add blog url

README.md

@@ -4,6 +4,8 @@ ChatCSDN基于RWKV1.5B基模型
 源码来源于：https://github.com/BlinkDL/RWKV-LM/tree/main/RWKV-v4neo
 主要是在RWKV提供的1.5B参数的基础之上，使用CSDN的问答数据和博客数据进行增量预训练，经过指令微调，得到拥有IT行业知识体系的大语言模型。
 原始模型参数地址：https://huggingface.co/BlinkDL/rwkv-4-pile-1b5
+微调后的模型参数地址：https://huggingface.co/zxm2023/ChatCSDN
+介绍博客地址：https://blog.csdn.net/zxm2015/article/details/130227450
 
 ## 预处理数据
 使用项目 https://github.com/EleutherAI/gpt-neox 提供的数据转换工具将.jsonl文件转换为.bin和.idx文件，目前已经将代码全部移植过来，放在tools文件夹中。词典使用的是20B_tokenizer.json。

img_demoAE.py

-########################################################################################################
-# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
-########################################################################################################
-
-import torch, types, os
-import numpy as np
-from PIL import Image
-import torch.nn as nn
-from torch.nn import functional as F
-import torchvision as vision
-import torchvision.transforms as transforms
-np.set_printoptions(precision=4, suppress=True, linewidth=200)
-print(f'loading...')
-
-########################################################################################################
-
-model_prefix = 'test/image_trained/out-v7c_d8_256-224-13bit-OB32x0.5-201'
-input_img = 'test/img_ae_test/test0.png'
-
-########################################################################################################
-
-class ToBinary(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        return torch.floor(x + 0.5) # no need for noise when we have plenty of data
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        return grad_output.clone() # pass-through
-
-class R_ENCODER(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-        self.args = args
-        dd = 8
-        self.Bxx = nn.BatchNorm2d(dd*64)
-
-        self.CIN = nn.Conv2d(3, dd, kernel_size=3, padding=1)
-        self.Cx0 = nn.Conv2d(dd, 32, kernel_size=3, padding=1)
-        self.Cx1 = nn.Conv2d(32, dd, kernel_size=3, padding=1)
-
-        self.B00 = nn.BatchNorm2d(dd*4)
-        self.C00 = nn.Conv2d(dd*4, 256, kernel_size=3, padding=1)
-        self.C01 = nn.Conv2d(256, dd*4, kernel_size=3, padding=1)
-        self.C02 = nn.Conv2d(dd*4, 256, kernel_size=3, padding=1)
-        self.C03 = nn.Conv2d(256, dd*4, kernel_size=3, padding=1)
-
-        self.B10 = nn.BatchNorm2d(dd*16)
-        self.C10 = nn.Conv2d(dd*16, 256, kernel_size=3, padding=1)
-        self.C11 = nn.Conv2d(256, dd*16, kernel_size=3, padding=1)
-        self.C12 = nn.Conv2d(dd*16, 256, kernel_size=3, padding=1)
-        self.C13 = nn.Conv2d(256, dd*16, kernel_size=3, padding=1)
-
-        self.B20 = nn.BatchNorm2d(dd*64)
-        self.C20 = nn.Conv2d(dd*64, 256, kernel_size=3, padding=1)
-        self.C21 = nn.Conv2d(256, dd*64, kernel_size=3, padding=1)
-        self.C22 = nn.Conv2d(dd*64, 256, kernel_size=3, padding=1)
-        self.C23 = nn.Conv2d(256, dd*64, kernel_size=3, padding=1)
-
-        self.COUT = nn.Conv2d(dd*64, args.my_img_bit, kernel_size=3, padding=1)
-
-    def forward(self, img):
-        ACT = F.mish
-
-        x = self.CIN(img)
-        xx = self.Bxx(F.pixel_unshuffle(x, 8))
-        x = x + self.Cx1(ACT(self.Cx0(x)))
-
-        x = F.pixel_unshuffle(x, 2)
-        x = x + self.C01(ACT(self.C00(ACT(self.B00(x)))))
-        x = x + self.C03(ACT(self.C02(x)))
-
-        x = F.pixel_unshuffle(x, 2)
