GitCode AI Community Documentation
Models

Model Usage Guide

GitCode AI community provides rich model resources that you can easily create, search, download, and use. This guide will help you understand how to perform model-related operations on the platform.

Model Creation

Creating New Models

  1. Log into your GitCode AI account
  2. Click “Model Center” > “Create Model” in the navigation bar
  3. Fill in basic model information:
    • Model name
    • Model description
    • Tags
    • License
  4. Select model type:
    • PyTorch
    • TensorFlow
    • ONNX
    • Other frameworks
  5. Upload model files
  6. Provide example code (optional)
  7. Click “Create” to complete

[Image: Model creation page screenshot]

Model Configuration Files

Each model requires a model-config.yaml configuration file. Example:

model-name: my-awesome-model
version: 1.0.0
framework: pytorch
task: image-classification
dependencies:
  - torch>=2.0.0
  - transformers>=4.30.0
  1. Enter keywords in the search box
  2. Use filters to narrow down results:
    • Task type
    • Framework
    • License
    • Download count
    • Update time

Supports the following advanced search syntax:

  • framework:pytorch - Search by framework
  • task:nlp - Search by task type
  • stars:>100 - Search by star count
  • language:python - Search by programming language

Model Download

Download via Web Interface

  1. Go to the model details page
  2. Click the “Download” button
  3. Select version and format

Download via Command Line

# Install GitCode CLI
pip install gitcode

# Download model
gitcode download username/model-name

# Download specific version
gitcode download username/model-name --version v1.0.0

Model Usage

Python Code Examples

from gitcode_hub import load_model

# Load model
model = load_model("username/model-name")

# Use model for inference
result = model.predict(input_data)

API Call Examples

import requests

API_URL = "https://api.gitcode.com/v1/models/username/model-name"
headers = {"Authorization": f"Bearer {API_TOKEN}"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# Send inference request
output = query({
    "inputs": "Hello, world!",
})

Best Practices

  1. Version Control

    • Use semantic versioning
    • Maintain backward compatibility
    • Record version update logs

  2. Documentation

    • Provide detailed model descriptions
    • Include usage examples
    • Explain model limitations and considerations

  3. Performance Optimization

    • Provide quantized model versions
    • Support batch inference
    • Optimize inference speed

  4. Security

    • Conduct model security testing
    • Provide model cards explaining potential risks
    • Comply with data privacy requirements

Common Questions

Q: How to update a published model? A: You can publish new versions through version management features, or update files for existing versions.

Q: What formats does the model support? A: Supports mainstream deep learning framework formats, including PyTorch, TensorFlow, ONNX, etc.

Q: How to handle model dependencies? A: Declare dependencies in model-config.yaml, or provide a requirements.txt file.