transformers pytorch llava text-generation visual-question-answering autotrain_compatible endpoints_compatible region:us
---
inference: true
pipeline_tag: visual-question-answering
---

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# 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.

**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.

**Paper or resources for more information:**
https://llava-vl.github.io/

## How to Deploy on SageMaker

Following `deploy_llava.ipynb` (full tutorial [here](https://medium.com/@liltom.eth/deploy-llava-1-5-on-amazon-sagemaker-168b2efd2489)) , bundle llava model weights and code into a `model.tar.gz` and upload to S3:

```python
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")

print(f"model uploaded to: {s3_model_uri}")
```
Then use `HuggingfaceModel` to deploy our real-time inference endpoint on SageMaker:

```python
from sagemaker.huggingface.model import HuggingFaceModel

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
   model_data=s3_model_uri,      # path to your model and script
   role=role,                    # iam role with permissions to create an Endpoint
   transformers_version="4.28.1",  # transformers version used
   pytorch_version="2.0.0",       # pytorch version used
   py_version='py310',            # python version used
   model_server_workers=1
)

# deploy the endpoint endpoint
predictor = huggingface_model.deploy(
    initial_instance_count=1,
    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
data = {
    "image" : 'https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png', 
    "question" : "Describe the image and color details.",
    # "max_new_tokens" : 1024,
    # "temperature" : 0.2,
    # "conv_mode" : "llava_v1"
}
output = predictor.predict(data)
print(output)
```
Or use [SageMakerRuntime](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker-runtime/client/invoke_endpoint.html#invoke-endpoint) to setup endpoint invoking client.

## License
Llama 2 is licensed under the LLAMA 2 Community License, 
Copyright (c) Meta Platforms, Inc. All Rights Reserved.

**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues

## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.

**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data.

## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.