typhoon-ai/typhoon2-qwen2vl-7b-vision-instruct
Typhoon2-Vision
Typhoon2-qwen2vl-7b-vision-instruct is a Thai 🇹🇭 vision-language model designed to support both image and video inputs. While Qwen2-VL is built to handle both image and video processing tasks, Typhoon2-VL is specifically optimized for image-based applications.
For technical-report. please see our arxiv.
Model Description
Here we provide Typhoon2-qwen2vl-7b-vision-instruct which is built upon Qwen2-VL-7B-Instruct.
- Model type: A 7B instruct decoder-only model with vision encoder based on Qwen2 architecture.
- Requirement: transformers 4.38.0 or newer.
- Primary Language(s): Thai 🇹🇭 and English 🇬🇧
- Demo:: https://vision.opentyphoon.ai/
- License: Apache-2.0
Quickstart
Here we show a code snippet to show you how to use the model with transformers.
Before running the snippet, you need to install the following dependencies:
pip install torch transformers accelerate pillowHow to Get Started with the Model
Use the code below to get started with the model. <p align="center"> <img src="https://cdn.pixabay.com/photo/2023/05/16/09/15/bangkok-7997046_1280.jpg" width="80%"/> <p>
Question: ระบุชื่อสถานที่และประเทศของภาพนี้เป็นภาษาไทย Answer: พระบรมมหาราชวัง, กรุงเทพฯ, ประเทศไทย
from PIL import Image
import requests
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
model_name = "scb10x/typhoon2-qwen2vl-7b-vision-instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_name, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_name)
# Image
url = "https://cdn.pixabay.com/photo/2023/05/16/09/15/bangkok-7997046_1280.jpg"
image = Image.open(requests.get(url, stream=True).raw)
conversation = [
{
"role": "user",
"content": [
{
"type": "image",
},
{"type": "text", "text": "ระบุชื่อสถานที่และประเทศของภาพนี้เป็นภาษาไทย"},
],
}
]
# Preprocess the inputs
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(
text=[text_prompt], images=[image], padding=True, return_tensors="pt"
)
inputs = inputs.to("cuda")
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [
output_ids[len(input_ids) :]
for input_ids, output_ids in zip(inputs.input_ids, output_ids)
]
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
print(output_text)
# ['พระบรมมหาราชวัง, กรุงเทพฯ, ประเทศไทย']Processing Multiple Images
from PIL import Image
import requests
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
model_name = "scb10x/typhoon2-qwen2vl-7b-vision-instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_name, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_name)
# Messages containing multiple images and a text query
conversation = [
{
"role": "user",
"content": [
{
"type": "image",
},
{
"type": "image",
},
{"type": "text", "text": "ระบุ 3 สิ่งที่คล้ายกันในสองภาพนี้"},
],
}
]
urls = [
"https://cdn.pixabay.com/photo/2023/05/16/09/15/bangkok-7997046_1280.jpg",
"https://cdn.pixabay.com/photo/2020/08/10/10/09/bangkok-5477405_1280.jpg",
]
images = [Image.open(requests.get(url, stream=True).raw) for url in urls]
# Preprocess the inputs
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(text=[text_prompt], images=images, padding=True, return_tensors="pt")
inputs = inputs.to("cuda")
# Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
# ['1. ทั้งสองภาพแสดงสถาปัตยกรรมที่มีลักษณะคล้ายกัน\n2. ทั้งสองภาพมีสีสันที่สวยงาม\n3. ทั้งสองภาพมีทิวทัศน์ที่สวยงาม']Tips
To balance between performance of the model and the cost of computation, you can set minimum and maximum number of pixels by passing arguments to the processer.
min_pixels = 128 * 28 * 28
max_pixels = 2560 * 28 * 28
processor = AutoProcessor.from_pretrained(
model_name, min_pixels=min_pixels, max_pixels=max_pixels
)Evaluation (Image)
Note: The first value in each cell represents Rouge-L.The second value (after /) represents Accuracy, normalized such that Rouge-L = 100%.
Intended Uses & Limitations
This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.
Follow us
https://twitter.com/opentyphoon
Support
https://discord.gg/us5gAYmrxw
Citation
- If you find Typhoon2 useful for your work, please cite it using:
@misc{typhoon2,
title={Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models},
author={Kunat Pipatanakul and Potsawee Manakul and Natapong Nitarach and Warit Sirichotedumrong and Surapon Nonesung and Teetouch Jaknamon and Parinthapat Pengpun and Pittawat Taveekitworachai and Adisai Na-Thalang and Sittipong Sripaisarnmongkol and Krisanapong Jirayoot and Kasima Tharnpipitchai},
year={2024},
eprint={2412.13702},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.13702},
}