**Large multi-modal models for strong performance and efficient deployment**

OmniLMM-3B 🤗 🤖 | OmniLMM-12B 🤗 🤖

**OmniLMM** is a family of open-source large multimodal models (LMMs) adept at vision & language modeling. The model processes images and text inputs and delivers high-quality text outputs. We release two featured versions of OmniLMM that are targeted at **strong performance and efficient deployment**: - **OmniLMM-12B**: Leading performance among comparable-sized models on multiple benchmarks. - **OmniLMM-3B**: Frontier end device multi-modal conversation with promising performance. [中文文档](./README_zh.md) ## Contents - [Contents](#contents) - [OmniLMM-12B](#omnilmm-12b) - [Evaluation](#evaluation) - [Examples](#examples) - [OmniLMM-3B](#omnilmm-3b) - [Evaluation](#evaluation-1) - [Examples](#examples-1) - [Demo](#demo) - [Install](#install) - [Inference](#inference) - [Model Zoo](#model-zoo) - [Multi-turn Conversation](#multi-turn-conversation) - [✅ TODO](#-todo) - [Model License](#model-license) - [Statement](#statement) - [🏫 Institutions](#-institutions) ## OmniLMM-12B **OmniLMM-12B** is the most capable version. The model is built based on EVA02-5B and Zephyr-7B-β, connected with a perceiver resampler layer, and trained on multimodal data in a curriculum fashion. The model has three notable features: - 🔥 **Strong Performance.** OmniLMM-12B achieves **leading performance** among models with comparable sizes, surpassing established LMMs on multiple benchmarks (including MME, MMBench, SEED-Bench, etc). The model also endows rich multi-modal world knowledge. - 🏆 **Trustworthy Behavior.** LMMs are known for suffering from hallucination, often generating text that is not factually grounded in images (e.g., faithfully describing non-existing objects in images). OmniLMM-12B is **the first state-of-the-art open-source LMM aligned via multimodal RLHF for trustworthy behavior** (using our recent [RLHF-V](https://rlhf-v.github.io/) technique). It **ranks #1** among open-source models on [MMHal-Bench](https://huggingface.co/datasets/Shengcao1006/MMHal-Bench), and **outperforms GPT-4V** on [Object HalBench](https://arxiv.org/abs/2312.00849). - 🕹 **Real-time Multimodal Interaction.** We combine the OmniLMM-12B and GPT-3.5 (text-only) into a **real-time multimodal interactive assistant**. The assistant accepts video streams from the camera and speech streams from the microphone and emits speech output. While still primary, we find the model can **replicate some of the fun cases shown in the Gemini Demo video, without any video edition**. ### Evaluation
Click to view results on MME, MMBench, MMMU, MMBench, MMHal-Bench, Object HalBench, SeedBench, LLaVA Bench W, MathVista.
Model Size MME MMB dev (en) MMMU val MMHal-Bench Object HalBench SeedBench-I MathVista LLaVA Bench W
GPT-4V† - 1409 75.1 56.8 3.53 / 70.8 86.4 / 92.7 71.6 47.8 93.1
Qwen-VL-Plus† - 1681 66.2 45.2 - - 65.7 36.0 73.7
Yi-VL 6B 6.7B - 68.2 39.1 - - 66.1 28.0 39.9
Qwen-VL-Chat 9.6B 1488 60.6 35.9 2.93 / 59.4 56.2 / 80.0 64.8 33.8 67.7
CogVLM 17.4B 1438 63.7 32.1 2.68 / 52.1 73.6 / 87.4 68.8 34.7 73.9
LLaVA 1.5 13.6B 1531 68.2 36.4 2.71 / 51.0 53.7 / 77.4 68.1 26.4 64.6
OmniLMM-12B 11.6B 1637 71.6 40.7 3.45 / 68.8 90.3 / 95.5 71.1 34.9 72.0
†: Proprietary models
### Examples

