indent_style = space
+indent_size = 2

MedEvalKit/LLaVA-NeXT/.gitattributes

@@ -0,0 +1,29 @@
+# https://git-scm.com/docs/gitattributes
+
+# Set the default behavior, in case people don't have core.autocrlf set.
+# https://git-scm.com/docs/gitattributes#_end_of_line_conversion
+* text=auto
+
+# common python attributes, taken from https://github.com/alexkaratarakis/gitattributes/blob/710900479a2bedeec7003d381719521ffbb18bf8/Python.gitattributes
+# Source files
+# ============
+*.pxd    text diff=python
+*.py     text diff=python
+*.py3    text diff=python
+*.pyw    text diff=python
+*.pyx    text diff=python
+*.pyz    text diff=python
+*.pyi    text diff=python
+
+# Binary files
+# ============
+*.db     binary
+*.p      binary
+*.pkl    binary
+*.pickle binary
+*.pyc    binary export-ignore
+*.pyo    binary export-ignore
+*.pyd    binary
+
+# Jupyter notebook
+*.ipynb  text eol=lf

MedEvalKit/LLaVA-NeXT/.gitignore

@@ -0,0 +1,73 @@
+# Python
+__pycache__
+*.pyc
+*.egg-info
+dist
+
+# Log
+*.log
+*.log.*
+# *.json
+# *.jsonl
+
+# Data
+!**/alpaca-data-conversation.json
+# Editor
+.idea
+*.swp
+.vscode
+
+# Other
+.DS_Store
+wandb
+output
+llavavid
+
+checkpoints
+project_checkpoints
+debug_checkpoints
+playground/data
+playground/cc3m_llava34b_cap
+ckpts*
+
+.ipynb_checkpoints
+chunyl_scripts
+*.ipynb
+
+# DevContainer
+!.devcontainer/*
+
+# Demo
+serve_images/
+notebooks/
+logs
+scripts/dist_*
+logs/
+submissions/
+cn_scripts/
+internal_project_checkpoints/
+work_dirs
+scripts/i18n/*
+playground/.nfs028b000000010add00000001
+HIP
+playground/.nfs028b0000017bff2c00000012
+scripts/qwen
+scripts/vicuna
+scripts/mistral
+scripts/baseline_rep
+scripts/cn_boli01_hl
+scripts/cn_boli01_lf
+scripts/cn_lf
+scripts/cn_lq
+scripts/cn_yg
+scripts/cn_yg_hao
+scripts/eva_encoder
+scripts/i18n
+scripts/i18n_higher_res
+scripts/multi-images
+scratchpad
+build/
+playground/*.json
+mlx_configs/
+data_processing/
+# demo/

MedEvalKit/LLaVA-NeXT/LICENSE

@@ -0,0 +1,201 @@
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MedEvalKit/LLaVA-NeXT/README.md

@@ -0,0 +1,247 @@
+<p align="center" width="100%">
+<img src="https://i.postimg.cc/pL17YtG4/WX20240508-220230-2x.png"  width="80%" height="80%">
+</p>
+
+# LLaVA-NeXT: Open Large Multimodal Models
+[![Static Badge](https://img.shields.io/badge/llava_video-paper-green)](http://arxiv.org/abs/2410.02713)
+[![Static Badge](https://img.shields.io/badge/llava_onevision-paper-green)](https://arxiv.org/abs/2408.03326)
+[![llava_next-blog](https://img.shields.io/badge/llava_next-blog-green)](https://llava-vl.github.io/blog/)
+
+[![llava_onevision-demo](https://img.shields.io/badge/llava_onevision-demo-red)](https://llava-onevision.lmms-lab.com/)
+[![llava_next-video_demo](https://img.shields.io/badge/llava_video-demo-red)](https://huggingface.co/spaces/WildVision/vision-arena)
+[![llava_next-interleave_demo](https://img.shields.io/badge/llava_next-interleave_demo-red)](https://huggingface.co/spaces/lmms-lab/LLaVA-NeXT-Interleave-Demo)
+[![Openbayes Demo](https://img.shields.io/static/v1?label=Demo&message=OpenBayes%E8%B4%9D%E5%BC%8F%E8%AE%A1%E7%AE%97&color=green)](https://openbayes.com/console/public/tutorials/gW0ng9jKXfO)
+
+[![llava_video-checkpoints](https://img.shields.io/badge/llava_video-checkpoints-blue)](https://huggingface.co/collections/lmms-lab/llava-next-video-661e86f5e8dabc3ff793c944)
+[![llava_onevision-checkpoints](https://img.shields.io/badge/llava_onevision-checkpoints-blue)](https://huggingface.co/collections/lmms-lab/llava-onevision-66a259c3526e15166d6bba37)
+[![llava_next-interleave_checkpoints](https://img.shields.io/badge/llava_next-interleave_checkpoints-blue)](https://huggingface.co/collections/lmms-lab/llava-next-interleave-66763c55c411b340b35873d1)
+[![llava_next-image_checkpoints](https://img.shields.io/badge/llava_next-image_checkpoints-blue)](https://huggingface.co/lmms-lab)
+
+## Release Notes
+- **[2025/08/29] 🔥 LLaVA-Critic-R1** We release LLaVA-Critic-R1, a family of generative critic VLM trained through GRPO using pairwise critic data. LLaVA-Critic-R1 not only demonstrates strong critic capability, but also achieves state-of-the-art policy performance at the 7B scale. Refer to [LLaVA-Critic-R1](llava-critic-r1/EasyR1/README.md) for more training details.
+
+  📄 **Explore more**:
+  - [LLaVA-Critic-GRPO Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-Critic-GRPO-dataset): Download the dataset.
+  - [LLaVA-Critic-R1-7B](https://huggingface.co/lmms-lab/LLaVA-Critic-R1-7B): LLaVA-Critic-R1 trained based on Qwen-2.5-VL-7B.
+  - [LLaVA-Critic-R1-7B-Plus-Qwen](https://huggingface.co/lmms-lab/LLaVA-Critic-R1-7B-Plus-Qwen): LLaVA-Critic-R1+ trained based on ThinkLite-VL-7B.
+  - [LLaVA-Critic-R1-7B-Plus-Mimo](https://huggingface.co/lmms-lab/LLaVA-Critic-R1-7B-Plus-Mimo): LLaVA-Critic-R1+ trained based on MiMo-VL-7B-RL-2508.
+  - [LLaVA-Critic-R1-7B-Plus-LLaMA32v](https://huggingface.co/lmms-lab/LLaVA-Critic-R1-7B-LLaMA32v): LLaVA-Critic-R1+ trained based on Llama-3.2-11B-Vision-Instruct.
+  - [Paper](https://arxiv.org/abs/2509.00676): Detailed information about LLaVA-Critic-R1.
+
+
+- **[2024/10/04] 🔥 LLaVA-Video** (formerly LLaVA-NeXT-Video) has undergone a major upgrade! We are excited to release **LLaVA-Video-178K**, a high-quality synthetic dataset for video instruction tuning. This dataset includes:
+
+  - 178,510 caption entries
+  - 960,792 open-ended Q&A pairs
+  - 196,198 multiple-choice Q&A items
+
+  Along with this, we’re also releasing the **LLaVA-Video 7B/72B models**, which deliver competitive performance on the latest video benchmarks, including [Video-MME](https://video-mme.github.io/home_page.html#leaderboard), [LongVideoBench](https://longvideobench.github.io/), and [Dream-1K](https://tarsier-vlm.github.io/).
+
+  📄 **Explore more**:
+  - [LLaVA-Video-178K Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K): Download the dataset.
+  - [LLaVA-Video Models](https://huggingface.co/collections/lmms-lab/llava-video-661e86f5e8dabc3ff793c944): Access model checkpoints.
+  - [Paper](http://arxiv.org/abs/2410.02713): Detailed information about LLaVA-Video.
+  - [LLaVA-Video Documentation](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/docs/LLaVA_Video_1003.md): Guidance on training, inference and evaluation.
+
+- [2024/09/13] 🔥 **🚀 [LLaVA-OneVision-Chat](docs/LLaVA_OneVision_Chat.md)**. The new LLaVA-OV-Chat (7B/72B) significantly improves the chat experience of LLaVA-OV. 📄
+  
+  ![](docs/ov_chat_images/chat_results.png)
+
+- [2024/08/06] 🔥 **🚀 [LLaVA-OneVision (OV)](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/)!** The new LLaVA-OV models (0.5B/7B/72B) achieve new state-of-the-art performance across single-image, multi-image, and video benchmarks, sometimes rivaling top commercial models on 47 diverse benchmarks. 📄 Explore More:
+  * [[Paper]](https://arxiv.org/abs/2408.03326): In-depth insights, new emegerging scenarios, ie, strong video understadning through task transfer from images.
+  * [[LLaVA-OV Doc]](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/docs/LLaVA_OneVision.md): Model inference and evaluation guidance.
+  * [[Scripts]](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/scripts/train): Start training models on your single-image/multi-image/video data.
+    
+- [2024/07/16] 🔥 **LLaVA-NeXT-Video** has been upgraded. The new 32B model achieves the best open-source performance on several video benchmarks, including [Video-MME](https://video-mme.github.io/home_page.html#leaderboard). Please refer to [this page](docs/LLaVA-NeXT-Video_0716.md) for details, refer to [llava_next-video_demo](https://huggingface.co/spaces/WildVision/vision-arena) for demo.
+
+
+- [2024/06/23] 🔥 **LLaVA-NeXT-Interleave** is released. We utilize image-text interleaved format to unify multi-image, video, and 3D tasks in one LLM and achieve **SoTA** performance on a wide range of benchmarks. Check out [paper](https://arxiv.org/pdf/2407.07895), [blog](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/), and [checkpoints](https://huggingface.co/collections/lmms-lab/llava-next-interleave-66763c55c411b340b35873d1) to see new capabilities and improved performance! We have released 0.5b, 7b, and 7b-dpo models.
+  * An all-round LLM for multi-image, video, and 3D with strong performance \[[demo](https://huggingface.co/spaces/lmms-lab/LLaVA-NeXT-Interleave-Demo)\]
+  * Construct interleave training data [**M4-Instruct**](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data)
+  * Construct multi-image benchmark [**LLaVA-Interleave Bench**](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Interleave-Bench)
+
+
+- [2024/05/25] 🔥 Wondering "[What Else Influences Visual Instruction Tuning Beyond Data?](https://llava-vl.github.io/blog/2024-05-25-llava-next-ablations/)" Our new [blog](https://llava-vl.github.io/blog/2024-05-25-llava-next-ablations/) summarizes empirical explorations to ablate the various design choices in improving LMMs except instruct data itself. Meanwhile, open-source the recapioned high-quality data using LLaVA-NeXT-34B on [[COCO]](https://huggingface.co/datasets/lmms-lab/LLaVA-ReCap-118K) [[LCS]](https://huggingface.co/datasets/lmms-lab/LLaVA-ReCap-558K) [[CC3M]](https://huggingface.co/datasets/lmms-lab/LLaVA-ReCap-CC3M).
+  * Architectures (LMM & Vision Encoder)
+  * Visual Representations (Resolution & # Tokens)
+  * Training Strategies (High-quality data & Trainable modules)
+
+- [2024/05/10] 🔥 **LLaVA-NeXT** (Stronger) models are released, with support of stronger LMM inlcuding LLama-3 (8B) and Qwen-1.5 (72B/110B) Check out [[blog](https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/)] and [[checkpoints](https://huggingface.co/lmms-lab)] to see improved performance!
+- [2024/05/10] 🔥 **LLaVA-NeXT** (Video) is released. The image-only-trained LLaVA-NeXT model is surprisingly strong on video tasks with zero-shot modality transfer. DPO training with AI feedback on videos can yield significant improvement. [[Blog](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/)], [[checkpoints](https://huggingface.co/collections/lmms-lab/llava-next-video-661e86f5e8dabc3ff793c944)] and [[sglang](https://github.com/sgl-project/sglang)]
+- [2024/01/30] 🔥 **LLaVA-NeXT** is out! With additional scaling to LLaVA-1.5, LLaVA-NeXT-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the [blog post](https://llava-vl.github.io/blog/2024-01-30-llava-next/), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). Training/eval data and scripts coming soon.
+<details>
+<summary>More</summary>
+  
+- [2024/03/10] 🔥 Releasing **LMMs-Eval**, a highly efficient evaluation pipeline we used when developing LLaVA-NeXT. It supports the evaluation of LMMs on dozens of public datasets and allows new dataset onboarding, making the dev of new LMMs much faster. [[Blog](https://lmms-lab.github.io/lmms-eval-blog/lmms-eval-0.1/)] [[Codebase](https://github.com/EvolvingLMMs-Lab/lmms-eval)]
+  
+- [2023/11/10] [LLaVA-Plus](https://llava-vl.github.io/llava-plus/) is released: Learning to Use Tools for Creating Multimodal Agents, with LLaVA-Plus (LLaVA that Plug and Learn to Use Skills). [[Project Page](https://llava-vl.github.io/llava-plus/)] [[Demo](https://llavaplus.ngrok.io/)] [[Code](https://github.com/LLaVA-VL/LLaVA-Plus-Codebase)] [[Paper](https://arxiv.org/abs/2311.05437)]
+- [2023/11/02] [LLaVA-Interactive](https://llava-vl.github.io/llava-interactive/) is released: Experience the future of human-AI multimodal interaction with an all-in-one demo for Image Chat, Segmentation, Generation and Editing. [[Project Page](https://llava-vl.github.io/llava-interactive/)] [[Demo](https://llavainteractive.ngrok.io/)] [[Code](https://github.com/LLaVA-VL/LLaVA-Interactive-Demo)] [[Paper](https://arxiv.org/abs/2311.00571)]
+- [2023/10/26] 🔥 LLaVA-1.5 with LoRA achieves comparable performance as full-model finetuning, with a reduced GPU RAM requirement ([ckpts](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md#llava-v15), [script](https://github.com/haotian-liu/LLaVA#train)). We also provide a [doc](https://github.com/haotian-liu/LLaVA/blob/main/docs/Finetune_Custom_Data.md) on how to finetune LLaVA-1.5 on your own dataset with LoRA.
+- [2023/10/12] Check out the Korean LLaVA (Ko-LLaVA), created by ETRI, who has generously supported our research! [[🤗 Demo](https://huggingface.co/spaces/etri-vilab/Ko-LLaVA)]
+- [2023/10/05] 🔥 LLaVA-1.5 is out! Achieving SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Check out the [technical report](https://arxiv.org/abs/2310.03744), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)!
+- [2023/09/26] LLaVA is improved with reinforcement learning from human feedback (RLHF) to improve fact grounding and reduce hallucination. Check out the new SFT and RLHF checkpoints at project [[LLavA-RLHF]](https://llava-rlhf.github.io/)
+- [2023/09/22] [LLaVA](https://arxiv.org/abs/2304.08485) is accepted by NeurIPS 2023 as **oral presentation**, and [LLaVA-Med](https://arxiv.org/abs/2306.00890) is accepted by NeurIPS 2023 Datasets and Benchmarks Track as **spotlight presentation**.
+- [2023/11/06] Support **Intel** dGPU and CPU platforms. [More details here.](https://github.com/haotian-liu/LLaVA/tree/intel/docs/intel)
+- [2023/10/12] LLaVA is now supported in [llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436) with 4-bit / 5-bit quantization support!
+- [2023/10/11] The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)!
+- [2023/10/10] [Roboflow Deep Dive](https://blog.roboflow.com/first-impressions-with-llava-1-5/): First Impressions with LLaVA-1.5.
+- [2023/09/20] We summarize our empirical study of training 33B and 65B LLaVA models in a [note](https://arxiv.org/abs/2309.09958). Further, if you are interested in the comprehensive review, evolution and trend of multimodal foundation models, please check out our recent survey paper [``Multimodal Foundation Models: From Specialists to General-Purpose Assistants''.](https://arxiv.org/abs/2309.10020)
+<p align="center">
+  <img src="https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings/blob/main/images/mfm_evolution.jpeg?raw=true" width=50%/>
+</p>
+
+- [2023/07/19] 🔥 We release a major upgrade, including support for LLaMA-2, LoRA training, 4-/8-bit inference, higher resolution (336x336), and a lot more. We release [LLaVA Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) for benchmarking open-ended visual chat with results from Bard and Bing-Chat. We also support and verify training with RTX 3090 and RTX A6000. Check out [LLaVA-from-LLaMA-2](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_from_LLaMA2.md), and our [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)!
+- [2023/06/26] [CVPR 2023 Tutorial](https://vlp-tutorial.github.io/) on **Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4**!  Please check out [[Slides](https://datarelease.blob.core.windows.net/tutorial/vision_foundation_models_2023/slides/Chunyuan_cvpr2023_tutorial_lmm.pdf)] [[Notes](https://arxiv.org/abs/2306.14895)] [[YouTube](https://youtu.be/mkI7EPD1vp8)] [[Bilibli](https://www.bilibili.com/video/BV1Ng4y1T7v3/)].
+- [2023/06/11] We released the preview for the most requested feature: DeepSpeed and LoRA support!  Please see documentations [here](./docs/LoRA.md).
+- [2023/06/01] We released **LLaVA-Med: Large Language and Vision Assistant for Biomedicine**, a step towards building biomedical domain large language and vision models with GPT-4 level capabilities.  Checkout the [paper](https://arxiv.org/abs/2306.00890) and [page](https://github.com/microsoft/LLaVA-Med).
+- [2023/05/06] We are releasing [LLaVA-Lighting-MPT-7B-preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview), based on MPT-7B-Chat!  See [here](#LLaVA-MPT-7b) for more details.
+- [2023/05/02] 🔥 We are releasing LLaVA-Lighting!  Train a lite, multimodal GPT-4 with just $40 in 3 hours!  See [here](#train-llava-lightning) for more details.
+- [2023/04/27] Thanks to the community effort, LLaVA-13B with 4-bit quantization allows you to run on a GPU with as few as 12GB VRAM!  Try it out [here](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava).
+- [2023/04/17] 🔥 We released **LLaVA: Large Language and Vision Assistant**. We propose visual instruction tuning, towards building large language and vision models with GPT-4 level capabilities.  Checkout the [paper](https://arxiv.org/abs/2304.08485) and [demo](https://llava.hliu.cc/).
+
+</details>
+
+<!-- <a href="https://llava.hliu.cc/"><img src="assets/demo.gif" width="70%"></a> -->
+
+**Usage and License Notices**: This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the [OpenAI Terms of Use](https://openai.com/policies/terms-of-use) for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. [Llama-1/2 community license](https://ai.meta.com/llama/license/) for LLaMA-2 and Vicuna-v1.5, [Tongyi Qianwen RESEARCH LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat/blob/main/LICENSE) and [Llama-3 Research License](https://llama.meta.com/llama3/license/)). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.
+
+## Models & Scripts
+
+### Installation
+
+#### 1. **Clone this repository and navigate to the LLaVA folder:**
+```bash
+git clone https://github.com/LLaVA-VL/LLaVA-NeXT
+cd LLaVA-NeXT
+```
+
+#### 2. **Install the inference package:**
+```bash
+conda create -n llava python=3.10 -y
+conda activate llava
+pip install --upgrade pip  # Enable PEP 660 support.
+pip install -e ".[train]"
+```
+
+### Project Navigation
+Please checkout the following page for more inference & evaluation details.
+
+#### - **LLaVA-OneVision: Easy Task Transfer**
+- [LLaVA-OneVision](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/docs/LLaVA_OneVision.md): for demo inference. The evaluation code is in [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval).
+
+#### - **LLaVA-NeXT: Stronger LLMs Supercharge Multimodal Capabilities in the Wild**
+- [LLaVA-NeXT-Image](./docs/LLaVA-NeXT.md): for image demo inference and evaluation of stronger LMMs using [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval).
+
+
+#### - LLaVA-NeXT: A Strong Zero-shot Video Understanding Model
+- [LLaVA-NeXT-Video](./docs/LLaVA-NeXT-Video.md): for video inference and evaluation scripts. We recommend to use [LMMs-video](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for evaluation.
+
+#### - LLaVA-NeXT: Tackling Multi-image, Video, and 3D in Large Multimodal Models
+- [LLaVA-NeXT-Interleave](./docs/LLaVA-NeXT-Interleave.md): for multi-image demo and evaluation scripts.
+
+## SGLang for SpeedUp Inference and Deployment
+
+We use [SGLang](https://github.com/sgl-project/sglang) to speed up inference and deployment of LLaVA-NeXT. You could make LLaVA-NeXT as a backend API service with SGLang.
+
+**Prepare Environment**:
+    Following the instruction in the [sglang](https://github.com/sgl-project/sglang?tab=readme-ov-file#install)
+
+### LLaVA-NeXT/OneVision
+
+Checkout the HTTP Post/Get and SRT usage at [sglang/examples/runtime/llava_onevision](https://github.com/sgl-project/sglang/tree/main/examples/runtime/llava_onevision)
+
+### LLaVA-NeXT (Video)
+
+**Launch and Run on (K) Nodes**:
+- Go to sglang project
+    ```
+    cd PATH_TO/sglang
+    ```
+- First node:
+    ```sh
+    bash examples/usage/llava_video/srt_example_llava_v.sh K 0 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
+    (e.g. bash examples/usage/llava_video/srt_example_llava_v.sh K 0 examples/usage/llava_video/videos/Q98Z4OTh8RwmDonc.mp4 lmms-lab/LLaVA-NeXT-Video-7B-DPO 16)
+    ```
+- Second node:
+    ```sh
+    bash examples/usage/llava_video/srt_example_llava_v.sh K 1 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
+    ```
+- The K node:
+    ```sh
+    bash examples/usage/llava_video/srt_example_llava_v.sh K K-1 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
+    ```
+
+
+## Citation
+
+If you find it useful for your research and applications, please cite related papers/blogs using this BibTeX:
+```bibtex
+@article{li2024llava,
+  title={LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models},
+  author={Li, Feng and Zhang, Renrui and Zhang, Hao and Zhang, Yuanhan and Li, Bo and Li, Wei and Ma, Zejun and Li, Chunyuan},
+  journal={arXiv preprint arXiv:2407.07895},
+  year={2024}
+}
+
+@misc{li2024llavanext-ablations,
+	title={LLaVA-NeXT: What Else Influences Visual Instruction Tuning Beyond Data?},
+	url={https://llava-vl.github.io/blog/2024-05-25-llava-next-ablations/},
+	author={Li, Bo and Zhang, Hao and Zhang, Kaichen and Guo, Dong and Zhang, Yuanhan and Zhang, Renrui and Li, Feng and Liu, Ziwei and Li, Chunyuan},
+	month={May},
+	year={2024}
+}
+
+@misc{li2024llavanext-strong,
+    title={LLaVA-NeXT: Stronger LLMs Supercharge Multimodal Capabilities in the Wild},
+    url={https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/},
+    author={Li, Bo and Zhang, Kaichen and Zhang, Hao and Guo, Dong and Zhang, Renrui and Li, Feng and Zhang, Yuanhan and Liu, Ziwei and Li, Chunyuan},
+    month={May},
+    year={2024}
+}
+
+@misc{zhang2024llavanext-video,
+  title={LLaVA-NeXT: A Strong Zero-shot Video Understanding Model},
+  url={https://llava-vl.github.io/blog/2024-04-30-llava-next-video/},
+  author={Zhang, Yuanhan and Li, Bo and Liu, haotian and Lee, Yong jae and Gui, Liangke and Fu, Di and Feng, Jiashi and Liu, Ziwei and Li, Chunyuan},
+  month={April},
+  year={2024}
+}
+
+@misc{liu2024llavanext,
+    title={LLaVA-NeXT: Improved reasoning, OCR, and world knowledge},
+    url={https://llava-vl.github.io/blog/2024-01-30-llava-next/},
+    author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Li, Bo and Zhang, Yuanhan and Shen, Sheng and Lee, Yong Jae},
+    month={January},
+    year={2024}
+}
+
+@misc{liu2023improvedllava,
+      title={Improved Baselines with Visual Instruction Tuning}, 
+      author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
+      publisher={arXiv:2310.03744},
+      year={2023},
+}
+
+@misc{liu2023llava,
+      title={Visual Instruction Tuning}, 
+      author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
+      publisher={NeurIPS},
+      year={2023},
+}
+```
+
+## Acknowledgement
+
+- [Vicuna](https://github.com/lm-sys/FastChat): the codebase we built upon, and our base model Vicuna-13B that has the amazing language capabilities!
+- The LLaVA-NeXT project is currently maintained by the team along with our contributors (listed alphabetically by the first names): [Bo Li](https://brianboli.com/), [Dong Guo](https://www.linkedin.com/in/dongguoset/), [Feng Li](https://scholar.google.com/citations?hl=zh-CN&user=ybRe9GcAAAAJ&view_op=list_works&sortby=pubdate), [Hao Zhang](https://scholar.google.com/citations?user=B8hPxMQAAAAJ&hl=en), [Kaichen Zhang](https://www.linkedin.com/in/kaichen-zhang-014b17219/?originalSubdomain=sg), [Renrui Zhang](https://zrrskywalker.github.io/), [Yuanhan Zhang](https://zhangyuanhan-ai.github.io/), led by [Chunyuan Li](https://chunyuan.li/) and with the guidance and help from [Haotian Liu](https://hliu.cc/).
+- The `lmms-eval` framework and its core contributors, including Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, and Kairui Hu, for their support on the evaluation side.
+
+## Related Projects
+
+- [Instruction Tuning with GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://github.com/microsoft/LLaVA-Med)
+- [Otter: In-Context Multi-Modal Instruction Tuning](https://github.com/Luodian/Otter)
+
+For future project ideas, please check out:
+- [SEEM: Segment Everything Everywhere All at Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)
+- [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) to detect, segment, and generate anything by marrying [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment-Anything](https://github.com/facebookresearch/segment-anything).

