update

README.md

@@ -1,44 +1,25 @@
----
-license: apache-2.0
-pipeline_tag: image-text-to-text
----
-### TinyLLaVA
+**<center><span style="font-size:2em;">TinyLLaVA</span></center>**
 
-We trained 1 model with fewer than 1B parameters using the TinyLLaVA approach, employing the same training settings as [TinyLLaVA](https://github.com/DLCV-BUAA/TinyLLaVABench). For the Language and Vision models, we chose [OpenELM-450M-Instruct](apple/OpenELM-450M-Instruct) and [clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16), respectively. The Connector was configured with a 2-layer MLP. The dataset used for training is the save as [LLaVA](https://github.com/haotian-liu/LLaVA). During testing, we found that [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.55B) exhibited significantly faster inference speed on CPU compared to [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B)
+[![arXiv](https://img.shields.io/badge/Arxiv-2402.14289-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2402.14289)[![Github](https://img.shields.io/badge/Github-Github-blue.svg)](https://github.com/TinyLLaVA/TinyLLaVA_Factory)[![Demo](https://img.shields.io/badge/Demo-Demo-red.svg)](http://8843843nmph5.vicp.fun/#/)
+TinyLLaVA has released a family of small-scale Large Multimodel Models(LMMs), ranging from 0.55B to 3.1B. Our best model, TinyLLaVA-Phi-2-SigLIP-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL.
+### TinyLLaVA
+Here, we introduce TinyLLaVA-OpenELM-450M-SigLIP-0.55B, which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [OpenELM-450M-Instruct](apple/OpenELM-450M-Instruct) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The dataset used for training this model is the [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md) dataset.
 
 ### Usage
-
-1. you need to download the generate file "generate_model.py".
-2. running the following command:
-
-```bash
-python generate_model --model jiajunlong/TinyLLaVA-0.89B --prompt 'you want to ask' --image '/path/to/related/image'
-```
-
-or  execute the following test code:
-
+Execute the following test code:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from generate_model import *
-model = AutoModelForCausalLM.from_pretrained("jiajunlong/TinyLLaVA-0.55B", trust_remote_code=True)
+hf_path = 'jiajunlong/TinyLLaVA-OpenELM-450M-SigLIP-0.55B'
+model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
+model.cuda()
 config = model.config
-tokenizer = AutoTokenizer.from_pretrained("jiajunlong/TinyLLaVA-0.55B", use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
-prompt="you want to ask"
-image="/path/to/related/image"
-output_text, genertaion_time = generate(prompt=prompt, image=image, model=model, tokenizer=tokenizer)
-print_txt = (
-        f'\r\n{"=" * os.get_terminal_size().columns}\r\n'
-        '\033[1m Prompt + Generated Output\033[0m\r\n'
-        f'{"-" * os.get_terminal_size().columns}\r\n'
-        f'{output_text}\r\n'
-        f'{"-" * os.get_terminal_size().columns}\r\n'
-        '\r\nGeneration took'
-        f'\033[1m\033[92m {round(genertaion_time, 2)} \033[0m'
-        'seconds.\r\n'
-    )
-print(print_txt)
+tokenizer = AutoTokenizer.from_pretrained(hf_path, use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
+prompt="What are these?"
+image_url="http://images.cocodataset.org/test-stuff2017/000000000001.jpg"
+output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, tokenizer=tokenizer)
+print('model output:', output_text)
+print('runing time:', genertaion_time)
 ```
-
 ### Result
 
 |                          model_name                          | gqa   | textvqa | sqa   | vqav2 | MME     | MMB   | MM-VET |
@@ -46,3 +27,11 @@ print(print_txt)
 | [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B) | 60.3  | 51.7    | 60.3  | 76.9  | 1276.5  | 55.2  | 25.8   |
 | [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.89B) | 50.38 | 36.37   | 50.02 | 65.44 | 1056.69 | 26.29 | 15.4   |
 
+P.S. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) is an open-source modular codebase for small-scale LMMs with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. This code repository provides standard training&evaluating pipelines, flexible data preprocessing&model configurations, and easily extensible architectures. Users can customize their own LMMs with minimal coding effort and less coding mistake.
+TinyLLaVA Factory integrates a suite of cutting-edge models and methods. 
+  - LLM currently supports OpenELM, TinyLlama, StableLM, Qwen, Gemma, and Phi. 
+  - Vision tower currently supports CLIP, SigLIP, Dino, and combination of CLIP and Dino.
+  - Connector currently supports MLP, Qformer, and Resampler.
+
+
+