Update README.md

README.md

@@ -118,74 +118,13 @@ Users (both direct and downstream) should be made aware of the following risks,
 
 ## How to Get Started with the Model
 ```python
-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}")
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="zeroMN/SHMT")
+```
+```python
+# Load model directly
+from transformers import AutoModel
+model = AutoModel.from_pretrained("zeroMN/SHMT")
 ```