File size: 3,546 Bytes
4b92543 47ee9f7 4b92543 47ee9f7 4b92543 8ab4933 639413c 8ab4933 47ee9f7 8ab4933 dc6df17 d1e4689 dc6df17 8ab4933 4b92543 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
inference: false
---
<br>
<br>
# LLaVA Model Card
## Model details
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-v1.5-7B 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-v1.5-7b")
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. |