Model Description
llava-calm2-siglip is an experimental Vision Language Model that can answer questions in Japanese about images.
Usage
from PIL import Image
import requests
from transformers import AutoProcessor, LlavaForConditionalGeneration
import torch
model = LlavaForConditionalGeneration.from_pretrained(
"cyberagent/llava-calm2-siglip",
torch_dtype=torch.bfloat16,
).to(0)
processor = AutoProcessor.from_pretrained("cyberagent/llava-calm2-siglip")
prompt = """USER: <image>
ใใฎ็ปๅใ่ชฌๆใใฆใใ ใใใ
ASSISTANT: """
url = "https://unsplash.com/photos/LipkIP4fXbM/download?force=true&w=640"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
inputs = processor(text=prompt, images=image, return_tensors="pt").to(0, torch.bfloat16)
generate_ids = model.generate(**inputs,
max_length=500,
do_sample=True,
temperature=0.2,
)
output = processor.tokenizer.decode(generate_ids[0][:-1], clean_up_tokenization_spaces=False)
print(output)
# USER: <image>
# ใใฎ็ปๅใ่ชฌๆใใฆใใ ใใใ
# ASSISTANT: ็ปๅใซใฏใๆจ่ฃฝใฎใใผใใซใฎไธใซ็ฝฎใใใใใใ็ผใๅจใง็ผใใใ3ใคใฎใใ็ผใใๆ ใฃใฆใใพใใใใ็ผใใฏใๅฐ้บฆ็ฒใใใผในใซใใ็ๅฐใไธธใ็ผใใไธญใซใฟใณใๅคฉใใใ็ด
ใทใงใฆใฌใชใฉใฎๅ
ทๆใๅ
ฅใใใใฎใงใใใใ็ผใใฏใใฝใผในใใใจใใผใบใ้ๆตท่ใใใคใใถใใใใใฆ้ฃในใใใจใๅคใใงใใ
Chat Template
USER: <image>
{user_message1}
ASSISTANT: {assistant_message1}<|endoftext|>
USER: {user_message2}
ASSISTANT: {assistant_message2}<|endoftext|>
USER: {user_message3}
ASSISTANT: {assistant_message3}<|endoftext|>
Model Details
- Model size: 7B
- Model type: Transformer-based Vision Language Model
- Language(s): Japanese, English
- Developed by: CyberAgent, Inc.
- License: Apache-2.0
Training
This model is a visual language instruction-following model based on LLaVA 1.5. It utilizes cyberagent/calm2-7b-chat as its language model and google/siglip-so400m-patch14-384 as its image encoder. During training, the first stage involved learning the MLP projection from scratch, which was followed by additional training of both the language model and the MLP projection in the second stage.
Dataset for Visual Instruction Tuning
In the second stage of Visual Instruction Tuning, we train on a dataset of conversations about images. These conversational data are generated using our in-house large-scale Japanese language model, based on images, captions, object labels, and bounding boxes from the MS-COCO and VisualGenome. For methods of generating conversational datasets for Visual Instruction Tuning without using images, please refer to LLaVA 1.5.
Evaluation Results
LLaVA Bench In-the-wild
Model | Detail | Conv | Complex | Average |
---|---|---|---|---|
llava-calm2-siglip | 51.2 | 55.9 | 65.51 | 57.54 |
Japanese Stable VLM | 26.02 | 24.84 | 29.18 | 26.68 |
SakanaAI EvoVLM-JP | 49.59 | 65.49 | 54.22 | 56.43 |
Heron BLIP v1 (620k) | 45.45 | 32.90 | 56.89 | 45.08 |
Heron GIT | 40.98 | 39.87 | 54.59 | 45.15 |
- LLaVA Bench In-the-wild translated into Japanese.
Heron-Bench
Model | Detail | Conv | Complex | Average |
---|---|---|---|---|
llava-calm2-siglip | 53.42 | 50.13 | 52.72 | 52.09 |
Japanese Stable VLM | 25.15 | 51.23 | 37.84 | 38.07 |
SakanaAI EvoVLM-JP | 50.31 | 44.42 | 40.47 | 45.07 |
Heron BLIP v1 (620k) | 49.09 | 41.51 | 45.72 | 45.44 |
Heron GIT | 42.77 | 54.20 | 43.53 | 46.83 |
Use and Limitations
Intended Use
This model is designed for use by the open-source community in vision-language applications and academic research.
Limitations and biases
This model, a general-purpose Japanese VLM, reaches optimal performance when specifically tuned with relevant data for each task. Though technically possible, commercial use is advised with caution, and the implementation of mechanisms to filter out inappropriate content is strongly recommended when deployed in production systems. This model is not advisable for use in applications that could potentially harm individuals or groups, or cause distress. CyberAgent expressly disclaims any liability for direct, indirect, special, incidental, or consequential damages, as well as for any losses that may result from using this model, regardless of the outcomes. Users must fully understand these limitations before employing the model.
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