MedEvalKit/LLaVA-NeXT/cog.yaml

@@ -0,0 +1,37 @@
+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  gpu: true
+
+  python_version: "3.11"
+
+  python_packages:
+    - "torch==2.0.1"
+    - "accelerate==0.21.0"
+    - "bitsandbytes==0.41.0"
+    - "deepspeed==0.9.5"
+    - "einops-exts==0.0.4"
+    - "einops==0.6.1"
+    - "gradio==3.35.2"
+    - "gradio_client==0.2.9"
+    - "httpx==0.24.0"
+    - "markdown2==2.4.10"
+    - "numpy==1.26.0"
+    - "peft==0.4.0"
+    - "scikit-learn==1.2.2"
+    - "sentencepiece==0.1.99"
+    - "shortuuid==1.0.11"
+    - "timm==0.6.13"
+    - "tokenizers==0.13.3"
+    - "torch==2.0.1"
+    - "torchvision==0.15.2"
+    - "transformers==4.31.0"
+    - "wandb==0.15.12"
+    - "wavedrom==2.0.3.post3"
+    - "Pygments==2.16.1"
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.0.3/pget" && chmod +x /usr/local/bin/pget
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"

MedEvalKit/LLaVA-NeXT/docs/LLaVA-NeXT-Interleave.md

@@ -0,0 +1,53 @@
+
+# LLaVA-NeXT: Tackling Multi-image, Video, and 3D in Large Multimodal Models
+
+## Contents
+- [Demo](#demo)
+- [Evaluation](#evaluation)
+
+## Demo
+
+> make sure you installed the LLaVA-NeXT model files via outside REAME.md
+
+1. **Example model:** `lmms-lab/llava-next-interleave-7b`
+
+
+To run a demo, execute:
+```bash
+# If you find any bug when running the demo, please make sure checkpoint path contains 'qwen'.
+# You can try command like 'mv llava-next-interleave-7b llava-next-interleave-qwen-7b'
+python playground/demo/interleave_demo.py --model_path path/to/ckpt
+```
+
+## Evaluation
+
+### Preparation
+
+Please download the evaluation data and its metadata from the following links:
+
+1. **llava-interleave-bench:** [here](https://huggingface.co/datasets/lmms-lab/llava-interleave-bench).
+
+Unzip eval_images.zip and there are Split1 and Split2 in it.
+Organize the downloaded data into the following structure:
+```
+
+interleave_data
+├── Split1
+│   ├── ...
+│   └── ...
+|
+├── Split2
+|   ├── ...
+│   └── ...
+├── multi_image_in_domain.json
+├── multi_image_out_domain.json
+└── multi_view_in_domain.json
+```
+
+### Inference and Evaluation
+Example:
+Please first edit /path/to/ckpt to the path of checkpoint, /path/to/images to the path of "interleave_data" in scripts/interleave/eval_all.sh and then run
+```bash
+bash scripts/interleave/eval_all.sh
+```
+

MedEvalKit/LLaVA-NeXT/docs/LLaVA-NeXT-Video.md

@@ -0,0 +1,81 @@
+
+# LLaVA-NeXT: A Strong Zero-shot Video Understanding Model
+
+## Contents
+- [Demo](#demo)
+- [Evaluation](#evaluation)
+
+## Demo
+
+> make sure you installed the LLaVA-NeXT model files via outside REAME.md
+
+1. **Example model:** `lmms-lab/LLaVA-NeXT-Video-7B-DPO`
+
+2. **Prompt mode:** `vicuna_v1` (use `mistral_direct` for `lmms-lab/LLaVA-NeXT-Video-34B-DPO`)
+
+3. **Sampled frames:** `32` (Defines how many frames to sample from the video.)
+
+4. **Spatial pooling stride:** `2` (With original tokens for one frame at 24x24, if stride=2, then the tokens for one frame are 12x12.)
+
+5. **Spatial pooling mode:** `average` (Options: `average`, `max`.)
+
+6. **Local video path:** `./data/llava_video/video-chatgpt/evaluation/Test_Videos/v_Lf_7RurLgp0.mp4`
+
+To run a demo, execute:
+```bash
+bash scripts/video/demo/video_demo.sh ${Example model} ${Prompt mode} ${Sampled frames} ${Spatial pooling stride} ${Spatial pooling mode} grid True ${Video path at local}
+```
+Example:
+```bash
+bash scripts/video/demo/video_demo.sh lmms-lab/LLaVA-NeXT-Video-7B-DPO vicuna_v1 32 2 average no_token True playground/demo/xU25MMA2N4aVtYay.mp4
+```
+
+**IMPORTANT** Please refer to [Latest video model](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/inference/docs/LLaVA-NeXT-Video_0716.md) for the runnning of the latest model.
+
+## Evaluation
+
+### Preparation
+
+Please download the evaluation data and its metadata from the following links:
+
+1. **video-chatgpt:** [here](https://github.com/mbzuai-oryx/Video-ChatGPT/blob/main/quantitative_evaluation/README.md#video-based-generative-performance-benchmarking).
+2. **video_detail_description:** [here](https://mbzuaiac-my.sharepoint.com/personal/hanoona_bangalath_mbzuai_ac_ae/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FQuantitative%5FEvaluation%2Fbenchamarking%2FTest%5FHuman%5FAnnotated%5FCaptions%2Ezip&parent=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FQuantitative%5FEvaluation%2Fbenchamarking&ga=1).
+3. **activity_qa:** [here](https://mbzuaiac-my.sharepoint.com/personal/hanoona_bangalath_mbzuai_ac_ae/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FData%2FActivityNet%5FTest%2D1%2D3%5Fvideos%2Ezip&parent=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FData&ga=1) and [here](https://github.com/MILVLG/activitynet-qa/tree/master/dataset).
+
+Organize the downloaded data into the following structure:
+```
+LLaVA-NeXT
+├── llava
+├── scripts
+└── data
+    └── llava_video
+        ├── video-chatgpt
+        │   ├── Test_Videos
+        │   ├── consistency_qa.json
+        │   ├── consistency_qa_test.json
+        │   ├── consistency_qa_train.json
+        ├── video_detail_description
+        │   └── Test_Human_Annotated_Captions
+        └── ActivityNet-QA
+            ├── all_test
+            ├── test_a.json
+            └── test_b.json
+```
+
+### Inference and Evaluation
+
+Example for video detail description evaluation (additional scripts are available in `scripts/eval`):
+```bash
+bash scripts/video/eval/video_detail_description_eval_shard.sh ${Example model} ${Prompt mode} ${Sampled frames} ${Spatial pooling stride} True 8
+```
+Example:
+```bash
+bash scripts/eval/video_detail_description_eval_shard.sh liuhaotian/llava-v1.6-vicuna-7b vicuna_v1 32 2 True 8 
+```
+
+### GPT Evaluation Example (Optional if the above step is completed)
+
+Assuming you have `pred.json` (model-generated predictions) for model `llava-v1.6-vicuna-7b` at `./work_dirs/eval_video_detail_description/llava-v1.6-vicuna-7b_vicuna_v1_frames_32_stride_2`:
+```bash
+bash scripts/video/eval/video_description_eval_only.sh llava-v1.6-vicuna-7b_vicuna_v1_frames_32_stride_2
+```

