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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ sample-15s.wav filter=lfs diff=lfs merge=lfs -text
.yml ADDED
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+ speech_recognition:
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+ waveform_path: "C:/Users/baby7/Desktop/multi_modal_model/sample-15s.wav"
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+ task: "speech_recognition"
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+ output_audio_key: "Transcription"
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+
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+ tests:
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+ - name: speech_recognition_test
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+ waveform_path: "C:/Users/baby7/Desktop/multi_modal_model/sample-15s.wav"
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+ expected_output: "Expected transcription"
SJMT_model.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ size 2493181678
app.py ADDED
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+ def mock_text_generation(input_text):
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+ # 模拟文本生成逻辑
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+ if input_text == "Tell me a joke.":
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+ return "Why don't scientists trust atoms? Because they make up everything!"
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+ return "I can come up with many ideas, but that request has stumped me!"
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+
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+ def mock_code_generation(input_code):
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+ # 模拟代码生成逻辑
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+ if input_code == "def greet(name):":
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+ return "def greet(name):\n return f'Hello, {name}!'"
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+ return "Hmm, I'm not sure how to complete that one."
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+
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+ # 测试文本生成功能
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+ input_text = "Tell me a joke."
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+ expected_output_text = "Why don't scientists trust atoms? Because they make up everything!"
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+ generated_text = mock_text_generation(input_text)
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+ assert generated_text == expected_output_text, f"Text generation test failed: expected {expected_output_text}, got {generated_text}"
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+ print("Text generation test passed")
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+
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+ # 测试代码生成功能
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+ input_code = "def greet(name):"
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+ expected_output_code = "def greet(name):\n return f'Hello, {name}!'"
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+ generated_code = mock_code_generation(input_code)
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+ assert generated_code == expected_output_code, f"Code generation test failed: expected {expected_output_code}, got {generated_code}"
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+ print("Code generation test passed")
code_generator/merges.txt ADDED
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+ {
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+ "bos_token": "<|endoftext|>",
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+ "eos_token": "<|endoftext|>",
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+ }
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code_generator/tokenizer_config.json ADDED
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+ }
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+ },
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+ "bos_token": "<|endoftext|>",
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+ "clean_up_tokenization_spaces": false,
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+ "eos_token": "<|endoftext|>",
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+ "extra_special_tokens": {},
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+ "tokenizer_class": "GPT2Tokenizer",
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+ "unk_token": "<|endoftext|>"
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+ }
code_generator/vocab.json ADDED
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config.json ADDED
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+ {
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+ "model_name": "Evolutionary Multi-Modal Model",
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+ "model_type": "transformer",
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+ "license": "mit",
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+ "language": "en",
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+ "datasets": [
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+ "Custom"
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+ ],
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+ "tags": [
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+ "text-generation",
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+ "code-generation",
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+ "speech-recognition",
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+ "multi-modal",
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+ "evolutionary"
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+ ],
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+ "base_model": "facebook/bart-base",
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+ "finetuned_from": [
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+ "gpt2",
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+ "bert-base-uncased",
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+ "facebook/wav2vec2-base-960h",
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+ "openai/clip-vit-base-patch32"
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+ ],
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+ "dataset": "Custom Multi-Modal Dataset",
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+ "metrics": [
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+ "perplexity",
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+ "bleu",
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+ "wer",
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+ "cer"
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+ ],
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+ "library_name": "transformers",
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+ "pipeline_tag": "text-generation",
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+ "inference": {
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+ "parameters": {
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+ "max_length": 50,
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+ "top_k": 50,
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+ "top_p": 0.95,
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+ "temperature": 1.2,
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+ "do_sample": true
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+ }
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+ },
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+ "extra_info": {
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+ "author": "zero",
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+ "version": "1.0",
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+ "description":"1.0",
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+ "citation": "@1.0"
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+ }
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+ }
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+
config.yml ADDED
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+ model_name: Evolutionary Multi-Modal Model
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+ model_type: transformer
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+ license: mit
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+ language: en zh
5
+ datasets:
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+ - "Custom"
7
+ tags:
8
+ - text-generation
9
+ - code-generation
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+ - speech-recognition
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+ - multi-modal
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+ - evolutionary
13
+ base_model: facebook/bart-base
14
+ finetuned_from: gpt2, bert-base-uncased, facebook/wav2vec2-base-960h, openai/clip-vit-base-patch32
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+ dataset: Custom Multi-Modal Dataset
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+
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+ metrics:
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+ - perplexity
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+ - bleu
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+ - wer
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+ - cer
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+
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ inference:
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+ parameters:
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+ max_length: 50
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+ top_k: 50
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+ top_p: 0.95
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+ temperature: 1.2
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+ do_sample: true
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+
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+ speech_recognition:
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+ waveform_path: "C:/Users/baby7/Desktop/权重参数/sample-15s.wav"
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+ task: "speech_recognition"
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+ output_audio_key: "Transcription"
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+
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+ text_generation:
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+ input_text: "What is the future of AI?"
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+ task: "text_generation"
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+ output_text_key: "Generated Text"
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+
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+ code_generation:
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+ input_code: "def add(a, b): return"
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+ task: "code_generation"
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+ output_code_key: "Generated Code"
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+
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+ tests:
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+ - name: speech_recognition_test
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+ waveform_path: "C:/Users/baby7/Desktop/权重参数/sample-15s.wav"
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+ expected_output: "Expected transcription"
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+ - name: text_generation_test
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+ input_text: "What is the future of AI?"
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+ expected_output: "Predicted text about AI"
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+ - name: code_generation_test
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+ input_code: "def add(a, b): return"
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+ expected_output: "def add(a, b): return a + b"
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+
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+ extra_info:
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+ author: zero
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+ version: 1.0
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+ description: |
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+ This Evolutionary Multi-Modal Model is designed for tasks like text generation, code generation,
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+ speech recognition, and vision understanding. It leverages the capabilities of multiple pre-trained
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+ models and applies evolutionary techniques to optimize performance across these tasks.
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+ citation:
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+ - |
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+ @article{your_reference_2025,
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+ title={Evolutionary Multi-Modal Model for Enhanced Performance},
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+ author={Your Name},
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+ journal={Journal of AI Research},
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+ year={2025}
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+ }
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vision_encoder/vocab.json ADDED
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