zeroMN/auto

3. 提供可下载文件

确保以下文件已上传到仓库，便于用户下载和运行： - 模型权重文件（如 AutoModel.pth）。 - 配置文件（如 config.json）。 - 依赖文件（如 requirements.txt）。 - 运行脚本（如 run_model.py）。

用户可以直接下载这些文件并运行模型。

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4. 自动运行模型的限制

Hugging Face Hub 本身不能自动运行上传的模型，但通过 Spaces 提供的接口可以解决这一问题。Spaces 能够运行托管的推理服务，让用户无需本地配置即可测试模型。

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推荐方法

• 快速测试：使用 Hugging Face Spaces 创建在线演示。
• 高级使用：在模型卡中提供完整的运行说明，允许用户本地运行模型。

通过这些方式，您可以让模型仓库既支持在线运行，也便于用户离线部署。

Uses

import torch
import torch.nn as nn
import torch.nn.functional as F
import os
# 配置类定义
class Config:
    def __init__(self):
        # 模型架构参数
        self.hidden_size = 768
        self.num_attention_heads = 12
        self.num_hidden_layers = 12
        self.intermediate_size = 3072
        self.hidden_dropout_prob = 0.1
        self.attention_probs_dropout_prob = 0.1

        # 图像相关
        self.image_size = 224
        self.image_channels = 3
        self.patch_size = 16

        # 文本相关
        self.max_position_embeddings = 512
        self.vocab_size = 30522
        self.type_vocab_size = 2

        # 语音相关
        self.audio_sample_rate = 16000
        self.audio_frame_size = 1024
        self.audio_hop_size = 512

        # 任务相关
        self.enable_vqa = True
        self.enable_caption = True
        self.enable_retrieval = True
        self.enable_asr = True  # 语音识别
        self.enable_realtime_asr = True  # 实时语音识别

        # 训练相关
        self.batch_size = 32
        self.learning_rate = 1e-4
        self.weight_decay = 0.01
        self.warmup_steps = 10000
        self.max_steps = 100000

# 模型相关类定义
class ImageEncoder(nn.Module):
    def __init__(self, config):
        super(ImageEncoder, self).__init__()
        self.config = config
        self.encoder_layer = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Flatten(),
            nn.Linear(64 * 111 * 111, config.hidden_size)
        )

    def forward(self, image):
        image_features = self.encoder_layer(image)
        return image_features

class TextEncoder(nn.Module):
    def __init__(self, config):
        super(TextEncoder, self).__init__()
        self.config = config
        self.transformer_layer = nn.TransformerEncoderLayer(
            d_model=config.hidden_size, 
            nhead=config.num_attention_heads,
            batch_first=True
        )
        self.transformer_encoder = nn.TransformerEncoder(
            self.transformer_layer, 
            num_layers=config.num_hidden_layers
        )

    def forward(self, text):
        text_features = self.transformer_encoder(text).mean(dim=1)
        return text_features

class AudioEncoder(nn.Module):
    def __init__(self, config):
        super(AudioEncoder, self).__init__()
        self.config = config
        self.encoder_layer = nn.Sequential(
            nn.Linear(config.audio_sample_rate, config.hidden_size),
            nn.ReLU(),
            nn.Linear(config.hidden_size, config.hidden_size)
        )

    def forward(self, audio):
        audio_features = self.encoder_layer(audio)
        return audio_features

class FusionLayer(nn.Module):
    def __init__(self, config):
        super(FusionLayer, self).__init__()
        self.config = config
        self.fusion_layer = nn.Linear(config.hidden_size * 3, config.hidden_size)

    def forward(self, image_features, text_features, audio_features):
        fused_features = torch.cat((image_features, text_features, audio_features), dim=1)
        fused_features = self.fusion_layer(fused_features)
        return fused_features

class VQALayer(nn.Module):
    def __init__(self, config):
        super(VQALayer, self).__init__()
        self.config = config
        self.vqa_layer = nn.Linear(config.hidden_size, config.vocab_size)