MedEvalKit/LLaVA-NeXT/docs/LLaVA-NeXT-Video_0716.md

@@ -0,0 +1,42 @@
+## LLaVA-NeXT-Video is upgraded 🚀
+
+In our [LLaVA-Video blog](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/) released this April, we shared two key observations: 
+- 🎬 AnyRes provides a shared and flexible representation between images and videos, and thus accommodates capability transfer between the two most common vision signals. Therefore, stronger image LMMs can naturally lead to stronger zero-shot video LMMs. 
+- 🗂️ There is a lack of high-quality language-video data, including video instruction-following data, and thus naive tuning on existing public data at that time results in performance degradation. Therefore, there is an urgent need to build high-quality video captions and QA datasets to train LMMs for improved video performance.
+
+Based on the insights, the new LLaVA-NeXT-Video in this release improves from two aspects:
+
+- 🎬 A stronger image LMMs ([LLaVA-NeXT-32B-Qwen](https://huggingface.co/lmms-lab/llava-next-qwen-32b)), which is built by initializing from Qwen-1.5 32B LLM. We further initialize our video training from this image checkpoint.
+- 🗂️ A new high-quality video dataset with 830k samples. It is combined with LLaVA-1.6 image training data, and applying the same image-video mixed training procedure leads to the new video model.
+The new model achieves the best open-source performance in several video benchmarks including [Video-MME](https://video-mme.github.io/home_page.html#leaderboard).
+
+### Resources
+- **Model Card**: [LLaVA-NeXT-Video-32B-Qwen on Hugging Face](https://huggingface.co/lmms-lab/LLaVA-NeXT-Video-32B-Qwen)
+- **Inference Script**:
+  ```bash
+  bash scripts/video/demo/video_demo.sh lmms-lab/LLaVA-NeXT-Video-32B-Qwen qwen_1_5 32 2 average grid True playground/demo/xU25MMA2N4aVtYay.mp4
+  ```
+
+### Evaluation Results
+| Model                       | NextQA-MC | video-mme(overall) |        | Egochema | Perception Test  (val) |
+|-----------------------------|-----------|--------------------|--------|----------|------------------------|
+|                             |           | w/o subs           | w subs |          |                        |
+| **Proprietary**                 |           |                    |        |          |                        |
+| GPT-4o                      | -         | 71.9               | 77.2   | 72.2     | -                      |
+| Gemini 1.5 Pro              | -         | 75.0               | 81.3   | 72.2     | -                      |
+| **Open-Source**                 |           |                    |        |          |                        |
+| VideoLLaMA 2 (8x7B)         | 76.3*     | 47.9               | 50.3   | 53.3     | 51.2*                  |
+| VILA-1.5-34B                | 67.89*    | 60.1               | 61.1   | 58.04*   | 54                     |
+| LLaVA-NeXT-Video (Qwen-32B) | 77.31     | 60.2               | 63.0   | 60.85    | 59.38                  |
+
+_*Results are reproduced by [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval). Please refer to the lmms-eval to reproduce the results._
+
+### Citations
+```bibtex
+@misc{zhang2024llavanextvideo,
+  title={LLaVA-NeXT: A Strong Zero-shot Video Understanding Model},
+  url={https://llava-vl.github.io/blog/2024-04-30-llava-next-video/},
+  author={Zhang, Yuanhan and Li, Bo and Liu, haotian and Lee, Yong jae and Gui, Liangke and Fu, Di and Feng, Jiashi and Liu, Ziwei and Li, Chunyuan},
+  month={April},
+  year={2024}
+}

MedEvalKit/LLaVA-NeXT/docs/LLaVA-NeXT.md

@@ -0,0 +1,93 @@
+# LLaVA-NeXT: Stronger LLMs Supercharge Multimodal Capabilities in the Wild
+
+## Quick Start With HuggingFace
+First please install our repo with code and environments: `pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git`
+
+Here is a quick inference code using [`llavanext-llama3-8B`](https://huggingface.co/lmms-lab/llama3-llava-next-8b) as an example. You will need to install [`flash-attn`](https://github.com/Dao-AILab/flash-attention) to use this code snippet. If you don't want to install it, you can set `attn_implementation=None` when load_pretrained_model
+```python
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+
+from PIL import Image
+import requests
+import copy
+import torch
+
+pretrained = "lmms-lab/llama3-llava-next-8b"
+model_name = "llava_llama3"
+device = "cuda"
+device_map = "auto"
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map) # Add any other thing you want to pass in llava_model_args
+
+model.eval()
+model.tie_weights()
+
+url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+image_tensor = process_images([image], image_processor, model.config)
+image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
+
+conv_template = "llava_llama_3" # Make sure you use correct chat template for different models
+question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
+conv = copy.deepcopy(conv_templates[conv_template])
+conv.append_message(conv.roles[0], question)
+conv.append_message(conv.roles[1], None)
+prompt_question = conv.get_prompt()
+
+input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+image_sizes = [image.size]
+
+
+cont = model.generate(
+    input_ids,
+    images=image_tensor,
+    image_sizes=image_sizes,
+    do_sample=False,
+    temperature=0,
+    max_new_tokens=256,
+    # Modalities should be the same size as the batch size
+    modalities=["image"]*input_ids.shape[0]
+)
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
+print(text_outputs)
+# The image shows a radar chart, also known as a spider chart or a web chart, which is a type of graph used to display multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Each axis represents a different variable, and the values are plotted along each axis and connected to form a polygon.\n\nIn this particular radar chart, there are several axes labeled with different variables, such as "MM-Vet," "LLaVA-Bench," "SEED-Bench," "MMBench-CN," "MMBench," "TextVQA," "VizWiz," "GQA," "BLIP-2," "InstructBLIP," "Owen-VL-Chat," and "LLaVA-1.5." These labels suggest that the chart is comparing the performance of different models or systems across various benchmarks or tasks, such as machine translation, visual question answering, and text-based question answering.\n\nThe chart is color-coded, with each color representing a different model or system. The points on the chart are connected to form a polygon, which shows the relative performance of each model across the different benchmarks. The closer the point is to the outer edge of the
+```
+
+## Evaluation
+
+**Install the evaluation package:**
+```bash
+# make sure you installed the LLaVA-NeXT model files via outside REAME.md
+pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+```
+
+### Check the evaluation results with [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval)
+Our models' evaluation results can be fully reproduced by using the [`lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval) toolkit. After you install lmms-eval and llava, you can run the evaluation using the following commands. To run following commands, you will have to install [`flash-attn`](https://github.com/Dao-AILab/flash-attention). If you do not want to install it, you can disable the flash-attn by specifying it in `--model_args pretrained=lmms-lab/llama3-llava-next-8b,conv_template=llava_llama_3,attn_implementation=None`.
+
+Please note that different torch versions might causing the results to vary.
+
+```shell
+# Evaluating Llama-3-LLaVA-NeXT-8B on multiple datasets
+accelerate launch --num_processes=8 \
+  -m lmms_eval \
+  --model llava \
+  --model_args pretrained=lmms-lab/llama3-llava-next-8b,conv_template=llava_llama_3 \
+  --tasks ai2d,chartqa,docvqa_val,mme,mmbench_en_dev \
+  --batch_size 1 \
+  --log_samples \
+  --log_samples_suffix llava_next \
+  --output_path ./logs/
+
+# Evaluating LLaVA-NeXT-72B on multiple datasets
+accelerate launch --num_processes=1 \
+  -m lmms_eval \
+  --model llava \
+  --model_args pretrained=lmms-lab/llava-next-72b,conv_template=qwen_1_5,model_name=llava_qwen,device_map=auto \
+  --tasks ai2d,chartqa,docvqa_val,mme,mmbench_en_dev \
+  --batch_size 1 \
+  --log_samples \
+  --log_samples_suffix llava_next \
+  --output_path ./logs/
+```

MedEvalKit/LLaVA-NeXT/docs/LLaVA_OneVision.md

@@ -0,0 +1,121 @@
+# LLaVA OneVision
+
+## Model Details
+
+LLaVA OneVision is a multi-modal model capable of processing images, text, image-text interleaved inputs, and videos. The model is trained in multiple stages:
+
+1. Stage-1: Initial training on 558K samples from the LCS dataset.
+2. Stage-1.5: Training on 4M high-quality samples with detailed captions, OCR and knowledge data.
+3. Stage-2: 
+   - Single-Image: Training on 3.2M instruction-following image samples.
+   - OneVision: Training on 1.6M single-image, multi-image and video samples with instructions.
+
+Key features:
+- Supports various input resolutions up to 2304 * 2304 pixels.
+- Single image input is represented by 729 * (9+1) tokens at most under `anyres_max_9` mode.
+- Supports multi-image and video inputs. Multi-image input is represented by 729 token for each image, and video input is represented by 196 token for each frame.
+- Available in three sizes: 0.5B, 7B and 72B parameter versions, fit for different memory and inference latency requirements.
+
+Some Implementation Details:
+- Trained using a combination of vision-specific (AdamW, 2e-6) and language model (AdamW, 1e-5) learning rates.
+- Each stage is trained for 1 epoch.
+
+The model uses [SO400M](https://huggingface.co/collections/google/siglip-659d5e62f0ae1a57ae0e83ba) as the vision encoder and [Qwen-2.0](https://huggingface.co/docs/transformers/model_doc/qwen2) as the language model, with trainable components including a projector and the full model in later stages.
+
+We recommend to use the scripts in [training](../scripts/) to get the details of the training process.
+
+## Inference Guidance
+
+We recommend to follow the [tutorial](./LLaVA_OneVision_Tutorials.ipynb) to get started on using our most basic 0.5B model for image, text, image-text interleaved, and video input. We use our 0.5B version as an example. This could be running on a GPU with 4GB memory. And with the following examples, you could see it's surprisingly have promising performance on understanding the image, interleaved image-text, and video. Tiny but mighty!
+
+## Evaluation Guidance
+
+We use the [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) toolkit to evaluate our models. Ensure you have installed the LLaVA-NeXT model files as per the instructions in the main README.md.
+
+Install lmms-eval:
+
+> pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+
+### Reproducing Evaluation Results
+
+Our models' evaluation results can be fully reproduced using the lmms-eval toolkit. After installing lmms-eval and llava, you can run the evaluation using the following commands.
+
+Note: These commands require flash-attn. If you prefer not to install it, disable flash-attn by adding `attn_implementation=None` to the `--model_args` parameter.
+
+Important: Different torch versions may cause slight variations in results. By default in `lmms-eval`, the requirement for torch version is set to the latest version. In `llava` repo, the torch version is set to `2.1.2`. Torch version `2.1.2` would be stable for both `llava` and `lmms-eval`
+
+### Evaluating LLaVA-OneVision on multiple datasets
+
+We recommend the developers and researchers to thoroughly evaluate the models on more datasets to get a comprehensive understanding of their performance in different scenarios. So we provide a comprehensive list of datasets for evaluation, and welcome to incoporate more evaluation tasks. Please refer to the [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) for more details.
+
+
+Task: single-image tasks.
+
+```bash
+# image tasks
+accelerate launch --num_processes=8 \
+-m lmms_eval \
+--model llava_onevision \
+--model_args pretrained=lmms-lab/llava-onevision-qwen2-0.5b-si,conv_template=qwen_1_5,model_name=llava_qwen \
+--tasks ai2d,chartqa,docvqa_val,infovqa_val,mme,realworldqa,mathvista_testmini,llava_in_the_wild,mmvet,mmbench_en_dev,ocrbench,mmmu,mathverse_testmini_vision_intensive,mathverse_testmini_vision_only,seedbench,scienceqa_img,mmstar \
+--batch_size 1 \
+--log_samples \
+--log_samples_suffix llava_onevision \
+--output_path ./logs/
+```
+
+## Data Visualization
+
+We provide visualization scripts to analyze the training data distribution in the notebook [llava_ov_plot.ipynb](../llava_ov_plot.ipynb). The notebook includes:
+
+- **Training Data Distribution by Category**: Visualizes data across Doc/Chart/Screen, General, Pure Language, Math/Reasoning, and General OCR categories with nested pie charts showing detailed dataset contributions.
+- **Training Data Distribution by Modality**: Shows the breakdown of single-image, multi-image, and video data with percentage calculations.
+- **Polar/Radar Charts**: Advanced visualizations for comparing model performance across different benchmarks.
+- **Timeline Visualizations**: Tracks the evolution of model capabilities over time.
+
+Task: video tasks. The video tasks are more computationally expensive. We recommend running them on a machine with a GPU with at least 16GB memory.
+
+```bash
+# video tasks
+accelerate launch --num_processes=8 \
+-m lmms_eval \
+--model llava_onevision \
+--model_args pretrained=lmms-lab/llava-onevision-qwen2-0.5b-ov,conv_template=qwen_1_5,model_name=llava_qwen \
+--tasks activitynetqa,videochatgpt,nextqa_mc_test,egoschema,video_dc499,videmme,videomme_w_subtitle,perceptiontest_val_mc \
+--batch_size 1 \
+--log_samples \
+--log_samples_suffix llava_onevision \
+--output_path ./logs/
+```
+
+## Data Visualization
+
+We provide visualization scripts to analyze the training data distribution in the notebook [llava_ov_plot.ipynb](../playground/llava_ov_plot.ipynb). The notebook includes:
+
+- **Training Data Distribution by Category**: Visualizes data across Doc/Chart/Screen, General, Pure Language, Math/Reasoning, and General OCR categories with nested pie charts showing detailed dataset contributions.
+- **Training Data Distribution by Modality**: Shows the breakdown of single-image, multi-image, and video data with percentage calculations.
+- **Polar/Radar Charts**: Advanced visualizations for comparing model performance across different benchmarks.
+- **Timeline Visualizations**: Tracks the evolution of model capabilities over time.
+
+Task: interleave tasks (`llava-interleave-bench` already contains most of existing image-text tasks). `mmmu_test` contains single image and multiple images as input, we run the model to obtain a submission file and you need to submit it to the [leaderboard](https://eval.ai/web/challenges/challenge-page/1700/overview) to get the accuracy for MMMU (multi-image) result.
+
+```bash
+accelerate launch --num_processes=8 \
+-m lmms_eval \
+--model llava_onevision \
+--model_args pretrained=lmms-lab/llava-onevision-qwen2-0.5b-ov,conv_template=qwen_1_5,model_name=llava_qwen \
+--tasks llava-interleave-bench,muirbench,mmmu_test \
+--batch_size 1 \
+--log_samples \
+--log_samples_suffix llava_onevision \
+--output_path ./logs/
+```
+
+## Data Visualization
+
+We provide visualization scripts to analyze the training data distribution in the notebook [llava_ov_plot.ipynb](../playground/llava_ov_plot.ipynb). The notebook includes:
+
+- **Training Data Distribution by Category**: Visualizes data across Doc/Chart/Screen, General, Pure Language, Math/Reasoning, and General OCR categories with nested pie charts showing detailed dataset contributions.
+- **Training Data Distribution by Modality**: Shows the breakdown of single-image, multi-image, and video data with percentage calculations.
+- **Polar/Radar Charts**: Advanced visualizations for comparing model performance across different benchmarks.
+- **Timeline Visualizations**: Tracks the evolution of model capabilities over time.

