Update README.md

README.md

@@ -69,6 +69,8 @@ This model, named `Evolutionary Multi-Modal Model`, is a multimodal transformer
 
 ### Direct Use
 ```python
+git lfs install
+
 git clone https://huggingface.co/zeroMN/SHMT.git
 ```
 ### Downstream Use
@@ -90,5 +92,74 @@ Users (both direct and downstream) should be made aware of the following risks,
 
 ## How to Get Started with the Model
 ```python
-git clone https://huggingface.co/zeroMN/SHMT.git
+import os
+import torch
+import torch.nn as nn
+import numpy as np
+import random
+from transformers import (
+    BartForConditionalGeneration, 
+    AutoModelForCausalLM, 
+    BertModel, 
+    Wav2Vec2Model,
+    CLIPModel,
+    AutoTokenizer
+)
+
+class MultiModalModel(nn.Module):
+    def __init__(self):
+        super(MultiModalModel, self).__init__()
+        # 初始化子模型
+        self.text_generator = BartForConditionalGeneration.from_pretrained('facebook/bart-base')
+        self.code_generator = AutoModelForCausalLM.from_pretrained('gpt2')
+        self.nlp_encoder = BertModel.from_pretrained('bert-base-uncased')
+        self.speech_encoder = Wav2Vec2Model.from_pretrained('facebook/wav2vec2-base-960h')
+        self.vision_encoder = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
+
+        # 初始化分词器和处理器
+        self.text_tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')
+        self.code_tokenizer = AutoTokenizer.from_pretrained('gpt2')
+        self.nlp_tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+        self.speech_processor = AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h')
+        self.vision_processor = AutoTokenizer.from_pretrained('openai/clip-vit-base-patch32')
+
+    def forward(self, task, inputs):
+        if task == 'text_generation':
+            attention_mask = inputs.get('attention_mask')
+            outputs = self.text_generator.generate(
+                inputs['input_ids'], 
+                max_new_tokens=100,  
+                pad_token_id=self.text_tokenizer.eos_token_id, 
+                attention_mask=attention_mask,
+                top_p=0.9,  
+                top_k=50,  
+                temperature=0.8,  
+                do_sample=True
+            )
+            return self.text_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        elif task == 'code_generation':
+            attention_mask = inputs.get('attention_mask')
+            outputs = self.code_generator.generate(
+                inputs['input_ids'], 
+                max_new_tokens=50,  
+                pad_token_id=self.code_tokenizer.eos_token_id, 
+                attention_mask=attention_mask,
+                top_p=0.95,  
+                top_k=50,  
+                temperature=1.2,  
+                do_sample=True
+            )
+            return self.code_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        # 添加其他任务的逻辑...
+
+# 计算模型参数数量的函数
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+# 初始化模型
+model = MultiModalModel()
+
+# 计算并打印模型参数数量
+total_params = count_parameters(model)
+print(f"模型总参数数量: {total_params}")
 ```