v1.2
版本发布时间: 2024-02-14 04:07:49
OpenGVLab/InternVL最新发布版本:v1.5.0(2024-05-09 00:04:27)
Date: 2024/02/12
Developed by: Zhe Chen, Weiyun Wang, Wenhai Wang, Erfei Cui, Zhangwei Gao, Xizhou Zhu, Lewei Lu, Tong Lu, Yu Qiao, Jifeng Dai
We are excited to introduce InternVL-Chat-V1.2. Inspired by LLaVA-NeXT-34B, we have also adopted Nous-Hermes-2-Yi-34B as the language model. Below is the pipeline.
From the experimental results, we've observed that a stronger language model (34B) can better leverage the powerful capabilities of our vision foundation model (InternViT-6B).
For better training reproducibility, we follow the minimalist design and data efficiency similar to LLaVA-NeXT. To reduce training costs, we provide a pre-trained MLP projector and only employ around 1 million visual instruction tuning samples for SFT. Our model has a total of 40 billion parameters and can be trained within 1.5 days using 32 A100 GPUs. The code, data, and model will be made publicly available.
Data Preparation
Inspired by LLaVA-NeXT, we adopted a data-efficient SFT strategy to train InternVL-Chat-V1.2, utilizing approximately 1.2M of visual instruction tuning samples in total, all of which are fully open-source. In a macro sense, we build upon ShareGPT-4V and additionally integrate LLaVA-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, and SynthDoG-EN. Most of the data remains consistent with LLaVA-NeXT.
For more details about data preparation, please see here.
Performance
* Proprietary Model
| name | image size | MMMU (val) |
MMMU (test) |
MathVista (testmini) |
MMB (test) |
MMB−CN (test) |
MMVP | MME | ScienceQA (image) |
POPE | TextVQA | SEEDv1 (image) |
VizWiz (test) |
GQA (test) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GPT-4V* | unknown | 56.8 | 55.7 | 49.9 | 77.0 | 74.4 | 38.7 | 1409/517 | - | - | 78.0 | 71.6 | - | - |
| Gemini Ultra* | unknown | 59.4 | - | 53.0 | - | - | - | - | - | - | 82.3 | - | - | - |
| Gemini Pro* | unknown | 47.9 | - | 45.2 | 73.6 | 74.3 | 40.7 | 1497/437 | - | - | 74.6 | 70.7 | - | - |
| Qwen-VL-Plus* | unknown | 45.2 | 40.8 | 43.3 | 67.0 | 70.7 | - | 1681/502 | - | - | 78.9 | 65.7 | - | - |
| Qwen-VL-Max* | unknown | 51.4 | 46.8 | 51.0 | 77.6 | 75.7 | - | - | - | - | 79.5 | - | - | - |
| LLaVA-NEXT-34B | 672x672 | 51.1 | 44.7 | 46.5 | 79.3 | 79.0 | - | 1631/397 | 81.8 | 87.7 | 69.5 | 75.9 | 63.8 | 67.1 |
| InternVL-Chat-V1.2 | 448x448 | 51.6 | 46.2 | 47.7 | 82.2 | 81.2 | 56.7 | 1672/509 | 83.3 | 88.0 | 69.7 | 75.6 | 60.0 | 64.0 |
- MMBench results are collected from the leaderboard.
- In most benchmarks, InternVL-Chat-V1.2 achieves better performance than LLaVA-NeXT-34B.
Training (SFT)
We provide slurm scripts for multi-node multi-GPU training. You can use either 32 or 64 GPUs to train this model. If you use 64 GPUs, training will take approximately 18 hours.
For more details about training, please see here.
The hyperparameters used for finetuning are listed in the following table.
| Hyperparameter | Trainable Param | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
|---|---|---|---|---|---|---|
| InternVL-Chat-V1.2 | 40B (full model) | 512 | 1e-5 | 1 | 2048 | 0.05 |