MedEvalKit/LLaVA-NeXT/docs/LLaVA_OneVision_Chat.md

@@ -0,0 +1,157 @@
+# LLaVA-OneVision-Chat: Improving Chat with Preference Learning
+
+[LLaVA-OneVision](https://arxiv.org/abs/2408.03326) has demonstrated strong multimodal capabilities, showing excellent performance on various benchmarks in single-image, multi-image and video scenarios. However, we see potential for further improvement, particularly in its visual chat abilities. To achieve this, we've focused on enhancing the model through preference alignment, and our early experiments have produced some promising insights.
+
+### Key Observations:
+
+- **Impact of Preference Learning**: By incorporating alignment learning—whether through human feedback or AI-generated feedback—we've observed a notable improvement in LLaVA-OneVision's chat experience. This progress is reflected in the significant performance gains recorded on both the LLaVA-W and WildVision benchmarks.
+- **Success of Self-Generated Feedback**: In LLaVA-OneVision's case, leveraging self-generated feedback data has proven to be a highly effective strategy for enhancing its visual chat capabilities. Specifically, [LLaVA-Critic](https://llava-vl.github.io/blog/2024-10-03-llava-critic/) is a utilized as a generalist evaluator to generate the scoring feedback for preference learning. This approach allows the model to refine its responses autonomously, leading to more natural and coherent conversations.
+
+----
+
+### Release
+
+- 🤗 Model Checkpoints: [[OV-7b-Chat]](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov-chat) | [[OV-72b-Chat]](https://huggingface.co/lmms-lab/llava-onevision-qwen2-72b-ov-chat)
+- 💬 Demo: [https://llava-onevision.lmms-lab.com](https://llava-onevision.lmms-lab.com/)
+
+----
+
+
+### Results
+
+The figure below illustrates the performance gain of LLaVA-OV-Chat across 5 benchmarks. The delta numbers shown on top of the bars indicate the improvement of the chat model variant(7b/72b) over its base model LLaVA-OV. 
+
+![](ov_chat_images/chat_results.png)
+
+| Model Name          | WildVision | LLaVA-W | LLaVA-Wilder | LiveBench | Video Detailed Description |
+|---------------------|------------|---------|--------------|-----------|----------------------------|
+| LLaVA-OV-7b         | 54.0       | 90.7    | 67.8         | 77.1      | 3.75                       |
+| LLaVA-OV-7b-Chat    | 67.3       | 100.3   | 71.6         | 84.5      | 3.87                       |
+| LLaVA-OV-72b        | 51.7       | 93.5    | 72.0         | 81.5      | 3.60                       |
+| LLaVA-OV-72b-Chat   | 70.0       | 104.4   | 75.9         | 88.5      | 3.86                       |
+| GPT-4V              | 79.8       | 98.0    | 81.0         | 73.7      | 4.00                       |
+
+LLaVA-OV-Chat consistently showcases exceptional visual chat capabilities across five open-ended multimodal tasks. Notably, compared to its base version, LLaVA-OV, which was trained using next-token prediction, LLaVA-OV-Chat—enhanced through alignment learning—receives significantly higher preference on four image-based tasks. The *[WildVision Bench](https://huggingface.co/datasets/WildVision/wildvision-bench)* simulates real-world multimodal interactions based on human preferences, mirroring scenarios from the online platform [WildVision-Arena (WV-Arena)](https://huggingface.co/spaces/WildVision/vision-arena). Both *[LLaVA-W](https://huggingface.co/datasets/lmms-lab/llava-bench-in-the-wild)* and *[LLaVA-Wilder](https://huggingface.co/datasets/lmms-lab/LLaVA-Bench-Wilder)* tackle daily-life visual tasks, with the former focusing on simpler cases and the latter on more challenging ones. The *[Multimodal Live-Bench](https://huggingface.co/spaces/lmms-lab/LiveBench)* leverages continuously updating news and online forums to test models' ability to generalize unobserved knowledge. Despite the alignment learning being conducted solely with images, LLaVA-OV-Chat also excels in *[Video Detailed Captioning](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/)*, demonstrating that its improved chat capabilities generalize effectively from image to video contexts.
+
+#### Examples
+
+|  Example 1 |
+| -- |
+| <div align="right" style="display: inline;"><img src="ov_chat_images/example1_tree.png" style="height:250px" align="right"></div><div align="left" style="display: inline"><div><b>[Question]</b>: How likely is it to snow after this picture was taken? What would change with this type of tree before it's likely to snow? </div></div> |
+| **[LLaVA-OneVision]**: It is highly unlikely that it would snow immediately after this picture was taken, as the lush green leaves suggest it's either late spring or summer. Before snowfall, which typically occurs in late autumn or winter, this type of tree, likely an oak, would shed its leaves, and the branches would appear bare. |
+| **[LLaVA-OneVision-Chat]**: Given the lush green leaves and the overall vibrancy of the tree in the image, it is highly unlikely that snow would follow immediately. This type of tree, which appears to be a deciduous species, typically sheds its leaves in autumn as a preparation for winter. Before it's likely to snow, the tree would undergo several changes:<br><br>1. **Color Change**: The leaves would change color from green to shades of yellow, orange, red, or brown as the chlorophyll breaks down, revealing other pigments. <br>2. **Leaf Fall**: As the days shorten and temperatures cool, the tree would gradually shed its leaves. This process is triggered by hormonal changes within the tree that cause the cells at the base of each leaf to weaken and eventually break, allowing the leaves to fall.<br>3. **Dormancy**: The tree would enter a state of dormancy, during which its metabolic processes slow down significantly. This helps the tree conserve energy and survive the cold winter months.<br>4. **Bark Changes**: The bark may become more rugged and textured as the tree ages and prepares for winter. This is a natural defense mechanism against cold temperatures and potential pests.<br>5. **Root Activity**: The roots would continue to absorb water and nutrients until the ground freezes, after which they would go dormant as well.<br>6. **Snow Preparation**: Once the tree has shed its leaves and entered dormancy, it would be better prepared to withstand snow and ice accumulation on its branches without the added weight of leaves.<br><br>In summary, the tree would need to go through the full autumn cycle of leaf color change and fall, followed by entering dormancy, before it would be likely to snow. This process can take several weeks to a few months, depending on the specific species of the tree and the local climate conditions.|
+
+|  Example 2 |
+| -- |
+| <div align="right" style="display: inline;"><img src="ov_chat_images/example2_dog.jpg" style="height:230px" align="right"></div><div align="left" style="display: inline"><div><b>[Question]</b>: Discuss how this creative twist on a classic work of art might be interpreted differently by various audiences. </div></div>|
+| **[LLaVA-OneVision]**: Different audiences might interpret this creative twist on the Mona Lisa in various ways. Art enthusiasts might appreciate the skillful execution and the playful reinterpretation of a classic, seeing it as a commentary on the universality of beauty and expression across species. Dog lovers could find joy in seeing their favorite breed portrayed with such dignity and elegance. Some might view it as a humorous take on high art, making it more accessible and relatable. Others might see it as a statement on the anthropomorphism of pets, reflecting how we often attribute human-like qualities to animals. Overall, the varied interpretations highlight the versatility of art in engaging diverse perspectives and emotions. |
+| **[LLaVA-OneVision-Chat]**: This creative twist on the Mona Lisa, featuring a dog's face, can be interpreted in various ways by different audiences. For art enthusiasts, it might be seen as a playful commentary on the nature of portraiture and the human tendency to anthropomorphize animals. It challenges traditional notions of beauty and subjectivity in art, inviting viewers to reconsider what makes a portrait compelling.<br><br>For dog lovers and pet owners, the image could evoke a sense of joy and familiarity, as they see their beloved pets reflected in one of the most iconic artworks in history. It might also highlight the deep bond between humans and their pets, suggesting that animals share in our cultural heritage.<br><br>In a broader social context, this image could be interpreted as a reflection of contemporary society's obsession with internet culture and memes. The substitution of the Mona Lisa's face with a dog's could be seen as a satirical take on how viral content often overshadows classical art in modern media.<br><br>Academically, this piece could spark discussions about copyright, artistic originality, and the public domain. It raises questions about the boundaries between homage and appropriation in art, encouraging critical thinking about intellectual property rights and creative freedom.<br><br>Overall, the diverse interpretations of this image underscore the power of art to engage, provoke, and connect people across different backgrounds and perspectives. |
+
+********
+
+### Iterative DPO for Enhanced Performance
+
+#### Feedback Data
+
+To optimize LLaVA-OneVision’s in-the-wild conversational abilities, we've employed an iterative Direct Preference Optimization (DPO) process. Through this method, we found that feedback from two primary sources is particularly effective:
+
+1. **Human Feedback from [LLaVA-RLHF](https://llava-rlhf.github.io/)**: Real-world human input plays a crucial role in guiding the model toward more intuitive and user-friendly responses.
+
+2. **AI Feedback from LLaVA-OV’s Self-Generated Responses**: Additionally, the AI's own self-generated feedback allows it to continuously improve and adapt, making this a valuable source for iterative learning. [LLaVA-Critic](https://llava-vl.github.io/blog/2024-10-03-llava-critic/) is a utilized as a generalist evaluator to generate the scoring feedback for preference learning
+
+By experimenting with either of these two forms of feedback, we've been able to significantly enhance LLaVA-OneVision's conversation capabilities, bringing it closer to achieving seamless visual chat interactions in dynamic, real-world environments.
+
+#### Alignment Learning with Iterative DPO 
+
+We provide a breakdown of the process for enhancing LLaVA-OneVision’s visual chat capabilities through iterative DPO.
+
+##### Requirements:
+
+1. **SFT Checkpoint**: We begin with a pretrained LLaVA-OneVision SFT (Supervised Fine-Tuning) model as the initial checkpoint for response generation.
+2. **Preference Data**: The dataset used in our experiments consists of (language-image prompt, response, preference) pairs sourced from human feedback or AI feedback, which serves as the training data for the model to align with user preference to improve chat experience.
+
+##### Step 1: Response Generation
+
+For each langauge-image prompt in the dataset, we randomly generate `k = 5` candidate responses from the starting checkpoint. To ensure diversity in the generated responses, we employ random decoding with the following parameters: `temperature = 0.7`, `top-p (nucleus sampling) = 0.9`. These settings encourage the generation of varied responses by balancing randomness and precision, giving us a broad spectrum of potential answers for further evaluation.
+
+##### Step 2: Scoring and Acquiring Feedback Data
+
+Once the candidate responses are generated, we utilize a feedback source (e.g., the reward signals from LLaVA-RLHF or reward signals from LLaVA-Critic) to score each of them. The reward model is responsible for evaluating the quality of the responses based on relevance, coherence, and appropriateness in relation to the given image-question pair. From the scored responses, we then select:
+
+- The **best** response (highest score)
+- The **worst** response (lowest score)
+
+These two responses serve as **pairwise feedback data** for the next phase of the training process.
+
+##### Step 3: Training with Iterative DPO
+
+Using the feedback data obtained in `Step 2`, we conduct DPO training in an iterative fashion. The process is as follows:
+
+1. In the $i^{th}$ round of training, we start with the pretrained model from the previous round $(i-1)^{th}$.
+2. We generate new candidate responses by repeating the response generation process outlined in `Step 1`.
+3. The reward source evaluates these new responses, and pairwise feedback data is acquired, as described in `Step 2`.
+4. Finally, we apply DPO training to the model using the feedback data. Each round of DPO training lasts for **`1 epoch`**.
+
+This iterative process is repeated for `N=3` rounds in total, with each round refining the model’s ability to generate high-quality visual chat responses by progressively incorporating feedback from both human and AI assessments.
+
+**Training script and data format**
+
+- Example training script: [`/scripts/train/dpo_ov7b.sh`](../scripts/train/dpo_ov7b.sh)
+- Format of training data:
+~~~json
+{
+  "id": "<image-id>",
+  "image": "<image path under args.image_folder>",
+  "prompt": "<input prompt/question>",
+  "chosen": "<chosen model response>",
+  "rejected": "<rejected model response>"
+}
+~~~
+
+
+------
+
+Check out on how we develop AI feedback for self-improvement LMMs, using [LLaVA-Critic](https://llava-vl.github.io/blog/2024-10-03-llava-critic/) as a generalist evaluator to generate the scoring feedback for preference learning!
+
+*Contributors to LLaVA-OneVision-Chat: [Tianyi Xiong](https://tyxiong23.github.io/), [Bo Li](https://brianboli.com/), [Dong Guo](https://www.linkedin.com/in/dongguoset/), [Huizhuo Yuan](https://scholar.google.com/citations?user=8foZzX4AAAAJ), [Quanquan Gu](https://web.cs.ucla.edu/~qgu/), [Chunyuan Li](https://scholar.google.com/citations?user=Zd7WmXUAAAAJ)*
+
+
+
+### Citation
+
+If you find it useful for your research and applications, please cite related papers/blogs using this BibTeX:
+```bibtex
+@misc{xiong2024llavaovchat,
+  title={LLaVA-OneVision-Chat: Improving Chat with Preference Learning},
+  url={https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/docs/LLaVA_OneVision_Chat.md},
+  author={Xiong, Tianyi and Li, Bo and Guo, Dong and Yuan, Huizhuo and Gu, Quanquan and Li, Chunyuan},
+  month={September},
+  year={2024}
+}
+
+@article{xiong2024llavacritic,
+  title={LLaVA-Critic: Learning to Evaluate Multimodal Models},
+  author={Xiong, Tianyi and Wang, Xiyao and Guo, Dong and Ye, Qinghao and Fan, Haoqi and Gu, Quanquan and Huang, Heng and Li, Chunyuan},
+  year={2024},
+  eprint={2410.02712},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2410.02712},
+}
+
+@article{li2024llavaov,
+  title={Llava-onevision: Easy visual task transfer},
+  author={Li, Bo and Zhang, Yuanhan and Guo, Dong and Zhang, Renrui and Li, Feng and Zhang, Hao and Zhang, Kaichen and Li, Yanwei and Liu, Ziwei and Li, Chunyuan},
+  journal={arXiv preprint arXiv:2408.03326},
+  year={2024}
+}
+
+@article{sun2023aligning,
+  title={Aligning large multimodal models with factually augmented rlhf},
+  author={Sun, Zhiqing and Shen, Sheng and Cao, Shengcao and Liu, Haotian and Li, Chunyuan and Shen, Yikang and Gan, Chuang and Gui, Liang-Yan and Wang, Yu-Xiong and Yang, Yiming and Keutzer, Kurt and Darrell, Trevor},
+  journal={arXiv preprint arXiv:2309.14525},
+  year={2023}
+}
+```
+
+

