ViFortune-AI/VOVis2.5-2B-pt

Ovis2.5-2B-Pretrained (Qwen3-1.7B + SigLIP2) - Final Version For Pretraining

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Ovis2.5-2B-Pretrained (Qwen3-1.7B + SigLIP2)

Ovis2.5-2B-Pretrained is a merged version combining:

• Vision Encoder: siglip2-so400m-patch16-512 (from Ovis2.5)
• Language Model (LLM): Qwen3-1.7B (lightweight, efficient, supports Vietnamese)

Note: This is a base/pretrained model, only merged weights, not instruction-tuned. For best conversational performance, further fine-tuning is required.

Architecture Details

Ovis MLLM	Vision Encoder	Language Model (LLM)	Status
VOvis2.5-2B-Pretrained(Final Version)	siglip2-so400m-patch16-512	Qwen3-1.7B	Base PT Model (Needs SFT)
Ovis2.5-2B (Official)	siglip2-so400m-patch16-512	Qwen3-1.7B	Instruction-Tuned
Ovis2.5-9B (Official)	siglip2-so400m-patch16-512	Qwen3-8B	Instruction-Tuned

Supported languages: Vietnamese 🇻🇳, English, Chinese

🚀 Quick Start

Cài đặt

pip install torch==2.8.0 transformers==4.51.3 numpy==1.26.4
pip install flash-attn==2.7.4.post1 --no-build-isolation

Quick Start

import torch
from PIL import Image
from transformers import AutoModelForCausalLM
import requests

model = AutoModelForCausalLM.from_pretrained(
    "AIDC-AI/VOvis2.5-2B-pt",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
).cuda()

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": Image.open(requests.get("https://cdn-uploads.huggingface.co/production/uploads/658a8a837959448ef5500ce5/TIlymOb86R6_Mez3bpmcB.png", stream=True).raw)},
        {"type": "text", "text": "Describe the image in detail."},
    ],
}]

input_ids, pixel_values, grid_thws = model.preprocess_inputs(
    messages=messages,
    add_generation_prompt=True,
    enable_thinking=True
)
input_ids = input_ids.cuda()
pixel_values = pixel_values.cuda() if pixel_values is not None else None
grid_thws = grid_thws.cuda() if grid_thws is not None else None

outputs = model.generate(
    inputs=input_ids,
    pixel_values=pixel_values,
    grid_thws=grid_thws,
    enable_thinking=True,
    enable_thinking_budget=True,
    max_new_tokens=3072,
    thinking_budget=1024,
)

response = model.text_tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)