toshi456/llava-jp-1.3b-v1.1-commoncatalog-cc-by-ext-10k

LLaVA-JP Model Card

Model detail

Model type:

LLaVA-JP is a vision-language model that can converse about input images.
This model is an LVLM model trained using google/siglip-so400m-patch14-384 as the image encoder and llm-jp/llm-jp-1.3b-v1.0 as the text decoder. supports the input of 768 x 768 high resolution images by scaling_on_scales method.

Training:

This model was initially trained with the Vision Projector using LLaVA-Pretrain-JA.
In the second phase, it was fine-tuned with 10.5k of commoncatalog-cc-by-ext.

resources for more information: https://github.com/tosiyuki/LLaVA-JP/tree/main

How to use the model

1. Download dependencies

git clone https://github.com/tosiyuki/LLaVA-JP.git

2. Inference

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.llava_gpt2 import LlavaGpt2ForCausalLM
from llava.train.dataset import tokenizer_image_token


if __name__ == "__main__":
    model_path = 'toshi456/llava-jp-1.3b-v1.1-commoncatalog-cc-by-ext-10k'
    device = "cuda" if torch.cuda.is_available() else "cpu"
    torch_dtype = torch.bfloat16 if device=="cuda" else torch.float32

    model = LlavaGpt2ForCausalLM.from_pretrained(
        model_path, 
        low_cpu_mem_usage=True,
        use_safetensors=True,
        torch_dtype=torch_dtype,
        device_map=device,
    )
    tokenizer = transformers.AutoTokenizer.from_pretrained(
        model_path,
        model_max_length=1532,
        padding_side="right",
        use_fast=False,
    )
    model.eval()

    conv_mode = "v1"
    conv = conv_templates[conv_mode].copy()

    # 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')
    
    image_size = model.get_model().vision_tower.image_processor.size["height"]
    if model.get_model().vision_tower.scales is not None:
        image_size = model.get_model().vision_tower.image_processor.size["height"] * len(model.get_model().vision_tower.scales)
    
    if device == "cuda":
        image_tensor = model.get_model().vision_tower.image_processor(
            image, 
            return_tensors='pt', 
            size={"height": image_size, "width": image_size}
        )['pixel_values'].half().cuda().to(torch_dtype)
    else:
        image_tensor = model.get_model().vision_tower.image_processor(
            image, 
            return_tensors='pt', 
            size={"height": image_size, "width": image_size}
        )['pixel_values'].to(torch_dtype)

    # create prompt
    # ユーザー: <image>\n{prompt}
    prompt = "画像について説明してください。"
    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)

    input_ids = input_ids[:, :-1] # </sep>がinputの最後に入るので削除する
    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)

    # predict
    with torch.inference_mode():
        output_id = model.generate(
            inputs=input_ids,
            images=image_tensor,
            do_sample=False,
            temperature=1.0,
            top_p=1.0,
            max_new_tokens=256,
            streamer=streamer,
            use_cache=True,
        )

    """画像には、木製の表面に座っている猫が描かれています。猫は、ラップトップの画面に集中しています。ラップトップは、黒い金属フレームと白いキーボードを持つ、鮮やかなオレンジ色です。猫の目は閉じており、リラックスした状態を示唆しています。背景は、猫のラップトップとその周囲の詳細を強調する灰色のテクスチャーです。画像にはテキストや他のオブジェクトは含まれていません。猫とラップトップの相対的な位置関係は、猫がラップトップの画面に集中していることを示唆しています。画像には他のオブジェクトや行動は含まれていません。<EOD|LLM-jp>"""

Training dataset

Stage1 Pretrain

• LLaVA-Pretrain-JA

Stage2 Fine-tuning

• commoncatalog-cc-by-ext

Acknowledgement

• LLaVA
• LLM-jp
• scaling_on_scales

License

CC BY 4.0