MedEvalKit/LLaVA-NeXT/docs/LLaVA_OneVision_Tutorials.ipynb

@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# (Frustratingly Easy) LLaVA OneVision Tutorial\n",
+    "\n",
+    "We know that it's always beneficial to have a unified interface for different tasks. So we are trying to unify the interface for image, text, image-text interleaved, and video input. And in this tutorial, we aim to provide the most straightforward way to use our model. \n",
+    "\n",
+    "We use our 0.5B version as an example. This could be running on a GPU with 4GB memory. And with the following examples, you could see it's surprisingly have promising performance on understanding the image, interleaved image-text, and video. Tiny but mighty!\n",
+    "\n",
+    "The same code could be used for 7B model as well.\n",
+    "\n",
+    "## Inference Guidance\n",
+    "\n",
+    "First please install our repo with code and environments: pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git\n",
+    "\n",
+    "Here is a quick inference code using [lmms-lab/qwen2-0.5b-si](https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-si) as an example. You will need to install `flash-attn` to use this code snippet. If you don't want to install it, you can set `attn_implementation=None` when load_pretrained_model\n",
+    "\n",
+    "### Image Input\n",
+    "Tackling the single image input with LLaVA OneVision is pretty straightforward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llava.model.builder import load_pretrained_model\n",
+    "from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token\n",
+    "from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX\n",
+    "from llava.conversation import conv_templates, SeparatorStyle\n",
+    "\n",
+    "from PIL import Image\n",
+    "import requests\n",
+    "import copy\n",
+    "import torch\n",
+    "\n",
+    "import sys\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "pretrained = \"lmms-lab/llava-onevision-qwen2-0.5b-si\"\n",
+    "model_name = \"llava_qwen\"\n",
+    "device = \"cuda\"\n",
+    "device_map = \"auto\"\n",
+    "llava_model_args = {\n",
+    "    \"multimodal\": True,\n",
+    "    \"attn_implementation\": \"sdpa\",\n",
+    "}\n",
+    "tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map, **llava_model_args)  # Add any other thing you want to pass in llava_model_args\n",
+    "\n",
+    "model.eval()\n",
+    "\n",
+    "url = \"https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true\"\n",
+    "image = Image.open(requests.get(url, stream=True).raw)\n",
+    "image_tensor = process_images([image], image_processor, model.config)\n",
+    "image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]\n",
+    "\n",
+    "conv_template = \"qwen_1_5\"  # Make sure you use correct chat template for different models\n",
+    "question = DEFAULT_IMAGE_TOKEN + \"\\nWhat is shown in this image?\"\n",
+    "conv = copy.deepcopy(conv_templates[conv_template])\n",
+    "conv.append_message(conv.roles[0], question)\n",
+    "conv.append_message(conv.roles[1], None)\n",
+    "prompt_question = conv.get_prompt()\n",
+    "\n",
+    "input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors=\"pt\").unsqueeze(0).to(device)\n",
+    "image_sizes = [image.size]\n",
+    "\n",
+    "\n",
+    "cont = model.generate(\n",
+    "    input_ids,\n",
+    "    images=image_tensor,\n",
+    "    image_sizes=image_sizes,\n",
+    "    do_sample=False,\n",
+    "    temperature=0,\n",
+    "    max_new_tokens=4096,\n",
+    ")\n",
+    "text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)\n",
+    "print(text_outputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You could use the following code to make it streaming in terminal, this would be pretty useful when creating a chatbot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from threading import Thread\n",
+    "from transformers import TextIteratorStreamer\n",
+    "import json\n",
+    "\n",
+    "url = \"https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true\"\n",
+    "image = Image.open(requests.get(url, stream=True).raw)\n",
+    "image_tensor = process_images([image], image_processor, model.config)\n",
+    "image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]\n",
+    "\n",
+    "conv_template = \"qwen_1_5\"\n",
+    "question = DEFAULT_IMAGE_TOKEN + \"\\nWhat is shown in this image?\"\n",
+    "conv = copy.deepcopy(conv_templates[conv_template])\n",
+    "conv.append_message(conv.roles[0], question)\n",
+    "conv.append_message(conv.roles[1], None)\n",
+    "prompt_question = conv.get_prompt()\n",
+    "\n",
+    "input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors=\"pt\").unsqueeze(0).to(device)\n",
+    "image_sizes = [image.size]\n",
+    "\n",
+    "max_context_length = getattr(model.config, \"max_position_embeddings\", 2048)\n",
+    "num_image_tokens = question.count(DEFAULT_IMAGE_TOKEN) * model.get_vision_tower().num_patches\n",
+    "\n",
+    "streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15)\n",
+    "\n",
+    "max_new_tokens = min(4096, max_context_length - input_ids.shape[-1] - num_image_tokens)\n",
+    "\n",
+    "if max_new_tokens < 1:\n",
+    "    print(\n",
+    "        json.dumps(\n",
+    "            {\n",
+    "                \"text\": question + \"Exceeds max token length. Please start a new conversation, thanks.\",\n",
+    "                \"error_code\": 0,\n",
+    "            }\n",
+    "        )\n",
+    "    )\n",
+    "else:\n",
+    "    gen_kwargs = {\n",
+    "        \"do_sample\": False,\n",
+    "        \"temperature\": 0,\n",
+    "        \"max_new_tokens\": max_new_tokens,\n",
+    "        \"images\": image_tensor,\n",
+    "        \"image_sizes\": image_sizes,\n",
+    "    }\n",
+    "\n",
+    "    thread = Thread(\n",
+    "        target=model.generate,\n",
+    "        kwargs=dict(\n",
+    "            inputs=input_ids,\n",
+    "            streamer=streamer,\n",
+    "            **gen_kwargs,\n",
+    "        ),\n",
+    "    )\n",
+    "    thread.start()\n",
+    "\n",
+    "    generated_text = \"\"\n",
+    "    for new_text in streamer:\n",
+    "        generated_text += new_text\n",
+    "        print(generated_text, flush=True)\n",
+    "        # print(json.dumps({\"text\": generated_text, \"error_code\": 0}), flush=True)\n",
+    "\n",
+    "    print(\"Final output:\", generated_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Image-Text Interleaved Input\n",
+    "\n",
+    "Now switching to our onevision model for more complex tasks. You should start to use `llava-onevision-qwen2-0.5b-ov` for image-text interleaved input and video input.\n",
+    "\n",
+    "Processing image-text interleaved input is a bit more complicated. But following the code below should work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model\n",
+    "pretrained = \"lmms-lab/llava-onevision-qwen2-0.5b-ov\"\n",
+    "model_name = \"llava_qwen\"\n",
+    "device = \"cuda\"\n",
+    "device_map = \"auto\"\n",
+    "llava_model_args = {\n",
+    "        \"multimodal\": True,\n",
+    "    }\n",
+    "overwrite_config = {}\n",
+    "overwrite_config[\"image_aspect_ratio\"] = \"pad\"\n",
+    "llava_model_args[\"overwrite_config\"] = overwrite_config\n",
+    "tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map, **llava_model_args)\n",
+    "\n",
+    "model.eval()\n",
+    "\n",
+    "# Load two images\n",
+    "url1 = \"https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true\"\n",
+    "url2 = \"https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png\"\n",
+    "\n",
+    "image1 = Image.open(requests.get(url1, stream=True).raw)\n",
+    "image2 = Image.open(requests.get(url2, stream=True).raw)\n",
+    "\n",
+    "images = [image1, image2]\n",
+    "image_tensors = process_images(images, image_processor, model.config)\n",
+    "image_tensors = [_image.to(dtype=torch.float16, device=device) for _image in image_tensors]\n",
+    "\n",
+    "# Prepare interleaved text-image input\n",
+    "conv_template = \"qwen_1_5\"\n",
+    "question = f\"{DEFAULT_IMAGE_TOKEN} This is the first image. Can you describe what you see?\\n\\nNow, let's look at another image: {DEFAULT_IMAGE_TOKEN}\\nWhat's the difference between these two images?\"\n",
+    "\n",
+    "conv = copy.deepcopy(conv_templates[conv_template])\n",
+    "conv.append_message(conv.roles[0], question)\n",
+    "conv.append_message(conv.roles[1], None)\n",
+    "prompt_question = conv.get_prompt()\n",
+    "\n",
+    "input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors=\"pt\").unsqueeze(0).to(device)\n",
+    "image_sizes = [image.size for image in images]\n",
+    "\n",
+    "# Generate response\n",
+    "cont = model.generate(\n",
+    "    input_ids,\n",
+    "    images=image_tensors,\n",
+    "    image_sizes=image_sizes,\n",
+    "    do_sample=False,\n",
+    "    temperature=0,\n",
+    "    max_new_tokens=4096,\n",
+    ")\n",
+    "text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)\n",
+    "print(text_outputs[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Video Input\n",
+    "\n",
+    "Now let's try video input. It's the same as image input, but you need to pass in a list of video frames. And remember to set the `<image>` token only once in the prompt, e.g. \"<image>\\nWhat is shown in this video?\", not \"<image>\\n<image>\\n<image>\\nWhat is shown in this video?\". Since we trained on this format, it's important to keep the format consistent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/tiger/miniconda3/envs/public_llava/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/home/tiger/miniconda3/envs/public_llava/lib/python3.10/site-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded LLaVA model: lmms-lab/llava-onevision-qwen2-7b-ov\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
+      "You are using a model of type llava to instantiate a model of type llava_qwen. This is not supported for all configurations of models and can yield errors.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading vision tower: google/siglip-so400m-patch14-384\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading checkpoint shards: 100%|██████████| 4/4 [00:08<00:00,  2.07s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Class: LlavaQwenForCausalLM\n",
+      "(16, 1024, 576, 3)\n",
+      "The video features a person standing on a stage, dressed in a black shirt and dark pants. A large hand appears from the background, reaching towards the person's pocket. The text 'Source: Joshua AG' is displayed at the top left corner of the frames, and 'EVAN CARMICHAEL' is shown in the top right corner. The text 'Anyone know what this pocket is for?' appears as the hand continues to reach into the pocket. The person then looks down at their pocket, and the text 'I've always wondered that' appears. The hand finally pulls out a small white device labeled 'iPod Nano'. The person holds up the iPod Nano, and the text 'is the new iPod Nano' appears. The video concludes with a close-up of the person holding the iPod Nano, showing it from different angles.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from operator import attrgetter\n",
+    "from llava.model.builder import load_pretrained_model\n",
+    "from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token\n",
+    "from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX\n",
+    "from llava.conversation import conv_templates, SeparatorStyle\n",
+    "\n",
+    "import torch\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import requests\n",
+    "import copy\n",
+    "import warnings\n",
+    "from decord import VideoReader, cpu\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "# Load the OneVision model\n",
+    "pretrained = \"lmms-lab/llava-onevision-qwen2-7b-ov\"\n",
+    "model_name = \"llava_qwen\"\n",
+    "device = \"cuda\"\n",
+    "device_map = \"auto\"\n",
+    "llava_model_args = {\n",
+    "    \"multimodal\": True,\n",
+    "}\n",
+    "tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map, attn_implementation=\"sdpa\", **llava_model_args)\n",
+    "\n",
+    "model.eval()\n",
+    "\n",
+    "\n",
+    "# Function to extract frames from video\n",
+    "def load_video(video_path, max_frames_num):\n",
+    "    if type(video_path) == str:\n",
+    "        vr = VideoReader(video_path, ctx=cpu(0))\n",
+    "    else:\n",
+    "        vr = VideoReader(video_path[0], ctx=cpu(0))\n",
+    "    total_frame_num = len(vr)\n",
+    "    uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)\n",
+    "    frame_idx = uniform_sampled_frames.tolist()\n",
+    "    spare_frames = vr.get_batch(frame_idx).asnumpy()\n",
+    "    return spare_frames  # (frames, height, width, channels)\n",
+    "\n",
+    "\n",
+    "# Load and process video\n",
+    "video_path = \"jobs.mp4\"\n",
+    "video_frames = load_video(video_path, 16)\n",
+    "print(video_frames.shape) # (16, 1024, 576, 3)\n",
+    "image_tensors = []\n",
+    "frames = image_processor.preprocess(video_frames, return_tensors=\"pt\")[\"pixel_values\"].half().cuda()\n",
+    "image_tensors.append(frames)\n",
+    "\n",
+    "# Prepare conversation input\n",
+    "conv_template = \"qwen_1_5\"\n",
+    "question = f\"{DEFAULT_IMAGE_TOKEN}\\nDescribe what's happening in this video.\"\n",
+    "\n",
+    "conv = copy.deepcopy(conv_templates[conv_template])\n",
+    "conv.append_message(conv.roles[0], question)\n",
+    "conv.append_message(conv.roles[1], None)\n",
+    "prompt_question = conv.get_prompt()\n",
+    "\n",
+    "input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors=\"pt\").unsqueeze(0).to(device)\n",
+    "image_sizes = [frame.size for frame in video_frames]\n",
+    "\n",
+    "# Generate response\n",
+    "cont = model.generate(\n",
+    "    input_ids,\n",
+    "    images=image_tensors,\n",
+    "    image_sizes=image_sizes,\n",
+    "    do_sample=False,\n",
+    "    temperature=0,\n",
+    "    max_new_tokens=4096,\n",
+    "    modalities=[\"video\"],\n",
+    ")\n",
+    "text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)\n",
+    "print(text_outputs[0])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.2 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

MedEvalKit/LLaVA-NeXT/docs/LLaVA_Video_1003.md

@@ -0,0 +1,134 @@
+# LLaVA Video
+
+##  Table of Contents
+
+1. [Model Summary](##model-summary)
+2. [Inference](##inference)
+3. [Training](##training)
+4. [Evaluation](##evaluation-guidance)
+6. [Citation](##citation)
+
+## Model Summary
+
+The LLaVA-Video models are 7/72B parameter models trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K) and [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), based on Qwen2 language model with a context window of 32K tokens.
+
+
+## Inference
+
+We provide the simple generation process for using our model. For more details, you could refer to [Github](https://github.com/LLaVA-VL/LLaVA-NeXT).
+
+```python
+# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+from PIL import Image
+import requests
+import copy
+import torch
+import sys
+import warnings
+from decord import VideoReader, cpu
+import numpy as np
+warnings.filterwarnings("ignore")
+def load_video(self, video_path, max_frames_num,fps=1,force_sample=False):
+    if max_frames_num == 0:
+        return np.zeros((1, 336, 336, 3))
+    vr = VideoReader(video_path, ctx=cpu(0),num_threads=1)
+    total_frame_num = len(vr)
+    video_time = total_frame_num / vr.get_avg_fps()
+    fps = round(vr.get_avg_fps()/fps)
+    frame_idx = [i for i in range(0, len(vr), fps)]
+    frame_time = [i/fps for i in frame_idx]
+    if len(frame_idx) > max_frames_num or force_sample:
+        sample_fps = max_frames_num
+        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
+        frame_idx = uniform_sampled_frames.tolist()
+        frame_time = [i/vr.get_avg_fps() for i in frame_idx]
+    frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
+    spare_frames = vr.get_batch(frame_idx).asnumpy()
+    # import pdb;pdb.set_trace()
+    return spare_frames,frame_time,video_time
+pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2"
+model_name = "llava_qwen"
+device = "cuda"
+device_map = "auto"
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map)  # Add any other thing you want to pass in llava_model_args
+model.eval()
+video_path = "XXXX"
+max_frames_num = "64"
+video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
+video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().bfloat16()
+video = [video]
+conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
+time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video."
+question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\nPlease describe this video in detail."
+conv = copy.deepcopy(conv_templates[conv_template])
+conv.append_message(conv.roles[0], question)
+conv.append_message(conv.roles[1], None)
+prompt_question = conv.get_prompt()
+input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+cont = model.generate(
+    input_ids,
+    images=video,
+    modalities= ["video"],
+    do_sample=False,
+    temperature=0,
+    max_new_tokens=4096,
+)
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip()
+print(text_outputs)
+```
+
+## Data Preparation
+
+1. **Download LLaVA-OneVision**  
+   Refer to the official instructions here: [LLaVA-OneVision Data](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). Make sure to follow the guidelines provided to obtain and organize the data correctly.
+
+2. **Download LLaVA-Video-178K**  
+   The dataset is available on Hugging Face: [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K). After downloading, place it in your desired directory.
+
+3. **Update `exp.yaml`**  
+   In the [`exp.yaml` file](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/scripts/video/train/exp.yaml), update the file paths to point to the directories where you stored the datasets:
+   - **Line 186-Line 263**: Specify the path for the LLaVA-Video-178K dataset.  
+   - For other data references, update them to point to your local LLaVA-OneVision data directory.
+
+## Training
+
+[[Scripts]](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/yhzhang/video_dev/scripts/video/train/SO400M_Qwen2_72B_ov_to_video_am9_aug6.sh): Start training models on your single-image/multi-image/video data.
+
+
+## Evaluation Guidance
+
+We use the [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) toolkit to evaluate our models. Ensure you have installed the LLaVA-NeXT model files as per the instructions in the main README.md.
+
+Install lmms-eval:
+
+> pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+
+### Reproducing Evaluation Results
+
+Our models' evaluation results can be fully reproduced using the lmms-eval toolkit. After installing lmms-eval and llava, you can run the evaluation using the following commands.
+
+Note: These commands require flash-attn. If you prefer not to install it, disable flash-attn by adding `attn_implementation=None` to the `--model_args` parameter.
+
+Important: Different torch versions may cause slight variations in results. By default in `lmms-eval`, the requirement for torch version is set to the latest version. In `llava` repo, the torch version is set to `2.1.2`. Torch version `2.1.2` would be stable for both `llava` and `lmms-eval`
+
+### Evaluating LLaVA-Video on multiple datasets
+
+We recommend the developers and researchers to thoroughly evaluate the models on more datasets to get a comprehensive understanding of their performance in different scenarios. So we provide a comprehensive list of datasets for evaluation, and welcome to incoporate more evaluation tasks. Please refer to the [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) for more details.
+
+```bash
+# video tasks
+accelerate launch --num_processes=8 \
+-m lmms_eval \
+--model llava_vid \
+--model_args pretrained=lmms-lab/LLaVA-Video-7B-Qwen2,conv_template=qwen_1_5,max_frames_num=64,mm_spatial_pool_mode=average \
+--tasks activitynetqa,videochatgpt,nextqa_mc_test,egoschema,video_dc499,videmme,videomme_w_subtitle,perceptiontest_val_mc \
+--batch_size 1 \
+--log_samples \
+--log_samples_suffix llava_vid \
+--output_path ./logs/
+```
+

MedEvalKit/LLaVA-NeXT/docs/README.md

@@ -0,0 +1,18 @@
+# LLaVA-NeXT Documentation
+
+Welcome to the LLaVA-NeXT documentation. This guide provides an overview of the different components and features of LLaVA-NeXT. Please refer to the following documents for detailed information on specific topics:
+
+1. [LLaVA OneVision](LLaVA_OneVision.md): Learn about the most advanced and unified version: LLaVA OneVision.
+    - [LLaVA OneVision: Inference Tutorials](LLaVA_OneVision_Tutorials.ipynb): Learn how to use LLaVA OneVision for inference.
+    - [LLaVA OneVision: Data Visualization](../llava_ov_plot.ipynb): Visualization scripts for analyzing training data distribution and model performance.
+    - [LLaVA Onevision Chat](LLaVA_OneVision_Chat.md): Improving Chat with Preference Learning
+
+2. [LLaVA-NeXT Interleave](LLaVA-NeXT-Interleave.md): Explore the interleaved training approach used in LLaVA-NeXT.
+
+3. [LLaVA-NeXT Video (0716)](LLaVA-NeXT-Video_0716.md): Discover the video processing capabilities of LLaVA-NeXT (version 0716).
+
+4. [LLaVA-NeXT Video](LLaVA-NeXT-Video.md): Get information about the latest video processing features in LLaVA-NeXT.
+
+5. [LLaVA-NeXT Overview](LLaVA-NeXT.md): Read a comprehensive overview of the LLaVA-NeXT project, including its architecture, features, and capabilities.
+
+These documents provide in-depth information on various aspects of LLaVA-NeXT. Please refer to them for detailed explanations, implementation details, and usage instructions.

