Model reference · open weights
InternVL-2 is an open-weight language model from OpenGVLab. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | OpenGVLab |
|---|---|
| Type | Language models |
| Task | Vision + text |
| Parameters (lead) | 40.1B |
| Runs with | transformers |
| Based on | OpenGVLab/InternViT-6B-448px-V1-2, NousResearch/Nous-Hermes-2-Yi-34B |
| Released | 2024-02-11 |
| Popularity | 7k downloads / month |
| Licence | Open weights |
About
[📂 GitHub] [📜 InternVL 1.0] [📜 InternVL 1.5] [📜 Mini-InternVL] [📜 InternVL 2.5]
[🆕 Blog] [🗨️ Chat Demo] [🤗 HF Demo] [🚀 Quick Start] [📖 Documents]
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.
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 1.2 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 have been made publicly available.
Model Type: multimodal large language model (MLLM)
Model Stats:
Training Strategy:
* Proprietary Model
| name | image size | MMMU(val) | MMMU(test) | MathVista(testmini) | MMB(test) | MMB−CN(test) | MMVP | MME | ScienceQA(image) | POPE | TextVQA(val) | 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 | 1687/489 | 83.3 | 88.0 | 72.5 | 75.6 | 60.0 | 64.0 |
Here, we have conducted only a simple performance comparison. For more detailed performance information and additional evaluation metrics, please refer to our performance summary table.
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
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys internvl-2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (internvl-2 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"internvl-2","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.