README.md

metadata

language:
  - ja
tags:
  - heron
  - vision
  - image-captioning
  - VQA
pipeline_tag: image-to-text
license:
  - cc-by-nc-4.0
inference: false

Heron BLIP Japanese StableLM Base 7B

DEMO

You can play the demo of this model here.

Model Details

Heron BLIP Japanese StableLM Base 7B is a vision-language model that can converse about input images.
This model was trained using the heron library. Please refer to the code for details.

Usage

Follow the installation guide.

import torch
from heron.models.video_blip import VideoBlipForConditionalGeneration, VideoBlipProcessor
from transformers import LlamaTokenizer

device_id = 0
device = f"cuda:{device_id}"

max_length = 512
MODEL_NAME = "turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0"
    
model = VideoBlipForConditionalGeneration.from_pretrained(
    MODEL_NAME, torch_dtype=torch.float16, ignore_mismatched_sizes=True
)

model = model.half()
model.eval()
model.to(device)

# prepare a processor
processor = VideoBlipProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
tokenizer = LlamaTokenizer.from_pretrained("novelai/nerdstash-tokenizer-v1", additional_special_tokens=['▁▁'])
processor.tokenizer = tokenizer

import requests
from PIL import Image

# prepare inputs
url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg"
image = Image.open(requests.get(url, stream=True).raw)

text = f"##human: この画像の面白い点は何ですか?\n##gpt: "

# do preprocessing
inputs = processor(
    text=text,
    images=image,
    return_tensors="pt",
    truncation=True,
)

inputs = {k: v.to(device) for k, v in inputs.items()}
inputs["pixel_values"] = inputs["pixel_values"].to(device, torch.float16)

# set eos token
eos_token_id_list = [
    processor.tokenizer.pad_token_id,
    processor.tokenizer.eos_token_id,
    int(tokenizer.convert_tokens_to_ids("##"))
]

# do inference
with torch.no_grad():
    out = model.generate(**inputs, max_length=256, do_sample=False, temperature=0., eos_token_id=eos_token_id_list, no_repeat_ngram_size=2)

# print result
print(processor.tokenizer.batch_decode(out))

Model Details

• Developed by: Turing Inc.
• Adaptor type: BLIP2
• Lamguage Model: Japanese StableLM Base Alpha
• Language(s): Japanese

Training

This model was initially trained with the Adaptor using STAIR Captions. In the second phase, it was fine-tuned with LLaVA-Instruct-150K-JA and Japanese Visual Genome using LoRA.

Training Dataset

• LLaVA-Instruct-150K-JA
• Japanese STAIR Captions
• Japanese Visual Genome VQA dataset

Use and Limitations

Intended Use

This model is intended for use in chat-like applications and for research purposes.

Limitations

The model may produce inaccurate or false information, and its accuracy is not guaranteed. It is still in the research and development stage.

How to cite

@misc{BlipJapaneseStableLM, 
    url    = {[https://huggingface.co/turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0](https://huggingface.co/turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0)}, 
    title  = {Heron BLIP Japanese StableLM Base 7B}, 
    author = {Kotaro Tanahashi, Yuichi Inoue, and Yu Yamaguchi}
}

Citations

@misc{JapaneseInstructBLIPAlpha, 
    url    = {[https://huggingface.co/stabilityai/japanese-instructblip-alpha](https://huggingface.co/stabilityai/japanese-instructblip-alpha)}, 
    title  = {Japanese InstructBLIP Alpha}, 
    author = {Shing, Makoto and Akiba, Takuya}
}

---

license: cc-by-nc-4.0