---
license: mit
---
# 🔥 SPHINX: A Mixer of Tasks, Domains, and Embeddings

Official implementation of ['SPHINX: A Mixer of Tasks, Domains, and Embeddings Advances Multi-modal Large Language Models'](https://github.com/Alpha-VLLM/LLaMA2-Accessory/tree/main/SPHINX).

Try out our [web demo 🚀](http://imagebind-llm.opengvlab.com/) here!

## News
* **[2023-10-17]** We release the demo, code, and model of SPHINX 🎉.

## Introduction

We present $\color{goldenrod}{SPHINX}$, a versatile multi-modal large language model (MLLM) with a mixer of training tasks, data domains, and visual embeddings. 

- **Task Mix.** For all-purpose capabilities, we mix a variety of vision-language tasks for mutual improvement: VQA, REC, REG, OCR, etc.

- **Embedding Mix.** We capture robust visual representations by fusing distinct visual architectures, pre-training, and granularity.

- **Domain Mix.** For data from real-world and synthetic domains, we mix the weights of two domain-specific models for complementarity.

<p align="center">                                                                                                                                          
  <img src="https://github.com/Alpha-VLLM/LLaMA2-Accessory/blob/main/SPHINX/figs/pipeline.png"/ width="90%"> <br>
</p>
<p align="center">                                                                                                                                          
  <img src="https://github.com/Alpha-VLLM/LLaMA2-Accessory/blob/main/SPHINX/figs/pipeline2.png"/ width="90%"> <br>
</p>


## Inference
This section provides a step-by-step guide for hosting a local SPHINX demo. If you're already familiar with the LLAMA2-Accessory toolkit, note that hosting a SPHINX demo follows the same pipeline as hosting demos for the other models supported by LLAMA2-Accessory.

### Installation
SPHINX is built upon LLaMA2-Accessory, please follow the instructions [here](https://llama2-accessory.readthedocs.io/en/latest/install.html) for environment setup.

### Weights
We provide the beta-version checkpoints on [HuggingFace🤗](https://huggingface.co/Alpha-VLLM/LLaMA2-Accessory/tree/main/finetune/mm/sphinx-sft). Please download them to your own machine. The file structure should appear as follows:
```
ckpt_path/
├── consolidated.00-of-02.model.pth
└── consolidated.01-of-02.model.pth
```

### Host Local Demo
Execute the following command for demo hosting:
``` bash
cd LLaMA2-Accessory/accessory
python demos/multi_turn_mm.py --n_gpus=2 \
--tokenizer_path=/path/to/tokenizer.model --llama_type=llama_ens \
--pretrained_path ckpt_path/
```
Explanation of each argument:

+ `--n_gpus`: Number of gpus to use. Utilizing more GPUs will alleviate memory usage on each GPU through model parallelism. Currently, this argument should be set to either 1 or 2, as support for *consolidated ckpt num < gpu num* is not yet available.
+ `--tokenizer_path`: Path to the official LLaMA2 tokenizer. Note that the tokenizer file is the same for both LLaMA and LLaMA2. You may download it from [here](https://huggingface.co/Alpha-VLLM/LLaMA2-Accessory/blob/main/config/tokenizer.model).
+ `--llama_type`: The model architecture of SPHINX is defined in [accessory/model/LLM/llama_ens.py](../accessory/model/LLM/llama_ens.py),  and specifying `--llama_type=llama_ens ` tells the demo program to use this architecture.
+ `--pretrained_path`: The path to pre-trained checkpoint.