InternVL2_5-26B

InternVL2.5-26B is a powerful multi-modal large model, specially designed for processing visual and language tasks, with excellent visual understanding, text generation and multi-modal reasoning capabilities. Here is its core message:

Core features

1. Model architecture

• Based on the 26B parameter scale multi-modal Transformer architecture, combined with advanced visual and language feature representation technology, it supports efficient processing of images, text and multi-modal input.

2. multimodal capabilities

• Supports complex visual tasks (such as image classification, object detection) and language tasks (such as text generation, semantic understanding).

• Excellent performance in multi-modal reasoning, capable of processing contextual information combining images and text.

3. training data

• Use large-scale multi-modal data sets for pre-training, covering rich visual and language scenarios to ensure generalization capabilities.

4. Application scenarios

• It is suitable for cross-modal question and answer, image and text generation, image subtitle generation and other scenarios, and is especially suitable for tasks that require high-precision multi-modal understanding.

Deployment requirements

• Python version : 3.9 or above.

• Supported framework : PyTorch 2.0 or higher, compatible with mainstream tools such as Hugging Face.

• Hardware recommendation : Supports multiple GPUs (such as A100 or H100) or TPU for efficient inference and training.

Quick to use

Use Hugging Face's transformers library to quickly load the model sample code:

 from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "InternVL/InternVL2_5-26B"

model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

#Example input input_text = "Describe the objects in the image."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0]))

Performance advantages

• Cross-modal question answering : Accurately understand the semantic relationship between images and text.

• Image and text generation : High-quality generation of descriptive and creative text.

• Task versatility : Strong performance in single-modal and multi-modal tasks.

For more information, please visit the official resources or the Hugging Face page to explore the potential of the model in multi-modal AI tasks.