zeroMN
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SHMT

@@ -31,197 +31,103 @@ model-index:
 pipeline_tag: text2text-generation
 ---
-# Model Card for Model ID
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-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-## Model Details
-### Model Description
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-## How to Get Started with the Model
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-## Training Details
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-#### Training Hyperparameters
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 pipeline_tag: text2text-generation
 ---
+Model Name: Evolutionary Multi-Modal Model
+Model Type: Transformer
+License: MIT
+Language: English, Chinese
+Datasets: Custom
+Tags:
+Text Generation
+Code Generation
+Speech Recognition
+Multi-Modal
+Evolutionary
+Base Model: Facebook/BART-Base
+Finetuned From: GPT-2, 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 Information
+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.