POINTS-Seeker-8B 是一款先进的多模态智能体搜索模型，它从零开始构建，旨在突破大型多模态模型（LMM）中静态参数化知识的认知局限。不同于在现有LMM基础上简单集成搜索工具的方式，POINTS-Seeker 采用智能体种子训练（Agentic Seeding） 进行原生训练——这是一个专门的训练阶段，用于植入智能体行为的基础前提——并配备了V-Fold自适应历史感知压缩机制，有效解决了长序列交互中的性能瓶颈问题。POINTS-Seeker-8B 在长序列、知识密集型视觉推理任务上展现出卓越性能。
请先通过以下命令安装 WePOINTS：
git clone https://github.com/WePOINTS/WePOINTS.git
cd ./WePOINTS
pip install -e .from transformers import AutoModelForCausalLM, AutoTokenizer, Qwen2VLImageProcessor
import torch
user_prompt = "explain the image" # replace with your instruction
image_path = 'your image path'
model_path = 'tencent/POINTS-Seeker'
model = AutoModelForCausalLM.from_pretrained(model_path,
trust_remote_code=True,
dtype=torch.bfloat16,
device_map='cuda')
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
image_processor = Qwen2VLImageProcessor.from_pretrained(model_path)
content = [
dict(type='image', image=image_path),
dict(type='text', text=user_prompt)
]
messages = [
{
'role': 'user',
'content': content
}
]
generation_config = {
'max_new_tokens': 2048,
'do_sample': False
}
response = model.chat(
messages,
tokenizer,
image_processor,
generation_config
)
print(response)
请参考我们的 github 仓库