KO
|
EN
gitlite — search
Search
#typescript
#ai-agents
#ai
#dsh-plugin
#deepseek-harness
#open-source
#cli
#claude-code
#codex
#developer-tools
#react
#windows
verl-internvl
★ 53
Open GitHub ↗
No description available.
Download README (.md)
Explore Similar Repositories
PsyLLM
:
Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling
awesome-zk-proofs
:
Resources for learning zero knowledge proof systems
abs-agg
:
An flexible custom metadata provider
Rank-GRPO
:
(ICLR'26 + Netflix) Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning
Churn-predictor
:
No description available.
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
verl-internvl
?
Download (.md)
<div align="center"> # InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency <div align="center"> <img width="500" alt="image" src="https://github.com/user-attachments/assets/930e6814-8a9f-43e1-a284-118a5732daa4"> <br> </div> [\[🔥 InternVL3.5 Report\]](https://huggingface.co/papers/2508.18265) [\[🗨️ Chat Demo\]](https://chat.intern-ai.org.cn/)  </div> This repository open-sources the training code of InternVL3.5 during the online RL stage, which is built upon the [PR](https://github.com/volcengine/verl/pull/2327) in [verl](https://github.com/volcengine/verl). Compared to the original PR, we have corrected the dialogue template for InternVL and updated a monkey patch for InternVL to enable sequence-parallel. For training details, please refer to the provided [scripts](shell). We use [MMPR-Tiny](https://huggingface.co/datasets/OpenGVLab/MMPR-Tiny) as the training dataset and initialize the model with InternVL3.5 trained after MPO. We also provide a [packaged conda environment](https://huggingface.co/Weiyun1025/InternVL3_5-RL-conda-env/blob/main/verl-internvl.tar.gz) for easy reproduction. For the original README of verl, please refer to [this file](README_verl.md). ## Experimental Results Based on this codebase, the InternVL3.5 series across all model scales achieve a significant improvement in reasoning performance.   ## Quick Start ### Training We open-source our training data (i.e., [MMPR-Tiny](https://huggingface.co/datasets/OpenGVLab/MMPR-Tiny)) on HuggingFace. To reproduce our training results, you need to download this dataset and move it into this folder. Additionally, considering that verl requires a validation dataset to be loaded, please prepare this data using this [script](examples/data_preprocess/geo3k.py). ``` ├── MMPR-Tiny │ ├── images │ └── mmpr_tiny.parquet ├── verl_data │ └── geo3k │ └── test.parquet ├── verl └── README.md ``` We also provide a [packaged conda environment](https://huggingface.co/Weiyun1025/InternVL3_5-RL-conda-env/blob/main/verl-internvl.tar.gz) for easy reproduction. After preparing the dataset and conda environment, you can launch the training using the commond as follows: ```shell sh shell/internvl3_5_8b.sh ``` ### Evaluation We mainly use [VLMEvalkit](https://github.com/open-compass/VLMEvalKit) to evaluate our models. Please refer to their documentation and our model configs for more details. As an example, you can set the config as follows: ```python "InternVL3_5-8B-Thinking": partial( InternVLChat, model_path="/path/to/your/moel", version="V2.0", cot_prompt_version="r1", max_new_tokens=32768, do_sample=True, use_lmdeploy=True, ), ``` ## Citation If you find this project useful in your research, please consider citing: ```BibTeX @article{wang2025internvl3_5, title={InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency}, author={Wang, Weiyun and Gao, Zhangwei and Gu, Lixin and Pu, Hengjun and Cui, Long and Wei, Xingguang and Liu, Zhaoyang and Jing, Linglin and Ye, Shenglong and Shao, Jie and others}, journal={arXiv preprint arXiv:2508.18265}, year={2025} } @article{wang2024mpo, title={Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization}, author={Wang, Weiyun and Chen, Zhe and Wang, Wenhai and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Zhu, Jinguo and Zhu, Xizhou and Lu, Lewei and Qiao, Yu and Dai, Jifeng}, journal={arXiv preprint arXiv:2411.10442}, year={2024} } ```
