README.md

metadata

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

Chat-Vector-LLaVA-v1.5-7b-JA Model Card

Model detail

Model type: Chat-Vector-LLaVA-v1.5-7b-JA is a vision-language model that can converse about input images in Japanese.
This model was created by adding and subtracting the weights of the llava-v1.5-7b, Llama-2-7b-hf, and ELYZA-japanese-Llama-2-7b models using the Chat Vector method as follows.

ELYZA-japanese-Llama-2-7b + (llava-v1.5-7b - Llama-2-7b-hf)

Chat-Vector-LLaVA-v1.5-7b-JAは、入力画像について日本語で会話できるvision-language modelです。
このモデルはChat Vectorの手法でllava-v1.5-7bとLlama-2-7b-hfとELYZA-japanese-Llama-2-7bのモデルの重みを以下の通り加減算することで作成しました。

ELYZA-japanese-Llama-2-7b + (llava-v1.5-7b - Llama-2-7b-hf)

Comparing VLMs

Model	JA-VG-VQA-500 (ROUGE-L)	JA-VLM-Bench-In-the-Wild (ROUGE-L)	Heron-Bench(Detail)	Heron-Bench(Conv)	Heron-Bench(Complex)	Heron-Bench(Average)
Japanese Stable VLM	-	40.50	25.15	51.23	37.84	38.07
EvoVLM-JP-v1-7B	19.70	51.25	50.31	44.42	40.47	45.07
Heron BLIP Japanese StableLM Base 7B llava-620k	14.51	33.26	49.09	41.51	45.72	45.44
Heron GIT Japanese StableLM Base 7B	15.18	37.82	42.77	54.20	43.53	46.83
llava-jp-1.3b-v1.0-620k	12.69	44.58	51.21	41.05	45.95	44.84
llava-jp-1.3b-v1.1	13.33	44.40	50.00	51.83	48.98	50.39
chat-vector-llava-v1.5-7b-ja	18.64	42.23	53.61	44.36	44.48	46.10

How to use the model

1. Download dependencies

git clone https://github.com/tosiyuki/vlm-chat-vector-ja.git

2. Inference

import requests
import torch
import transformers
from PIL import Image

from transformers.generation.streamers import TextStreamer
from llava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
from llava.mm_utils import tokenizer_image_token, process_images


if __name__ == "__main__":
    model_path = 'toshi456/chat-vector-llava-v1.5-7b-ja'
    device = "cuda" if torch.cuda.is_available() else "cpu"
    torch_dtype = torch.bfloat16 if device=="cuda" else torch.float32

    model = LlavaLlamaForCausalLM.from_pretrained(
        model_path, 
        device_map=device,
        low_cpu_mem_usage=True,
        use_safetensors=True,
        torch_dtype=torch.float16,
    ).eval()
    tokenizer = transformers.AutoTokenizer.from_pretrained(
        model_path,
        model_max_length=1024,
        padding_side="right",
        use_fast=False,
    )
    model.get_model().vision_tower.load_model()
    model = model.to(device)

    eos_token_id_list = [
        tokenizer.eos_token_id,
        tokenizer.bos_token_id,
    ]

    # image pre-process
    image_url = "https://huggingface.co/rinna/bilingual-gpt-neox-4b-minigpt4/resolve/main/sample.jpg"
    image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')

    if not isinstance(image, list):
        image = [image]
    
    image_tensor = process_images(image, model.get_model().vision_tower.image_processor, model.config)
    if type(image_tensor) is list:
        image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
    else:
        image_tensor = image_tensor.to(model.device, dtype=torch.float16)

    # create prompt
    # ユーザー: <image>\n{prompt}
        conv_mode = "llava_llama_2"
    conv = conv_templates[conv_mode].copy()
    prompt = "猫の隣には何がありますか？"
    inp = DEFAULT_IMAGE_TOKEN + '\n' + prompt
    conv.append_message(conv.roles[0], inp)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()

    input_ids = tokenizer_image_token(
        prompt, 
        tokenizer, 
        IMAGE_TOKEN_INDEX, 
        return_tensors='pt'
    ).unsqueeze(0)
    if device == "cuda":
        input_ids = input_ids.to(device)

    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    keywords = [stop_str]
    streamer = TextStreamer(tokenizer, skip_prompt=True, timeout=20.0)

    # parameter
    temperature = 0.0
    top_p = 1.0
    max_new_tokens=256

    # predict
    with torch.inference_mode():
        model.generate(
            inputs=input_ids,
            images=image_tensor,
            do_sample=True if temperature > 0 else False,
            temperature=temperature,
            top_p=top_p,
            max_new_tokens=max_new_tokens,
            streamer=streamer,
            use_cache=True,
            eos_token_id=eos_token_id_list,
        )

    """猫の隣には、コンピューター（パソコン）があります。<s>"""

Acknowledgement

• LLaVA
• Chat Vector

License

cc-by-nc-4.0