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README.md

@@ -8,14 +8,14 @@ inference: false
 # LLaVA Model Card
 
 ## Model details
-This is a fork from origianl [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b). This repo added `code/inference.py` and `code/requirements.txt` to provide customize inference script and environment for SageMaker deployment.
+This is a fork from origianl [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b). This repo added `code/inference.py` and `code/requirements.txt` to provide customize inference script and environment for SageMaker deployment.
 
 **Model type:**
 LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
 It is an auto-regressive language model, based on the transformer architecture.
 
 **Model date:**
-LLaVA-v1.5-13B was trained in September 2023.
+LLaVA-v1.5-7B was trained in September 2023.
 
 **Paper or resources for more information:**
 https://llava-vl.github.io/
@@ -28,7 +28,7 @@ Following `deploy_llava.ipynb` (full tutorial [here](https://medium.com/@liltom.
 from sagemaker.s3 import S3Uploader
 
 # upload model.tar.gz to s3
-s3_model_uri = S3Uploader.upload(local_path="./model.tar.gz", desired_s3_uri=f"s3://{sess.default_bucket()}/llava-v1.5-13b")
+s3_model_uri = S3Uploader.upload(local_path="./model.tar.gz", desired_s3_uri=f"s3://{sess.default_bucket()}/llava-v1.5-7b")
 
 print(f"model uploaded to: {s3_model_uri}")
 ```
@@ -53,7 +53,6 @@ predictor = huggingface_model.deploy(
     instance_type="ml.g5.xlarge",
 )
 ```
-
 ## Inference on SageMaker
 Default `conv_mode` for llava-1.5 is setup as `llava_v1` to process `raw_prompt` into meaningful `prompt`. You can also setup `conv_mode` as `raw` to directly use `raw_prompt`. 
 ```python