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model_name: Evolutionary Multi-Modal Model
model_type: transformer
license: mit
language: en zh
datasets:
  - "Custom"
tags:
  - text-generation
  - code-generation
  - speech-recognition
  - multi-modal
  - evolutionary
base_model: facebook/bart-base
finetuned_from: gpt2, bert-base-uncased, facebook/wav2vec2-base-960h, openai/clip-vit-base-patch32
dataset: Custom Multi-Modal Dataset

metrics:
  - perplexity
  - bleu
  - wer
  - cer

library_name: transformers
pipeline_tag: text-generation
inference:
  parameters:
    max_length: 50
    top_k: 50
    top_p: 0.95
    temperature: 1.2
    do_sample: true

speech_recognition:
  waveform_path: "C:/Users/baby7/Desktop/权重参数/sample-15s.wav"
  task: "speech_recognition"
  output_audio_key: "Transcription"

text_generation:
  input_text: "What is the future of AI?"
  task: "text_generation"
  output_text_key: "Generated Text"

code_generation:
  input_code: "def add(a, b): return"
  task: "code_generation"
  output_code_key: "Generated Code"

tests:
  - name: speech_recognition_test
    waveform_path: "C:/Users/baby7/Desktop/权重参数/sample-15s.wav"
    expected_output: "Expected transcription"
  - name: text_generation_test
    input_text: "What is the future of AI?"
    expected_output: "Predicted text about AI"
  - name: code_generation_test
    input_code: "def add(a, b): return"
    expected_output: "def add(a, b): return a + b"

extra_info:
  author: zero
  version: 1.0
  description: |
    This Evolutionary Multi-Modal Model is designed for tasks like text generation, code generation, 
    speech recognition, and vision understanding. It leverages the capabilities of multiple pre-trained
    models and applies evolutionary techniques to optimize performance across these tasks.
  citation:
    - |
      @article{your_reference_2025,
        title={Evolutionary Multi-Modal Model for Enhanced Performance},
        author={Your Name},
        journal={Journal of AI Research},
        year={2025}
      }