Principle:OpenGVLab InternVL Model Export

Knowledge Sources	InternVL HuggingFace Model Sharing
Domains	Model_Deployment, Training
Last Updated	2026-02-07 00:00 GMT

Overview

A model serialization strategy that saves trained model weights, configuration, and tokenizer files in HuggingFace-compatible format for sharing and deployment.

Description

After training, the model must be saved in a format that can be loaded for inference, further fine-tuning, or sharing. The export process:

• Saves model weights (safetensors or pytorch_model.bin)
• Saves the model configuration (config.json)
• Saves the tokenizer files
• Handles DeepSpeed checkpoint consolidation when using multi-GPU training

Usage

Use model export at the end of any training workflow to persist the trained model. The HuggingFace Trainer handles this automatically via trainer.save_model().

Theoretical Basis

Model export follows the HuggingFace serialization protocol:

# Pseudo-code: Model export
def save_model(output_dir):
    # 1. Save model weights
    model.save_pretrained(output_dir)  # Creates model.safetensors + config.json

    # 2. Save tokenizer
    tokenizer.save_pretrained(output_dir)  # Creates tokenizer files

    # 3. DeepSpeed: consolidate sharded checkpoints
    if using_deepspeed:
        deepspeed.zero.consolidate_fp32_weights(output_dir)

Related Pages

Implemented By

• Implementation:OpenGVLab_InternVL_Trainer_Save_Model