MedEvalKit/LLaVA-NeXT/docs/onevision_trial.py

@@ -0,0 +1,111 @@
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+
+from PIL import Image
+import requests
+import copy
+import torch
+
+import sys
+import warnings
+
+warnings.filterwarnings("ignore")
+
+pretrained = "lmms-lab/llava-onevision-qwen2-0.5b-si"
+model_name = "llava_qwen"
+device = "cuda"
+device_map = "auto"
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args
+
+model.eval()
+
+url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+image_tensor = process_images([image], image_processor, model.config)
+image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
+
+conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
+question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
+conv = copy.deepcopy(conv_templates[conv_template])
+conv.append_message(conv.roles[0], question)
+conv.append_message(conv.roles[1], None)
+prompt_question = conv.get_prompt()
+
+input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+image_sizes = [image.size]
+
+
+cont = model.generate(
+    input_ids,
+    images=image_tensor,
+    image_sizes=image_sizes,
+    do_sample=False,
+    temperature=0,
+    max_new_tokens=4096,
+)
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
+print(text_outputs)
+
+from threading import Thread
+from transformers import TextIteratorStreamer
+import json
+
+url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+image_tensor = process_images([image], image_processor, model.config)
+image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
+
+conv_template = "qwen_1_5"
+question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
+conv = copy.deepcopy(conv_templates[conv_template])
+conv.append_message(conv.roles[0], question)
+conv.append_message(conv.roles[1], None)
+prompt_question = conv.get_prompt()
+
+input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+image_sizes = [image.size]
+
+max_context_length = getattr(model.config, "max_position_embeddings", 2048)
+num_image_tokens = question.count(DEFAULT_IMAGE_TOKEN) * model.get_vision_tower().num_patches
+
+streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15)
+
+max_new_tokens = min(4096, max_context_length - input_ids.shape[-1] - num_image_tokens)
+
+if max_new_tokens < 1:
+    print(
+        json.dumps(
+            {
+                "text": question + "Exceeds max token length. Please start a new conversation, thanks.",
+                "error_code": 0,
+            }
+        )
+    )
+else:
+    gen_kwargs = {
+        "do_sample": False,
+        "temperature": 0,
+        "max_new_tokens": max_new_tokens,
+        "images": image_tensor,
+        "image_sizes": image_sizes,
+    }
+
+    thread = Thread(
+        target=model.generate,
+        kwargs=dict(
+            inputs=input_ids,
+            streamer=streamer,
+            **gen_kwargs,
+        ),
+    )
+    thread.start()
+
+    generated_text = ""
+    for new_text in streamer:
+        generated_text += new_text
+        sys.stdout.write(new_text)
+        sys.stdout.flush()
+
+    print("\nFinal output:", generated_text)

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/Dockerfile

@@ -0,0 +1,76 @@
+# Start from the NVIDIA official image (ubuntu-22.04 + cuda-12.6 + python-3.10)
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html
+FROM nvcr.io/nvidia/pytorch:24.08-py3
+
+# Define environments
+ENV MAX_JOBS=32
+ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
+ENV DEBIAN_FRONTEND=noninteractive
+ENV NODE_OPTIONS=""
+ENV PIP_ROOT_USER_ACTION=ignore
+ENV HF_HUB_ENABLE_HF_TRANSFER="1"
+
+# Define installation arguments
+ARG APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/
+ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
+
+# Set apt source
+RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak && \
+    { \
+    echo "deb ${APT_SOURCE} jammy main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse"; \
+    } > /etc/apt/sources.list
+
+# Install systemctl
+RUN apt-get update && \
+    apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \
+    apt-get clean
+
+# Install tini
+RUN apt-get update && \
+    apt-get install -y tini && \
+    apt-get clean
+
+# Change pip source
+RUN pip config set global.index-url "${PIP_INDEX}" && \
+    pip config set global.extra-index-url "${PIP_INDEX}" && \
+    python -m pip install --upgrade pip
+
+# Uninstall nv-pytorch fork
+RUN pip uninstall -y torch torchvision torchaudio \
+    pytorch-quantization pytorch-triton torch-tensorrt \
+    transformer_engine flash_attn apex megatron-core \
+    xgboost opencv grpcio
+
+# Fix cv2
+RUN rm -rf /usr/local/lib/python3.10/dist-packages/cv2
+
+# Install torch-2.6.0+cu124 + vllm-0.8.4
+# torch-2.6.0+cu124: cxx11abi=False
+# torch-2.6.0+cu126: cxx11abi=True
+# see https://github.com/flashinfer-ai/flashinfer/issues/911
+RUN pip install --no-cache-dir "vllm==0.8.4" "torch==2.6.0" "torchvision==0.21.0" "torchaudio==2.6.0" tensordict torchdata \
+    "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \
+    "numpy<2.0.0" "pyarrow>=15.0.0" pandas \
+    ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb liger-kernel mathruler \
+    pytest yapf py-spy pyext pre-commit ruff
+
+# Install flash-attn-2.7.4.post1 (cxx11abi=False)
+RUN wget -nv https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl && \
+    pip install --no-cache-dir flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+
+# Install flashinfer-0.2.2.post1+cu124 (cxx11abi=False)
+# vllm-0.8.3 does not support flashinfer>=0.2.3
+# see https://github.com/vllm-project/vllm/pull/15777
+RUN wget -nv https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.2.post1/flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl && \
+    pip install --no-cache-dir flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl
+
+# Fix packages
+RUN pip uninstall -y pynvml nvidia-ml-py && \
+    pip install --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1"
+
+# Reset pip config
+RUN pip config unset global.index-url && \
+    pip config unset global.extra-index-url

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/Dockerfile.legacy

@@ -0,0 +1,72 @@
+# Start from the NVIDIA official image (ubuntu-22.04 + cuda-12.6 + python-3.10)
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html
+FROM nvcr.io/nvidia/pytorch:24.08-py3
+
+# Define environments
+ENV MAX_JOBS=32
+ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
+ENV DEBIAN_FRONTEND=noninteractive
+ENV NODE_OPTIONS=""
+ENV PIP_ROOT_USER_ACTION=ignore
+ENV HF_HUB_ENABLE_HF_TRANSFER="1"
+
+# Define installation arguments
+ARG APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/
+ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
+ARG VLLM_COMMIT=227578480d71fc94ef46ca77fb69496412158d68
+
+# Set apt source
+RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak && \
+    { \
+    echo "deb ${APT_SOURCE} jammy main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse"; \
+    } > /etc/apt/sources.list
+
+# Install systemctl
+RUN apt-get update && \
+    apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \
+    apt-get clean
+
+# Install tini
+RUN apt-get update && \
+    apt-get install -y tini && \
+    apt-get clean
+
+# Change pip source
+RUN pip config set global.index-url "${PIP_INDEX}" && \
+    pip config set global.extra-index-url "${PIP_INDEX}" && \
+    python -m pip install --upgrade pip
+
+# Uninstall nv-pytorch fork
+RUN pip uninstall -y torch torchvision torchaudio \
+    pytorch-quantization pytorch-triton torch-tensorrt \
+    transformer_engine flash_attn apex megatron-core \
+    xgboost opencv grpcio
+
+# Fix cv2
+RUN rm -rf /usr/local/lib/python3.10/dist-packages/cv2
+
+# Install vllm-0.7.4-nightly
+RUN pip install --no-cache-dir vllm --pre --extra-index-url "https://wheels.vllm.ai/${VLLM_COMMIT}" && \
+    git clone -b verl_v1 https://github.com/hiyouga/vllm.git && \
+    cp -r vllm/vllm/ /usr/local/lib/python3.10/dist-packages/
+
+# Install torch-2.5.1
+RUN pip install --no-cache-dir "torch==2.5.1" "torchvision==0.20.1" "torchaudio==2.5.1" tensordict torchdata \
+    "transformers>=4.49.0" accelerate datasets peft hf-transfer \
+    ray[default] codetiming hydra-core pandas "pyarrow>=15.0.0" pylatexenc qwen-vl-utils wandb liger-kernel mathruler \
+    pytest yapf py-spy pyext pre-commit ruff
+
+# Install flash_attn-2.7.4.post1
+RUN wget -nv https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl && \
+    pip install --no-cache-dir flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+
+# Fix packages
+RUN pip uninstall -y pynvml nvidia-ml-py && \
+    pip install --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1"
+
+# Reset pip config
+RUN pip config unset global.index-url && \
+    pip config unset global.extra-index-url

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
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MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/Makefile

@@ -0,0 +1,18 @@
+.PHONY: build commit quality style
+
+check_dirs := examples scripts verl setup.py
+
+build:
+	python3 setup.py sdist bdist_wheel
+
+commit:
+	pre-commit install
+	pre-commit run --all-files
+
+quality:
+	ruff check $(check_dirs)
+	ruff format --check $(check_dirs)
+
+style:
+	ruff check $(check_dirs) --fix
+	ruff format $(check_dirs)

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/README.md

@@ -0,0 +1,33 @@
+# EasyR1 for LLaVA-Critic-R1
+
+This codebase is a customized extension of [EasyR1](https://github.com/hiyouga/EasyR1), specifically modified to support RL training using LLaMA-3.2-Vision-11B-Instruct as the base model. For RL training using Qwen-2.5-VL and Mimo-VL as base models, please use the original EasyR1 codebase. We will integrate support for all models into a unified codebase in the future.
+
+## Training based on Qwen-2.5-VL
+
+```
+bash ./examples/qwen2_5_vl_7b_llava_critic_grpo.sh
+```
+
+## Training based on ThinkLite-VL
+
+```
+bash ./examples/thinklite_vl_7b_llava_critic_grpo.sh
+```
+
+## Training based on Mimo-VL
+
+### SFT checkpoint
+```
+bash ./examples/mimo_vl_7b_llava_critic_grpo.sh
+```
+
+### RL checkpoint
+```
+bash ./examples/mimo_vl_rl_7b_llava_critic_grpo.sh
+```
+
+## Training based on LLaMA-3.2-Vision
+
+```
+bash ./examples/llama3.2v_critic_grpo.sh
+```

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/assets/baselines.md

@@ -0,0 +1,53 @@
+# Baselines
+
+Environment: [hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0](https://hub.docker.com/layers/hiyouga/verl/ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0/images/sha256-335ed6cd1fe73090e458409cfa4394d6abf4cd0503ca44dbafdc28ff72e5ed20)
+
+EasyR1 version: [v0.3.0](https://github.com/hiyouga/EasyR1/tree/v0.3.0)
+
+Welcome to contribute new data points!
+
+## Algorithm Baselines
+
+### [Qwen2.5-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on [Math12k](https://huggingface.co/datasets/hiyouga/math12k)
+
+| Size | Algorithm   | Bits | LR   | KL   | Test Score |
+| ---- | ----------- | ---- | ---- | ---- | ---------- |
+| 7B   | GRPO        | AMP  | 1e-6 | 1e-2 | 0.73->0.79 |
+
+### [Qwen2.5-VL-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on [Geometry3k](https://huggingface.co/datasets/hiyouga/geometry3k)
+
+| Size | Algorithm   | Bits | LR   | KL   | Test Score |
+| ---- | ----------- | ---- | ---- | ---- | ---------- |
+| 7B   | GRPO        | AMP  | 1e-6 | 1e-2 | 0.39->0.52 |
+| 7B   | GRPO        | BF16 | 1e-6 | 1e-2 | 0.39->0.52 |
+| 7B   | GRPO        | AMP  | 1e-6 | 1e-3 | 0.39->0.52 |
+| 7B   | RLOO        | AMP  | 1e-6 | 1e-2 | 0.39->0.53 |
+| 3B   | GRPO        | AMP  | 1e-6 | 1e-2 | 0.27->0.44 |
+| 32B  | GRPO        | BF16 | 1e-6 | 1e-2 | 0.46->0.61 |
+
+> [!NOTE]
+> The hyper-parameters not listed are all the same as the default values.
+
+## Performance Baselines
+
+### [Qwen2.5-VL-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on [Geometry3k](https://huggingface.co/datasets/hiyouga/geometry3k)
+
+| Size | GPU Type      | Bits | Batch Size | vLLM Util | vLLM TP | Peak Mem | Peak VRAM | Throughput | Sec per step | Actor MFU |
+| ---- | ------------- | ---- | ---------- | --------- | ------- | -------- | --------- | ---------- | ------------ | --------- |
+| 3B   | 8 * H100 80GB | AMP  | 4 / 16     | 0.6       | 2       | 120GB    | 35GB      | 1200       | 180s         | 6.3%      |
+| 7B   | 8 * H100 80GB | AMP  | 4 / 16     | 0.6       | 2       | 140GB    | 60GB      | 1200       | 180s         | 13.6%     |
+| 7B   | 8 * H100 80GB | AMP  | 10 / 20    | 0.6       | 2       | 150GB    | 75GB      | 1400       | 170s         | 19.2%     |
+| 7B   | 8 * L20 48GB  | AMP  | 4 / 16     | 0.6       | 2       | 150GB    | 44GB      | 410        | 580s         | 26.5%     |
+| 7B   | 8 * H100 80GB | BF16 | 4 / 16     | 0.6       | 2       | 150GB    | 50GB      | 1280       | 190s         | 13.9%     |
+| 32B  | 8 * H100 80GB | BF16 | 1 / 8      | 0.6       | 8       | 240GB    | 68GB      | 360        | 860s         | 11.2%     |
+
+- Batch Size: micro_batch_size_per_device_for_update / micro_batch_size_per_device_for_experience
+- vLLM Util: rollout.gpu_memory_utilization
+- vLLM TP: rollout.tensor_parallel_size
+- Peak Mem: Peak CPU memory usage
+- Peak VRAM: Peak GPU memory usage
+- Throughput: Number of tokens per second per GPU by one training step
+- Sec per step: Average time per step in seconds
+
+> [!NOTE]
+> The hyper-parameters not listed are all the same as the default values.

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/baselines/qwen2_5_vl_3b_clevr.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+set -x
+
+export PYTHONUNBUFFERED=1
+
+MODEL_PATH=Qwen/Qwen2.5-VL-3B-Instruct  # replace it with your local file path
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=BUAADreamer/clevr_count_70k@train \
+    data.val_files=BUAADreamer/clevr_count_70k@test \
+    data.format_prompt=./examples/format_prompt/r1v_format.jinja \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    worker.rollout.tensor_parallel_size=1 \
+    worker.reward.reward_type=sequential \
+    worker.reward.reward_function=./examples/reward_function/r1v.py:compute_score \
+    trainer.experiment_name=qwen2_5_vl_3b_clevr \
+    trainer.n_gpus_per_node=2

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/baselines/qwen2_5_vl_3b_geoqa8k.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+set -x
+
+export PYTHONUNBUFFERED=1
+
+MODEL_PATH=Qwen/Qwen2.5-VL-3B-Instruct  # replace it with your local file path
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=leonardPKU/GEOQA_8K_R1V@train \
+    data.val_files=leonardPKU/GEOQA_8K_R1V@test \
+    data.format_prompt=./examples/format_prompt/r1v_format.jinja \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    worker.rollout.tensor_parallel_size=1 \
+    worker.reward.reward_type=sequential \
+    worker.reward.reward_function=./examples/reward_function/r1v.py:compute_score \
+    trainer.experiment_name=qwen2_5_vl_3b_geoqa8k \
+    trainer.n_gpus_per_node=8

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/config.yaml

@@ -0,0 +1,96 @@
+data:
+  train_files: hiyouga/math12k@train
+  val_files: hiyouga/math12k@test
+  prompt_key: problem
+  answer_key: answer
+  image_key: image
+  max_prompt_length: 4096
+  max_response_length: 2048
+  rollout_batch_size: 256
+  val_batch_size: 1024
+  format_prompt: ./examples/format_prompt/math_format.jinja
+  override_chat_template: null
+  shuffle: true
+  seed: 1
+  max_pixels: 4194304
+  min_pixels: 262144
+  filter_overlong_prompts: true
+
+algorithm:
+  adv_estimator: grpo
+  disable_kl: false
+  use_kl_loss: true
+  kl_penalty: low_var_kl
+  kl_coef: 1.0e-2
+
+worker:
+  actor:
+    global_batch_size: 128
+    micro_batch_size_per_device_for_update: 4
+    micro_batch_size_per_device_for_experience: 8
+    max_grad_norm: 1.0
+    padding_free: true
+    ulysses_sequence_parallel_size: 1
+    is_entropy_reg: False
+    entropy_coef: 0.001
+    model:
+      model_path: Qwen/Qwen2.5-7B-Instruct
+      enable_gradient_checkpointing: true
+      trust_remote_code: true
+      freeze_vision_tower: false
+    optim:
+      lr: 1.0e-6
+      weight_decay: 1.0e-2
+      strategy: adamw  # {adamw, adamw_bf16}
+      lr_warmup_ratio: 0.0
+    fsdp:
+      enable_full_shard: true
+      enable_cpu_offload: false
+      enable_rank0_init: true
+    offload:
+      offload_params: true  # true: more CPU memory; false: more GPU memory
+      offload_optimizer: true  # true: more CPU memory; false: more GPU memory
+
+  rollout:
+    n: 8
+    temperature: 1.0
+    top_p: 0.99
+    gpu_memory_utilization: 0.6
+    max_num_batched_tokens: 8192
+    max_model_len: 8192
+    enforce_eager: false
+    enable_chunked_prefill: false
+    tensor_parallel_size: 2
+    limit_images: 10
+    val_override_config:
+      temperature: 0.5
+      n: 1
+
+  ref:
+    fsdp:
+      enable_full_shard: true
+      enable_cpu_offload: true  # true: more CPU memory; false: more GPU memory
+      enable_rank0_init: true
+    offload:
+      offload_params: false
+
+  reward:
+    reward_type: batch
+    reward_function: ./examples/reward_function/math.py:compute_score
+
+trainer:
+  total_epochs: 15
+  max_steps: null
+  project_name: easy_r1
+  experiment_name: qwen2_5_7b_math_grpo
+  logger: ["console", "wandb"]
+  nnodes: 1
+  n_gpus_per_node: 8
+  val_freq: -1  # -1 to disable
+  val_before_train: false
+  val_only: false
+  val_generations_to_log: 3
+  save_freq: 10  # -1 to disable
+  save_limit: -1  # -1 to disable
+  save_checkpoint_path: null
+  load_checkpoint_path: null

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/format_prompt/caption_format.jinja

@@ -0,0 +1 @@
+{{ content | trim }} You FIRST describe the given image in a detailed manner, THEN think about the reasoning process as an internal monologue based on the generated description and given question and then provide the final answer. The description MUST BE enclosed within <caption> </caption> tags. The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \boxed{}.