-        x = x + self.C11(ACT(self.C10(ACT(self.B10(x)))))
-        x = x + self.C13(ACT(self.C12(x)))
-
-        x = F.pixel_unshuffle(x, 2)
-        x = x + self.C21(ACT(self.C20(ACT(self.B20(x)))))
-        x = x + self.C23(ACT(self.C22(x)))
-
-        x = self.COUT(x + xx)
-        return torch.sigmoid(x)
-
-class R_DECODER(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-        self.args = args
-        dd = 8
-        self.CIN = nn.Conv2d(args.my_img_bit, dd*64, kernel_size=3, padding=1)
-
-        self.B00 = nn.BatchNorm2d(dd*64)
-        self.C00 = nn.Conv2d(dd*64, 256, kernel_size=3, padding=1)
-        self.C01 = nn.Conv2d(256, dd*64, kernel_size=3, padding=1)
-        self.C02 = nn.Conv2d(dd*64, 256, kernel_size=3, padding=1)
-        self.C03 = nn.Conv2d(256, dd*64, kernel_size=3, padding=1)
-
-        self.B10 = nn.BatchNorm2d(dd*16)
-        self.C10 = nn.Conv2d(dd*16, 256, kernel_size=3, padding=1)
-        self.C11 = nn.Conv2d(256, dd*16, kernel_size=3, padding=1)
-        self.C12 = nn.Conv2d(dd*16, 256, kernel_size=3, padding=1)
-        self.C13 = nn.Conv2d(256, dd*16, kernel_size=3, padding=1)
-
-        self.B20 = nn.BatchNorm2d(dd*4)
-        self.C20 = nn.Conv2d(dd*4, 256, kernel_size=3, padding=1)
-        self.C21 = nn.Conv2d(256, dd*4, kernel_size=3, padding=1)
-        self.C22 = nn.Conv2d(dd*4, 256, kernel_size=3, padding=1)
-        self.C23 = nn.Conv2d(256, dd*4, kernel_size=3, padding=1)
-
-        self.Cx0 = nn.Conv2d(dd, 32, kernel_size=3, padding=1)
-        self.Cx1 = nn.Conv2d(32, dd, kernel_size=3, padding=1)
-        self.COUT = nn.Conv2d(dd, 3, kernel_size=3, padding=1)
-
-    def forward(self, code):
-        ACT = F.mish
-        x = self.CIN(code)
-
-        x = x + self.C01(ACT(self.C00(ACT(self.B00(x)))))
-        x = x + self.C03(ACT(self.C02(x)))
-        x = F.pixel_shuffle(x, 2)
-
-        x = x + self.C11(ACT(self.C10(ACT(self.B10(x)))))
-        x = x + self.C13(ACT(self.C12(x)))
-        x = F.pixel_shuffle(x, 2)
-
-        x = x + self.C21(ACT(self.C20(ACT(self.B20(x)))))
-        x = x + self.C23(ACT(self.C22(x)))
-        x = F.pixel_shuffle(x, 2)
-
-        x = x + self.Cx1(ACT(self.Cx0(x)))
-        x = self.COUT(x)
-        
-        return torch.sigmoid(x)
-
-########################################################################################################
-
-print(f'building model...')
-args = types.SimpleNamespace()
-args.my_img_bit = 13
-encoder = R_ENCODER(args).eval().cuda()
-decoder = R_DECODER(args).eval().cuda()
-
-zpow = torch.tensor([2**i for i in range(0,13)]).reshape(13,1,1).cuda().long()
-
-encoder.load_state_dict(torch.load(f'{model_prefix}-E.pth'))
-decoder.load_state_dict(torch.load(f'{model_prefix}-D.pth'))
-
-########################################################################################################
-
-print(f'test image...')
-img_transform = transforms.Compose([
-    transforms.PILToTensor(),
-    transforms.ConvertImageDtype(torch.float),
-    transforms.Resize((224, 224))
-])
-
-with torch.no_grad():
-    img = img_transform(Image.open(input_img)).unsqueeze(0).cuda()
-    z = encoder(img)
-    z = ToBinary.apply(z)
-
-    zz = torch.sum(z.squeeze().long() * zpow, dim=0)
-    print(f'Code shape = {zz.shape}\n{zz.cpu().numpy()}\n')
-    
-    out = decoder(z)
-    vision.utils.save_image(out, f"{input_img.split('.')[0]}-out-13bit.jpg")