Audio-Text-to-Text
Transformers
English
Chinese
transformer
multimodal
vqa
text
audio
Eval Results (legacy)
Instructions to use zeroMN/SHMT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeroMN/SHMT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zeroMN/SHMT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| def mock_text_generation(input_text): | |
| # 模拟文本生成逻辑 | |
| if input_text == "Tell me a joke.": | |
| return "Why don't scientists trust atoms? Because they make up everything!" | |
| return "I can come up with many ideas, but that request has stumped me!" | |
| def mock_code_generation(input_code): | |
| # 模拟代码生成逻辑 | |
| if input_code == "def greet(name):": | |
| return "def greet(name):\n return f'Hello, {name}!'" | |
| return "Hmm, I'm not sure how to complete that one." | |
| # 测试文本生成功能 | |
| input_text = "Tell me a joke." | |
| expected_output_text = "Why don't scientists trust atoms? Because they make up everything!" | |
| generated_text = mock_text_generation(input_text) | |
| assert generated_text == expected_output_text, f"Text generation test failed: expected {expected_output_text}, got {generated_text}" | |
| print("Text generation test passed") | |
| # 测试代码生成功能 | |
| input_code = "def greet(name):" | |
| expected_output_code = "def greet(name):\n return f'Hello, {name}!'" | |
| generated_code = mock_code_generation(input_code) | |
| assert generated_code == expected_output_code, f"Code generation test failed: expected {expected_output_code}, got {generated_code}" | |
| print("Code generation test passed") | |