    def forward(self, fused_features):
        vqa_output = self.vqa_layer(fused_features)
        return vqa_output

class CaptionLayer(nn.Module):
    def __init__(self, config):
        super(CaptionLayer, self).__init__()
        self.config = config
        self.caption_layer = nn.Linear(config.hidden_size, config.vocab_size)

    def forward(self, fused_features):
        caption_output = self.caption_layer(fused_features)
        return caption_output

class RetrievalLayer(nn.Module):
    def __init__(self, config):
        super(RetrievalLayer, self).__init__()
        self.config = config
        self.retrieval_layer = nn.Linear(config.hidden_size, config.vocab_size)

    def forward(self, fused_features):
        retrieval_output = self.retrieval_layer(fused_features)
        return retrieval_output

class ASRLayer(nn.Module):
    def __init__(self, config):
        super(ASRLayer, self).__init__()
        self.config = config
        self.asr_layer = nn.Linear(config.hidden_size, config.vocab_size)

    def forward(self, fused_features):
        asr_output = self.asr_layer(fused_features)
        return asr_output

class RealtimeASRLayer(nn.Module):
    def __init__(self, config):
        super(RealtimeASRLayer, self).__init__()
        self.config = config
        self.realtime_asr_layer = nn.Linear(config.hidden_size, config.vocab_size)

    def forward(self, fused_features):
        realtime_asr_output = self.realtime_asr_layer(fused_features)
        return realtime_asr_output

class TextOutputLayer(nn.Module):
    def __init__(self, config):
        super(TextOutputLayer, self).__init__()
        self.config = config
        self.text_output_layer = nn.Linear(config.hidden_size, config.vocab_size)

    def forward(self, fused_features):
        text_output = self.text_output_layer(fused_features)
        return text_output

# 主模型定义
class AutoModel(nn.Module):
    def __init__(self, config):
        super(AutoModel, self).__init__()
        self.config = config
        self.image_encoder = ImageEncoder(config)
        self.text_encoder = TextEncoder(config)
        self.audio_encoder = AudioEncoder(config)
        self.fusion_layer = FusionLayer(config)
        self.vqa_layer = VQALayer(config)
        self.caption_layer = CaptionLayer(config)
        self.retrieval_layer = RetrievalLayer(config)
        self.asr_layer = ASRLayer(config)
        self.realtime_asr_layer = RealtimeASRLayer(config)
        self.text_output_layer = TextOutputLayer(config)

    def forward(self, image, text, audio):
        image_features = self.image_encoder(image)
        text_features = self.text_encoder(text)
        audio_features = self.audio_encoder(audio)
        fused_features = self.fusion_layer(image_features, text_features, audio_features)
        vqa_output = self.vqa_layer(fused_features)
        caption_output = self.caption_layer(fused_features)
        retrieval_output = self.retrieval_layer(fused_features)
        asr_output = self.asr_layer(fused_features)
        realtime_asr_output = self.realtime_asr_layer(fused_features)
        text_output = self.text_output_layer(fused_features)
        return vqa_output, caption_output, retrieval_output, asr_output, realtime_asr_output, text_output

# 测试代码
config = Config()
model = AutoModel(config)
image = torch.randn(1, 3, 224, 224)
text = torch.randn(1, config.max_position_embeddings, config.hidden_size)
audio = torch.randn(1, config.audio_sample_rate)
vqa_output, caption_output, retrieval_output, asr_output, realtime_asr_output, text_output = model(image, text, audio)

# 输出结果
print("VQA output shape:", vqa_output.shape)
print("Caption output shape:", caption_output.shape)
print("Retrieval output shape:", retrieval_output.shape)
print("ASR output shape:", asr_output.shape)
print("Realtime ASR output shape:", realtime_asr_output.shape)
print("Text output shape:", text_output.shape)

# 打印总参数数量
total_params = sum(p.numel() for p in model.parameters())
print(f"\n总参数数量: {total_params}")



# 保存模型权重
save_path = "save.pth"
torch.save(model.state_dict(), save_path)
print(f"模型权重已保存到: {save_path}")

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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