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.

python

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

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

bibtex

@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

bibtex

@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}
}

turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0

Heron BLIP Japanese StableLM Base 7B

DEMO

Model Details

Usage

Model Details

Training

Training Dataset

Use and Limitations

Intended Use

Limitations

How to cite

Citations

license: cc-by-nc-4.0

turing-motors/
heron-chat-blip-ja-stablelm-base-7b-v0