Update README.md

README.md

@@ -8,35 +8,21 @@ pipeline_tag: image-text-to-text
 We trained a TinyLLaVA model with 3.1B parameters, employing the same training settings as [TinyLLaVA](https://github.com/DLCV-BUAA/TinyLLaVABench). For the Language and Vision models, we chose [Phi-2](microsoft/phi-2) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The Connector was configured with a 2-layer MLP. The dataset used for training 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 tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B --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 *
 
 hf_path = 'tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B'
 model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
+model.cuda()
 config = model.config
 tokenizer = AutoTokenizer.from_pretrained(hf_path, 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)
+prompt="What are these?"
+image_url="http://images.cocodataset.org/val2017/000000039769.jpg"
+output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, model=model, tokenizer=tokenizer)
+
+print('model output: ', output_text)
+print('runing time: ', genertaion_time)
 ```
 ### Result