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/format_prompt/math_format.jinja

@@ -0,0 +1 @@
+{{ content | trim }} You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \boxed{}.

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/format_prompt/r1v_format.jinja

@@ -0,0 +1 @@
+{{ content | trim }} A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/llama3.2v_critic_grpo.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+
+set -x
+
+export PYTHONUNBUFFERED=1
+
+MODEL_PATH=meta-llama/Llama-3.2-11B-Vision-Instruct # replace it with your local file path
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.val_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.shuffle=True \
+    data.format_prompt=./examples/format_prompt/math_format.jinja \
+    data.rollout_batch_size=256 \
+    data.max_prompt_length=4096 \
+    data.prompt_key=question \
+    worker.actor.is_entropy_reg=False \
+    worker.actor.global_batch_size=128 \
+    worker.actor.optim.lr=5.0e-7 \
+    worker.actor.micro_batch_size_per_device_for_update=4 \
+    worker.actor.micro_batch_size_per_device_for_experience=8 \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    worker.rollout.tensor_parallel_size=8 \
+    worker.rollout.limit_images=1 \
+    worker.rollout.gpu_memory_utilization=0.6 \
+    worker.reward.reward_function=./examples/reward_function/math.py:compute_score \
+    trainer.experiment_name=llama3.2v_critic_grpo \
+    trainer.n_gpus_per_node=8 

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/mimo_vl_7b_llava_critic_grpo.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+MODEL_PATH=XiaomiMiMo/MiMo-VL-7B-SFT-2508
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.val_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.filter_overlong_prompts=True \
+    data.prompt_key=question \
+    data.max_prompt_length=6142 \
+    worker.actor.is_entropy_reg=False \
+    worker.actor.global_batch_size=128 \
+    worker.actor.micro_batch_size_per_device_for_update=4 \
+    worker.actor.micro_batch_size_per_device_for_experience=8 \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    trainer.experiment_name=mimo_vl_7b_llava_critic_grpo \
+    trainer.n_gpus_per_node=8 
+

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/mimo_vl_rl_7b_llava_critic_grpo.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+MODEL_PATH=XiaomiMiMo/MiMo-VL-7B-RL-2508
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.val_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.filter_overlong_prompts=True \
+    data.prompt_key=question \
+    data.max_prompt_length=6142 \
+    worker.actor.is_entropy_reg=False \
+    worker.actor.global_batch_size=128 \
+    worker.actor.micro_batch_size_per_device_for_update=4 \
+    worker.actor.micro_batch_size_per_device_for_experience=8 \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    trainer.experiment_name=mimo_vl_rl_7b_llava_critic_grpo \
+    trainer.n_gpus_per_node=8 
+

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/qwen2_5_vl_7b_llava_critic_grpo.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+MODEL_PATH=Qwen/Qwen2.5-VL-7B-Instruct
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.val_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.filter_overlong_prompts=True \
+    data.prompt_key=question \
+    data.max_prompt_length=6142 \
+    worker.actor.is_entropy_reg=False \
+    worker.actor.global_batch_size=128 \
+    worker.actor.micro_batch_size_per_device_for_update=4 \
+    worker.actor.micro_batch_size_per_device_for_experience=8 \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    trainer.experiment_name=qwen2_5_vl_7b_llava_critic_grpo \
+    trainer.n_gpus_per_node=8 
+

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/reward_function/caption.py

@@ -0,0 +1,46 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+from typing import Dict, List
+
+from mathruler.grader import extract_boxed_content, grade_answer
+
+
+def format_reward(predict: str) -> float:
+    pattern = re.compile(r"<caption>.*</caption>.*<think>.*</think>.*\\boxed\{.*\}.*", re.DOTALL)
+    format_match = re.fullmatch(pattern, predict)
+    return 1.0 if format_match else 0.0
+
+
+def accuracy_reward(predict: str, ground_truth: str) -> float:
+    answer = extract_boxed_content(predict)
+    return 1.0 if grade_answer(answer, ground_truth) else 0.0
+
+
+def compute_score(predicts: List[str], ground_truths: List[str], format_weight: float = 0.1) -> List[Dict[str, float]]:
+    scores = []
+    for predict, ground_truth in zip(predicts, ground_truths):
+        predict = re.sub(r"\s*(<|>|/)\s*", r"\1", predict)  # handle qwen2.5vl-32b format
+        format_score = format_reward(predict)
+        accuracy_score = accuracy_reward(predict, ground_truth)
+        scores.append(
+            {
+                "overall": (1 - format_weight) * accuracy_score + format_weight * format_score,
+                "format": format_score,
+                "accuracy": accuracy_score,
+            }
+        )
+
+    return scores

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/reward_function/math.py

@@ -0,0 +1,46 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+from typing import Dict, List
+
+from mathruler.grader import extract_boxed_content, grade_answer
+
+
+def format_reward(predict: str) -> float:
+    pattern = re.compile(r"<think>.*</think>.*\\boxed\{.*\}.*", re.DOTALL)
+    format_match = re.fullmatch(pattern, predict)
+    return 1.0 if format_match else 0.0
+
+
+def accuracy_reward(predict: str, ground_truth: str) -> float:
+    answer = extract_boxed_content(predict)
+    return 1.0 if grade_answer(answer, ground_truth) else 0.0
+
+
+def compute_score(predicts: List[str], ground_truths: List[str], format_weight: float = 0.1) -> List[Dict[str, float]]:
+    scores = []
+    for predict, ground_truth in zip(predicts, ground_truths):
+        predict = re.sub(r"\s*(<|>|/)\s*", r"\1", predict)  # handle qwen2.5vl-32b format
+        format_score = format_reward(predict)
+        accuracy_score = accuracy_reward(predict, ground_truth)
+        scores.append(
+            {
+                "overall": (1 - format_weight) * accuracy_score + format_weight * format_score,
+                "format": format_score,
+                "accuracy": accuracy_score,
+            }
+        )
+
+    return scores

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/reward_function/r1v.py

@@ -0,0 +1,47 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+from typing import Dict
+
+from mathruler.grader import grade_answer
+
+
+def format_reward(predict: str) -> float:
+    pattern = re.compile(r"<think>.*?</think>\s*<answer>.*?</answer>", re.DOTALL)
+    format_match = re.fullmatch(pattern, predict)
+    return 1.0 if format_match else 0.0
+
+
+def accuracy_reward(predict: str, ground_truth: str) -> float:
+    try:
+        content_match = re.search(r"<answer>(.*?)</answer>", predict)
+        given_answer = content_match.group(1).strip() if content_match else predict.strip()
+        if grade_answer(given_answer, ground_truth.strip()):
+            return 1.0
+
+    except Exception:
+        pass
+
+    return 0.0
+
+
+def compute_score(predict: str, ground_truth: str, format_weight: float = 0.5) -> Dict[str, float]:
+    format_score = format_reward(predict)
+    accuracy_score = accuracy_reward(predict, ground_truth)
+    return {
+        "overall": (1 - format_weight) * accuracy_score + format_weight * format_score,
+        "format": format_score,
+        "accuracy": accuracy_score,
+    }

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/runtime_env.yaml

@@ -0,0 +1,9 @@
+working_dir: ./
+excludes: ["/.git/"]
+env_vars:
+  TOKENIZERS_PARALLELISM: "true"
+  NCCL_DEBUG: "WARN"
+  VLLM_LOGGING_LEVEL: "WARN"
+  TORCH_NCCL_AVOID_RECORD_STREAMS: "1"
+  PYTORCH_CUDA_ALLOC_CONF: "expandable_segments:False"
+  PYTHONUNBUFFERED: "1"

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/thinklite_vl_7b_llava_critic_grpo.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+MODEL_PATH=russwang/ThinkLite-VL-7B
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.val_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.filter_overlong_prompts=True \
+    data.prompt_key=question \
+    data.max_prompt_length=6142 \
+    worker.actor.is_entropy_reg=False \
+    worker.actor.global_batch_size=128 \
+    worker.actor.optim.lr=5.0e-7 \
+    worker.actor.micro_batch_size_per_device_for_update=4 \
+    worker.actor.micro_batch_size_per_device_for_experience=8 \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    trainer.experiment_name=thinklite_vl_7b_llava_critic_grpo \
+    trainer.n_gpus_per_node=8 

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/init

@@ -0,0 +1 @@
+

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/pyproject.toml

@@ -0,0 +1,39 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "verl"
+dynamic = [
+    "version",
+    "dependencies",
+    "optional-dependencies",
+    "requires-python",
+    "authors",
+    "description",
+    "readme",
+    "license"
+]
+
+[tool.ruff]
+target-version = "py39"
+line-length = 119
+indent-width = 4
+
+[tool.ruff.lint]
+ignore = ["C901", "E501", "E741", "W605", "C408"]
+select = ["C", "E", "F", "I", "W", "RUF022"]
+
+[tool.ruff.lint.per-file-ignores]
+"__init__.py" = ["E402", "F401", "F403", "F811"]
+
+[tool.ruff.lint.isort]
+lines-after-imports = 2
+known-first-party = ["verl"]
+known-third-party = ["torch", "transformers", "wandb"]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/requirements.txt

@@ -0,0 +1,19 @@
+accelerate
+codetiming
+datasets
+liger-kernel
+mathruler
+numpy
+omegaconf
+pandas
+peft
+pillow
+pyarrow>=15.0.0
+pylatexenc
+qwen-vl-utils
+ray[default]
+tensordict
+torchdata
+transformers>=4.51.0
+vllm>=0.7.3
+wandb

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/scripts/model_merger.py

@@ -0,0 +1,187 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+import re
+from concurrent.futures import ThreadPoolExecutor
+from typing import Dict, List, Tuple
+
+import numpy as np
+import torch
+from torch.distributed._tensor import DTensor, Placement, Shard
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoModelForTokenClassification,
+    AutoModelForVision2Seq,
+    PretrainedConfig,
+    PreTrainedModel,
+)
+
+
+def merge_by_placement(tensors: List[torch.Tensor], placement: Placement):
+    if placement.is_replicate():
+        return tensors[0]
+    elif placement.is_partial():
+        raise NotImplementedError("Partial placement is not supported yet")
+    elif placement.is_shard():
+        return torch.cat(tensors, dim=placement.dim).contiguous()
+    else:
+        raise ValueError(f"Unsupported placement: {placement}")
+
+
+def upload_model_to_huggingface(local_path: str, remote_path: str):
+    # Push to hugging face
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    api.create_repo(repo_id=remote_path, private=False, exist_ok=True)
+    api.upload_folder(repo_id=remote_path, folder_path=local_path, repo_type="model")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--local_dir", required=True, type=str, help="The path for your saved model")
+    parser.add_argument("--hf_upload_path", default=False, type=str, help="The path of the huggingface repo to upload")
+    args = parser.parse_args()
+    local_dir: str = args.local_dir
+
+    assert not local_dir.endswith("huggingface"), "The local_dir should not end with huggingface."
+
+    # copy rank zero to find the shape of (dp, fsdp)
+    rank = 0
+    world_size = 0
+    for filename in os.listdir(local_dir):
+        match = re.match(r"model_world_size_(\d+)_rank_0\.pt", filename)
+        if match:
+            world_size = match.group(1)
+            break
+
+    assert world_size, "No model file with the proper format."
+
+    rank0_weight_path = os.path.join(local_dir, f"model_world_size_{world_size}_rank_{rank}.pt")
+    state_dict = torch.load(rank0_weight_path, map_location="cpu", weights_only=False)
+    pivot_key = sorted(state_dict.keys())[0]
+    weight = state_dict[pivot_key]
+    if isinstance(weight, DTensor):
+        # get sharding info
+        device_mesh = weight.device_mesh
+        mesh = device_mesh.mesh
+        mesh_dim_names = device_mesh.mesh_dim_names
+    else:
+        # for non-DTensor
+        mesh = np.array([int(world_size)], dtype=np.int64)
+        mesh_dim_names = ("fsdp",)
+
+    print(f"Got device mesh {mesh}, mesh_dim_names {mesh_dim_names}")
+
+    assert mesh_dim_names in (("fsdp",), ("ddp", "fsdp")), f"Unsupported mesh_dim_names {mesh_dim_names}."
+
+    if "tp" in mesh_dim_names:
+        # fsdp * tp
+        total_shards = mesh.shape[-1] * mesh.shape[-2]
+        mesh_shape = (mesh.shape[-2], mesh.shape[-1])
+    else:
+        # fsdp
+        total_shards = mesh.shape[-1]
+        mesh_shape = (mesh.shape[-1],)
+
+    print(f"Processing {total_shards} model shards in total.")
+    model_state_dict_lst = []
+    model_state_dict_lst.append(state_dict)
+    model_state_dict_lst.extend([""] * (total_shards - 1))
+
+    def process_one_shard(rank, model_state_dict_lst):
+        model_path = os.path.join(local_dir, f"model_world_size_{world_size}_rank_{rank}.pt")
+        state_dict = torch.load(model_path, map_location="cpu", weights_only=False)
+        model_state_dict_lst[rank] = state_dict
+        return state_dict
+
+    with ThreadPoolExecutor(max_workers=min(32, os.cpu_count())) as executor:
+        for rank in range(1, total_shards):
+            executor.submit(process_one_shard, rank, model_state_dict_lst)
+
+    state_dict: Dict[str, List[torch.Tensor]] = {}
+    param_placements: Dict[str, List[Placement]] = {}
+    keys = set(model_state_dict_lst[0].keys())
+    for key in keys:
+        state_dict[key] = []
+        for model_state_dict in model_state_dict_lst:
+            try:
+                tensor = model_state_dict.pop(key)
+            except Exception:
+                print(f"Cannot find key {key} in rank {rank}.")
+
+            if isinstance(tensor, DTensor):
+                state_dict[key].append(tensor._local_tensor.bfloat16())
+                placements = tuple(tensor.placements)
+                # replicated placement at ddp dimension can be discarded
+                if mesh_dim_names[0] == "ddp":
+                    placements = placements[1:]
+
+                if key not in param_placements:
+                    param_placements[key] = placements
+                else:
+                    assert param_placements[key] == placements
+            else:
+                state_dict[key].append(tensor.bfloat16())
+
+    del model_state_dict_lst
+
+    for key in sorted(state_dict):
+        if not isinstance(state_dict[key], list):
+            print(f"No need to merge key {key}")
+            continue
+
+        if key in param_placements:
+            # merge shards
+            placements: Tuple[Shard] = param_placements[key]
+            if len(mesh_shape) == 1:
+                # 1-D list, FSDP without TP
+                assert len(placements) == 1
+                shards = state_dict[key]
+                state_dict[key] = merge_by_placement(shards, placements[0])
+            else:
+                # 2-D list, FSDP + TP
+                raise NotImplementedError("FSDP + TP is not supported yet.")
+        else:
+            state_dict[key] = torch.cat(state_dict[key], dim=0)
+
+    print("Merge completed.")
+    hf_path = os.path.join(local_dir, "huggingface")
+    config: PretrainedConfig = AutoConfig.from_pretrained(hf_path)
+    architectures: List[str] = getattr(config, "architectures", ["Unknown"])
+
+    if "ForTokenClassification" in architectures[0]:
+        AutoClass = AutoModelForTokenClassification
+    elif "ForCausalLM" in architectures[0]:
+        AutoClass = AutoModelForCausalLM
+    elif "ForConditionalGeneration" in architectures[0]:
+        AutoClass = AutoModelForVision2Seq
+    else:
+        raise NotImplementedError(f"Unknown architecture {architectures}.")
+
+    with torch.device("meta"):
+        model: PreTrainedModel = AutoClass.from_config(config, torch_dtype=torch.bfloat16)
+
+    assert isinstance(model, PreTrainedModel)
+    model.to_empty(device="cpu")
+
+    print(f"Saving model to {hf_path}...")
+    model.save_pretrained(hf_path, state_dict=state_dict)
+    del state_dict, model
+
+    if args.hf_upload_path:
+        upload_model_to_huggingface(hf_path, args.hf_upload_path)