We combine the OmniLMM-12B and GPT-3.5 (text-only) into a **real-time multimodal interactive assistant**. Video frames are described in text using OmniLMM-12B, and ChatGPT 3.5 (text-only) is employed to generate response according to the descriptions and user prompts. The demo video is a raw recording without edition.
## OmniLMM-3B **OmniLMM-3B** (i.e., MiniCPM-V) is an efficient version with promising performance for deployment. The model is built based on SigLip-400M and [MiniCPM-2.4B](https://github.com/OpenBMB/MiniCPM/), connected by a perceiver resampler. Notable features of OmniLMM-3B include: - ⚡️ **High Efficiency.** OmniLMM-3B can be **efficiently deployed on most GPU cards and personal computers**, and **even on end devices such as mobile phones**. In terms of visual encoding, we compress the image representations into 64 tokens via a perceiver resampler, which is significantly fewer than other LMMs based on MLP architecture (typically > 512 tokens). This allows OmniLMM-3B to operate with **much less memory cost and higher speed during inference**. - 🔥 **Promising Performance.** OmniLMM-3B achieves **state-of-the-art performance** on multiple benchmarks (including MMMU, MME, and MMbech, etc) among models with comparable sizes, surpassing existing LMMs built on Phi-2. It even **achieves comparable or better performance than the 9.6B Qwen-VL-Chat**. - 🙌 **Bilingual Support.** OmniLMM-3B is **the first edge-deployable LMM supporting bilingual multimodal interaction in English and Chinese**. This is achieved by generalizing multimodal capabilities across languages, a technique from our ICLR 2024 spotlight [paper](https://arxiv.org/abs/2308.12038). ### Evaluation
Model Size MME MMB dev (en) MMB dev (zh) MMMU val CMMMU val
LLaVA-Phi 3B 1335 59.8 - - -
MobileVLM 3B 1289 59.6 - - -
Imp-v1 3B 1434 66.5 - - -
Qwen-VL-Chat 9.6B 1487 60.6 56.7 35.9 30.7
CogVLM 17.4B 1438 63.7 53.8 32.1 -
OmniLMM-3B 3B 1452 67.3 61.9 34.7 32.1
### Examples We deploy OmniLLM-3B on end devices. The demo video is the raw screen recording on a OnePlus 9R without edition.

## Demo Click here to try out the Demo of [OmniLMM-12B](http://120.92.209.146:8081) and [OmniLMM-3B](http://120.92.209.146:80). ## Install 1. Clone this repository and navigate to the source folder ```bash git clone https://github.com/OpenBMB/OmniLMM.git cd OmniLMM ``` 2. Create conda environment ```Shell conda create -n OmniLMM python=3.10 -y conda activate OmniLMM ``` 3. Install dependencies ```shell pip install -r requirements.txt ``` ## Inference ### Model Zoo | Model | Description | Download Link | |:----------------------|:-------------------|:---------------:| | OmniLMM-12B | The most capable version with strong performance. | [🤗](https://huggingface.co/openbmb/OmniLMM-12B)    [](https://modelscope.cn/models/OpenBMB/OmniLMM-12B/files) | | OmniLMM-3B | The efficient version for end device deployment. | [🤗](https://huggingface.co/openbmb/MiniCPM-V)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V/files) | ### Multi-turn Conversation Please refer to the following codes to run `OmniLMM`.
```python from chat import OmniLMMChat, img2base64 chat_model = OmniLMMChat('openbmb/OmniLMM-12B') # or 'openbmb/MiniCPM-V' im_64 = img2base64('./assets/COCO_test2015_000000262144.jpg') # First round chat msgs = [{"role": "user", "content": "What are the people doing?"}] inputs = {"image": im_64, "question": json.dumps(msgs)} answer = chat_model.process(inputs) print(answer) # Second round chat # pass history context of multi-turn conversation msgs.append({"role": "assistant", "content": answer}) msgs.append({"role": "user", "content": "Describe the image"}) inputs = {"image": im_64, "question": json.dumps(msgs)} answer = chat_model.process(inputs) print(answer) ``` We can obtain the following results: ``` "The people in the image are playing baseball. One person is pitching a ball, another one is swinging a bat to hit it, and there's also an umpire present who appears to be watching the game closely." "The image depicts a baseball game in progress. A pitcher is throwing the ball, while another player is swinging his bat to hit it. An umpire can be seen observing the play closely." ``` ## ✅ TODO - [ ] Fine-tuning support - [ ] Local Web-UI deployment - [ ] Code release for real-time interactive assistant - [ ] Improved OCR capability ## Model License The code in this repo is released according to [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) The usage of OmniLMMs' parameters is subject to "[General Model License Agreement - Source Notes - Publicity Restrictions - Commercial License](https://github.com/OpenBMB/General-Model-License/blob/main/通用模型许可协议-来源说明-宣传限制-商业授权.md)" The parameters are fully open to acedemic research Please contact cpm@modelbest.cn to obtain a written authorization for commercial uses. Free commercial use is also allowed after registration. ## Statement As LMMs, OmniLMMs generate contents by learning a large mount of multimodal corpora, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by OmniLMMs does not represent the views and positions of the model developers We will not be liable for any problems arising from the use of OmniLMM open source models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model. ## 🏫 Institutions This project is developed by the following institutions: - [THUNLP](https://nlp.csai.tsinghua.edu.cn/) - [ModelBest](https://modelbest.cn/) - [Zhihu](https://www.zhihu.com/ )