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japanese-instructblip-alpha

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instructblip
feature-extraction
vision
image-captioning
japanese-stablelm
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japanese-instructblip-alpha / README.md
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metadata
language:
  - ja
tags:
  - instructblip
  - vision
  - image-captioning
  - japanese-stablelm
pipeline_tag: image-to-text
license:
  - other
extra_gated_heading: Access Japanese StableLM Instruct Alpha
extra_gated_description: >-
  This repository is publicly accessible, but you have to accept the conditions
  to access its files and content.
extra_gated_button_content: Access repository
extra_gated_fields:
  Name: text
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  I agree to accept the conditions and share above info with Stability AI: checkbox
extra_gated_prompt: >
  ### JAPANESE STABLELM RESEARCH LICENSE AGREEMENT

  Dated: August 7, 2023


  "Agreement" means the terms and conditions for use, reproduction, distribution
  and modification of the Software Products set forth herein.


  “Documentation” means any specifications, manuals, documentation, and other
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  "Stability AI" or "we" means Stability AI Ltd.


  "Software" means, collectively, Stability AI’s proprietary Japanese StableLM
  made available under this Agreement.


  “Software Products” means Software and Documentation.


  By using or distributing any portion or element of the Software Products, you
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  - License Rights and Redistribution.
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  —----------

  ### Japanese StableLM Acceptable Use Policy

  If you access, use, or distribute any Stability AI models, software, or other
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  We want everyone to use Stability Technology safely and responsibly. You agree
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  Nothing in this AUP is intended to prevent or impede any good faith research,
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  chance to remedy such flaws before others can exploit them. If you have
  questions about this AUP, contact us at legal@stability.ai.

Japanese InstructBLIP Alpha

japanese-instructblip-icon

Model Details

Japanese InstructBLIP Alpha is a vision-language instruction-following model that enables to generate Japanese descriptions for input images and optionally input texts such as questions.

Usage

First install additional dependencies in requirements.txt:

pip install sentencepiece einops

import torch
from transformers import LlamaTokenizer, AutoModelForVision2Seq, BlipImageProcessor
from PIL import Image
import requests

# helper function to format input prompts
def build_prompt(prompt="", sep="\n\n### "):
    sys_msg = "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。"
    p = sys_msg
    roles = ["指示", "応答"]
    user_query = "与えられた画像について、詳細に述べてください。"
    msgs = [": \n" + user_query, ": "]
    if prompt:
        roles.insert(1, "入力")
        msgs.insert(1, ": \n" + prompt)
    for role, msg in zip(roles, msgs):
        p += sep + role + msg
    return p

# load model
model = AutoModelForVision2Seq.from_pretrained("stabilityai/japanese-instructblip-alpha", trust_remote_code=True)
processor = BlipImageProcessor.from_pretrained("stabilityai/japanese-instructblip-alpha")
tokenizer = LlamaTokenizer.from_pretrained("novelai/nerdstash-tokenizer-v1", additional_special_tokens=['▁▁'])
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# prepare inputs
url = "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
prompt = "" # input empty string for image captioning. You can also input questions as prompts 
prompt = build_prompt(prompt)
inputs = processor(images=image, return_tensors="pt")
text_encoding = tokenizer(prompt, add_special_tokens=False, return_tensors="pt")
text_encoding["qformer_input_ids"] = text_encoding["input_ids"].clone()
text_encoding["qformer_attention_mask"] = text_encoding["attention_mask"].clone()
inputs.update(text_encoding)

# generate
outputs = model.generate(
    **inputs.to(device, dtype=model.dtype),
    num_beams=5,
    max_new_tokens=32,
    min_length=1,
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip()
print(generated_text)
# 桜と東京スカイツリー

Model Details

Training

Japanese InstructBLIP Alpha leverages the InstructBLIP architecture. It consists of 3 components: a frozen vision image encoder, a Q-Former, and a frozen LLM. The vision encoder and the Q-Former were initialized with Salesforce/instructblip-vicuna-7b. For the frozen LLM, Japanese-StableLM-Instruct-Alpha-7B model was used. During training, only Q-Former was trained.

Training Dataset

The training dataset includes the following public datasets:

Use and Limitations

Intended Use

This model is intended to be used by the open-source community in chat-like applications in adherence with the research license.

Limitations and bias

Although the aforementioned datasets help to steer the base language models into "safer" distributions of text, not all biases and toxicity can be mitigated through fine-tuning. We ask that users be mindful of such potential issues that can arise in generated responses. Do not treat model outputs as substitutes for human judgment or as sources of truth. Please use responsibly.

How to cite 

@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}
}

Citations 

@misc{dai2023instructblip,
    title         = {InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning}, 
    author        = {Wenliang Dai and Junnan Li and Dongxu Li and Anthony Meng Huat Tiong and Junqi Zhao and Weisheng Wang and Boyang Li and Pascale Fung and Steven Hoi},
    year          = {2023},
    eprint        = {2305.06500},
    archivePrefix = {arXiv},
    primaryClass  = {cs.CV}
}