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/setup.py

@@ -0,0 +1,61 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import re
+
+from setuptools import find_packages, setup
+
+
+def get_version() -> str:
+    with open(os.path.join("verl", "__init__.py"), encoding="utf-8") as f:
+        file_content = f.read()
+        pattern = r"__version__\W*=\W*\"([^\"]+)\""
+        (version,) = re.findall(pattern, file_content)
+        return version
+
+
+def get_requires() -> list[str]:
+    with open("requirements.txt", encoding="utf-8") as f:
+        file_content = f.read()
+        lines = [line.strip() for line in file_content.strip().split("\n") if not line.startswith("#")]
+        return lines
+
+
+extra_require = {
+    "dev": ["pre-commit", "ruff"],
+}
+
+
+def main():
+    setup(
+        name="verl",
+        version=get_version(),
+        description="An Efficient, Scalable, Multi-Modality RL Training Framework based on veRL",
+        long_description=open("README.md", encoding="utf-8").read(),
+        long_description_content_type="text/markdown",
+        author="verl",
+        author_email="zhangchi.usc1992@bytedance.com, gmsheng@connect.hku.hk, hiyouga@buaa.edu.cn",
+        license="Apache 2.0 License",
+        url="https://github.com/volcengine/verl",
+        package_dir={"": "."},
+        packages=find_packages(where="."),
+        python_requires=">=3.9.0",
+        install_requires=get_requires(),
+        extras_require=extra_require,
+    )
+
+
+if __name__ == "__main__":
+    main()

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/verl.egg-info/PKG-INFO

@@ -0,0 +1,262 @@
+Metadata-Version: 2.4
+Name: verl
+Version: 0.3.1.dev0
+Summary: An Efficient, Scalable, Multi-Modality RL Training Framework based on veRL
+Home-page: https://github.com/volcengine/verl
+Author: verl
+Author-email: zhangchi.usc1992@bytedance.com, gmsheng@connect.hku.hk, hiyouga@buaa.edu.cn
+License: Apache 2.0 License
+Requires-Python: >=3.9.0
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: accelerate
+Requires-Dist: codetiming
+Requires-Dist: datasets
+Requires-Dist: liger-kernel
+Requires-Dist: mathruler
+Requires-Dist: numpy
+Requires-Dist: omegaconf
+Requires-Dist: pandas
+Requires-Dist: peft
+Requires-Dist: pillow
+Requires-Dist: pyarrow>=15.0.0
+Requires-Dist: pylatexenc
+Requires-Dist: qwen-vl-utils
+Requires-Dist: ray[default]
+Requires-Dist: tensordict
+Requires-Dist: torchdata
+Requires-Dist: transformers>=4.51.0
+Requires-Dist: vllm>=0.7.3
+Requires-Dist: wandb
+Provides-Extra: dev
+Requires-Dist: pre-commit; extra == "dev"
+Requires-Dist: ruff; extra == "dev"
+Dynamic: author
+Dynamic: author-email
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: license
+Dynamic: license-file
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+# EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework
+
+[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/EasyR1)](https://github.com/hiyouga/EasyR1/stargazers)
+[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
+
+This project is a clean fork of the original [veRL](https://github.com/volcengine/verl) project to support vision language models, we thank all the authors for providing such a high-performance RL training framework.
+
+EasyR1 is efficient and scalable due to the design of **[HybirdEngine](https://arxiv.org/abs/2409.19256)** and the latest release of **[vLLM](https://github.com/vllm-project/vllm)**'s SPMD mode.
+
+## Features
+
+- Supported models
+  - Llama3/Qwen2/Qwen2.5/Qwen3 language models
+  - Qwen2/Qwen2.5-VL vision language models
+  - DeepSeek-R1 distill models
+
+- Supported algorithms
+  - GRPO
+  - Reinforce++
+  - ReMax
+  - RLOO
+
+- Supported datasets
+  - Any text, vision-text dataset in a [specific format](#custom-dataset)
+
+- Supported tricks
+  - Padding-free training
+  - Resuming from checkpoint
+  - Wandb & SwanLab & Mlflow & Tensorboard tracking
+
+## Requirements
+
+### Software Requirements
+
+- Python 3.9+
+- transformers>=4.51.0
+- flash-attn>=2.4.3
+- vllm>=0.8.3
+
+We provide a [Dockerfile](./Dockerfile) to easily build environments.
+
+We recommend using the [pre-built docker image](https://hub.docker.com/r/hiyouga/verl) in EasyR1.
+
+```bash
+docker pull hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.4-flashinfer0.2.2-cxx11abi0
+```
+
+### Hardware Requirements
+
+\* *estimated*
+
+| Method                   | Bits |  1.5B  |   3B   |   7B   |   32B   |
+| ------------------------ | ---- | ------ | ------ | ------ | ------- |
+| GRPO Full Fine-Tuning    |  AMP | 2*24GB | 4*40GB | 8*40GB | 16*80GB |
+| GRPO Full Fine-Tuning    | BF16 | 1*24GB | 1*40GB | 4*40GB |  8*80GB |
+
+> [!NOTE]
+> Use `worker.actor.fsdp.torch_dtype=bf16` and `worker.actor.optim.strategy=adamw_bf16` to enable bf16 training.
+>
+> We are working hard to reduce the VRAM in RL training, LoRA support will be integrated in next updates.
+
+## Tutorial: Run Qwen2.5-VL GRPO on [Geometry3K](https://huggingface.co/datasets/hiyouga/geometry3k) Dataset in Just 3 Steps
+
+![image](assets/qwen2_5_vl_7b_geo.png)
+
+### Installation
+
+```bash
+git clone https://github.com/hiyouga/EasyR1.git
+cd EasyR1
+pip install -e .
+```
+
+### GRPO Training
+
+```bash
+bash examples/qwen2_5_vl_7b_geo3k_grpo.sh
+```
+
+### Merge Checkpoint in Hugging Face Format
+
+```bash
+python3 scripts/model_merger.py --local_dir checkpoints/easy_r1/exp_name/global_step_1/actor
+```
+
+> [!TIP]
+> If you encounter issues with connecting to Hugging Face, consider using `export HF_ENDPOINT=https://hf-mirror.com`.
+>
+> If you want to use SwanLab logger, consider using `bash examples/qwen2_5_vl_7b_geo3k_swanlab.sh`.
+
+## Custom Dataset
+
+Please refer to the example datasets to prepare your own dataset.
+
+- Text dataset: https://huggingface.co/datasets/hiyouga/math12k
+- Image-text dataset: https://huggingface.co/datasets/hiyouga/geometry3k
+- Multi-image-text dataset: https://huggingface.co/datasets/hiyouga/journeybench-multi-image-vqa
+
+## How to Understand GRPO in EasyR1
+
+![image](assets/easyr1_grpo.png)
+
+- To learn about the GRPO algorithm, you can refer to [Hugging Face's blog](https://huggingface.co/docs/trl/v0.16.1/en/grpo_trainer).
+
+## How to Run 70B+ Model in Multi-node Environment
+
+1. Start the Ray head node.
+
+```bash
+ray start --head --port=6379 --dashboard-host=0.0.0.0
+```
+
+2. Start the Ray worker node and connect to the head node.
+
+```bash
+ray start --address=<head_node_ip>:6379
+```
+
+3. Check the Ray resource pool.
+
+```bash
+ray status
+```
+
+4. Run training script on the Ray head node only.
+
+```bash
+bash examples/qwen2_5_vl_7b_geo3k_grpo.sh
+```
+
+See the **[veRL's official doc](https://verl.readthedocs.io/en/latest/start/multinode.html)** for more details about multi-node training and Ray debugger.
+
+## Other Baselines
+
+We also reproduced the following two baselines of the [R1-V](https://github.com/deep-agent/R1-V) project.
+- [CLEVR-70k-Counting](examples/baselines/qwen2_5_vl_3b_clevr.sh): Train the Qwen2.5-VL-3B-Instruct model on counting problem.
+- [GeoQA-8k](examples/baselines/qwen2_5_vl_3b_geoqa8k.sh): Train the Qwen2.5-VL-3B-Instruct model on GeoQA problem.
+
+## Performance Baselines
+
+See [baselines.md](assets/baselines.md).
+
+## Awesome Work using EasyR1
+
+- **MMR1**: Advancing the Frontiers of Multimodal Reasoning. [![[code]](https://img.shields.io/github/stars/LengSicong/MMR1)](https://github.com/LengSicong/MMR1)
+- **Vision-R1**: Incentivizing Reasoning Capability in Multimodal Large Language Models. [![[code]](https://img.shields.io/github/stars/Osilly/Vision-R1)](https://github.com/Osilly/Vision-R1) [![[arxiv]](https://img.shields.io/badge/arxiv-2503.06749-blue)](https://arxiv.org/abs/2503.06749)
+- **Seg-Zero**: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement. [![[code]](https://img.shields.io/github/stars/dvlab-research/Seg-Zero)](https://github.com/dvlab-research/Seg-Zero) [![[arxiv]](https://img.shields.io/badge/arxiv-2503.06520-blue)](https://arxiv.org/abs/2503.06520)
+- **MetaSpatial**: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse. [![[code]](https://img.shields.io/github/stars/PzySeere/MetaSpatial)](https://github.com/PzySeere/MetaSpatial) [![[arxiv]](https://img.shields.io/badge/arxiv-2503.18470-blue)](https://arxiv.org/abs/2503.18470)
+- **Temporal-R1**: Envolving Temporal Reasoning Capability into LMMs via Temporal Consistent Reward. [![[code]](https://img.shields.io/github/stars/appletea233/Temporal-R1)](https://github.com/appletea233/Temporal-R1)
+- **NoisyRollout**: Reinforcing Visual Reasoning with Data Augmentation. [![[code]](https://img.shields.io/github/stars/John-AI-Lab/NoisyRollout)](https://github.com/John-AI-Lab/NoisyRollout) [![[arxiv]](https://img.shields.io/badge/arxiv-2504.13055-blue)](https://arxiv.org/pdf/2504.13055)
+- **GUI-R1**: A Generalist R1-Style Vision-Language Action Model For GUI Agents. [![[code]](https://img.shields.io/github/stars/ritzz-ai/GUI-R1)](https://github.com/ritzz-ai/GUI-R1) [![[arxiv]](https://img.shields.io/badge/arxiv-2504.10458-blue)](https://arxiv.org/abs/2504.10458)
+- **R1-Track**: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/Wangbiao2/R1-Track)](https://github.com/Wangbiao2/R1-Track)
+- **VisionReasoner**: Unified Visual Perception and Reasoning via Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/dvlab-research/VisionReasoner)](https://github.com/dvlab-research/VisionReasoner) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.12081-blue)](https://arxiv.org/abs/2505.12081)
+- **MM-UPT**: Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO. [![[code]](https://img.shields.io/github/stars/waltonfuture/MM-UPT)](https://github.com/waltonfuture/MM-UPT) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.22453-blue)](https://arxiv.org/pdf/2505.22453)
+- **RL-with-Cold-Start**: Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start. [![[code]](https://img.shields.io/github/stars/waltonfuture/RL-with-Cold-Start)](https://github.com/waltonfuture/RL-with-Cold-Start) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.22334-blue)](https://arxiv.org/pdf/2505.22334)
+- **ViGoRL**: Grounded Reinforcement Learning for Visual Reasoning. [![[code]](https://img.shields.io/github/stars/Gabesarch/grounded-rl)](https://github.com/Gabesarch/grounded-rl) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.22334-blue)](https://arxiv.org/abs/2505.23678)
+- **Revisual-R1**: Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/CSfufu/Revisual-R1)](https://github.com/CSfufu/Revisual-R1) [![[arxiv]](https://img.shields.io/badge/arxiv-2506.04207-blue)](https://arxiv.org/abs/2506.04207)
+- **SophiaVL-R1**: Reinforcing MLLMs Reasoning with Thinking Reward. [![[code]](https://img.shields.io/github/stars/kxfan2002/SophiaVL-R1)](https://github.com/kxfan2002/SophiaVL-R1) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.17018-blue)](https://arxiv.org/abs/2505.17018)
+  
+## TODO
+
+- Support LoRA (high priority).
+- Support ulysses parallelism for VLMs (middle priority).
+- Support more VLM architectures.
+
+> [!NOTE]
+> We will not provide scripts for supervised fine-tuning and inference in this project. If you have such requirements, we recommend using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory).
+
+### Known bugs
+
+These features are temporarily disabled for now, we plan to fix them one-by-one in the future updates.
+
+- Vision language models are not compatible with ulysses parallelism yet.
+
+## Discussion Group
+
+👋 Join our [WeChat group](assets/wechat.jpg).
+
+## FAQs
+
+> ValueError: Image features and image tokens do not match: tokens: 8192, features 9800
+
+Increase the `data.max_prompt_length` or reduce the `data.max_pixels`.
+
+> RuntimeError: CUDA Error: out of memory at /workspace/csrc/cumem_allocator.cpp:62
+
+Reduce the `worker.rollout.gpu_memory_utilization` and enable `worker.actor.offload.offload_params`.
+
+> RuntimeError: 0 active drivers ([]). There should only be one.
+
+Uninstall `deepspeed` from the current python environment.
+
+## Citation
+
+Core contributors: [Yaowei Zheng](https://github.com/hiyouga), [Junting Lu](https://github.com/AL-377), [Shenzhi Wang](https://github.com/Shenzhi-Wang), [Zhangchi Feng](https://github.com/BUAADreamer), [Dongdong Kuang](https://github.com/Kuangdd01) and Yuwen Xiong
+
+We also thank Guangming Sheng and Chi Zhang for helpful discussions.
+
+```bibtex
+@misc{zheng2025easyr1,
+  title        = {EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework},
+  author       = {Yaowei Zheng, Junting Lu, Shenzhi Wang, Zhangchi Feng, Dongdong Kuang, Yuwen Xiong},
+  howpublished = {\url{https://github.com/hiyouga/EasyR1}},
+  year         = {2025}
+}
+```
+
+We recommend to also cite the original work.
+
+```bibtex
+@article{sheng2024hybridflow,
+  title   = {HybridFlow: A Flexible and Efficient RLHF Framework},
+  author  = {Guangming Sheng and Chi Zhang and Zilingfeng Ye and Xibin Wu and Wang Zhang and Ru Zhang and Yanghua Peng and Haibin Lin and Chuan Wu},
+  year    = {2024},
+  journal = {arXiv preprint arXiv: 2409.19256}
+}
+```

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/verl.egg-info/SOURCES.txt

@@ -0,0 +1,133 @@
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+setup.py
+./verl/__init__.py
+./verl/protocol.py
+./verl/models/__init__.py
+./verl/models/monkey_patch.py
+./verl/models/transformers/__init__.py
+./verl/models/transformers/flash_attention_utils.py
+./verl/models/transformers/qwen2_vl.py
+./verl/single_controller/__init__.py
+./verl/single_controller/base/__init__.py
+./verl/single_controller/base/decorator.py
+./verl/single_controller/base/worker.py
+./verl/single_controller/base/worker_group.py
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+./verl/single_controller/base/register_center/ray.py
+./verl/single_controller/ray/__init__.py
+./verl/single_controller/ray/base.py
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+./verl/trainer/config.py
+./verl/trainer/core_algos.py
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+./verl/trainer/metrics.py
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+./verl/utils/flops_counter.py
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+./verl/utils/ulysses.py
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+verl/models/transformers/qwen2_vl.py
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+verl/single_controller/ray/base.py
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MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/verl.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/verl.egg-info/requires.txt

@@ -0,0 +1,23 @@
+accelerate
+codetiming
+datasets
+liger-kernel
+mathruler
+numpy
+omegaconf
+pandas
+peft
+pillow
+pyarrow>=15.0.0
+pylatexenc
+qwen-vl-utils
+ray[default]
+tensordict
+torchdata
+transformers>=4.51.0
+vllm>=0.7.3
+wandb
+
+[dev]
+pre-commit
+ruff