Update README.md

README.md

@@ -11,9 +11,9 @@ library_name: transformers
 
 # WORK IN PROGRESS
 
-## Model type
-TinyLLaVA, a tiny model (1.4B) trained using the exact training recipe of [LLaVA-1.5](https://github.com/haotian-liu/LLaVA).
-We trained our TinyLLaVA using [TinyLlama](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) as our LLM backbone, and [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) as our vision backbone.
+We present TinyLLaVA, a small vision-language chatbot (1.4B) that reaches comparable performances with contemporary vision language models on common benchmarks, using less parameters.
+TinyLLaVA was trained by finetuning [TinyLlama](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the [LLaVA-1.5](https://github.com/haotian-liu/LLaVA) dataset, following the training recipe of [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). For more details, please refer to the [LLaVA-1.5 paper](https://arxiv.org/abs/2310.03744).
+
 
 ## Model Performance
 We have evaluated TinyLLaVA on [GQA](https://cs.stanford.edu/people/dorarad/gqa/about.html), [VizWiz](https://www.vizwiz.com/), [VQAv2](https://visualqa.org/), [TextVQA](https://textvqa.org/) and [SQA](https://github.com/lupantech/ScienceQA).
@@ -31,8 +31,6 @@ We have evaluated TinyLLaVA on [GQA](https://cs.stanford.edu/people/dorarad/gqa/
 
 More evaluations are ongoing.
 
-## Model use
-The weights have been converted to hf format.
 
 ## How to use the model
 
@@ -79,4 +77,8 @@ raw_image = Image.open(requests.get(image_file, stream=True).raw)
 inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
 output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
 print(processor.decode(output[0][2:], skip_special_tokens=True))
-```
+```
+
+## Contact
+
+This model was trained by [Baichuan Zhou](https://baichuanzhou.github.io/), from Beihang Univerisity, under the supervision of  [Prof. Lei Huang](https://huangleibuaa.github.io/).