Tecosys Whitepaper

@Tecosys

Website: Tecosys.in

? Invest in the Future with Tecosys! ? Take 2 minutes to Read ✅*
*Value:

? Intelligent AI models for businesses, Governments and consumers. Our AI Models are 35% cheaper than the lastest openai model gpt-4o and Gemini 1.5 pro. But the quality of our model is better than all. You can test our @cerinabot and then type /chat and then click cerina premium and click bhai_ub (Bhai Unbounded), now ask any questions, it will answer precisely. There are many more features which you can test.

? Advanced AI agents to automate all tasks

Profits Proposition:

? B2B & B2C: API sales and freemium revenue of our models.

? B2G: Customised AI and Computer Vision based Automation, Tracking and monitoring.

? More Detailed Information:

? Freemium structures of our all chatbots services

? Organising Events with AI

? Home and Office Automation with our AI Models

? Selling our Advanced Text to Speech and Voice Cloning Models

? Selling our Advanced Graphics Models like text to image and text to logo

? Selling our Advanced RAG models to the researchers and advocates

? AI Engineer

? AI Doctor (Check Reports, Advise)

Benefits:

? Creativity, communication, and insights.

? We are building our erc20 token, you will get your profit regularly.

? We are also offering partnership.

Call to Action:

? Invest in Tecosys and shape the future of AI.

Email us: [email protected]
Message us: @eviral

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? Cerinabot Update ?

Gpt 4 and DALL E are working fine. Check out the @CerinaBot to use it

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? Thanks to the all subscribers who subscribed our plan ✅

Creating a text-to-image model from scratch is a complex task involving deep learning techniques, typically Generative Adversarial Networks (GANs) or variations such as DALL·E by OpenAI, which was specifically designed for generating images from textual descriptions. For a generic and simplified example, I'll outline how you might start with such a project using PyTorch, a popular machine learning library. This won't be a fully functional model but should give you an idea of the structure and process.

Keep in mind, training a model capable of generating high-quality images from text requires a vast amount of data and substantial computational resources, often beyond the scope of individual or academic efforts.

Requirements

- Python 3.8 or newer
- PyTorch
- torchvision
- A dataset of images with corresponding textual descriptions (e.g., MS COCO or a similar dataset)

Installation of Dependencies

First, install PyTorch and torchvision. It's best to follow the instructions on the official PyTorch website as the commands can vary depending on your system and whether you're using CUDA for GPU acceleration.

```
pip install torch torchvision

```

Code
Because an actual implementation of a text-to-image model is extensive, here’s a very high-level pseudo-code outline to illustrate the basic approach:

```
import torch
from torch import nn
from torchvision.models import resnet50
from torch.utils.data import DataLoader

# Assuming you have a custom dataset class for loading your dataset
from my_dataset import TextImageDataset

class TextToImageModel(nn.Module):
def __init__(self):
super(TextToImageModel, self).__init__()
# Text encoder (e.g., a pre-trained transformer model)
self.text_encoder = MyTextEncoder()
# Image decoder (this can be a part of a GAN or an autoencoder)
self.image_decoder = MyImageDecoder()

def train(model, data_loader, optimizer, criterion, epochs):
model.train()
for epoch in range(epochs):
for text_inputs, true_images in data_loader:
optimizer.zero_grad()
generated_images = model(text_inputs)
loss = criterion(generated_images, true_images)
loss.backward()
optimizer.step()

# Initialize your dataset and dataloader
dataset = TextImageDataset(...) # Implement this class to return text-image pairs
data_loader = DataLoader(dataset, batch_size=32, shuffle=True)

# Initialize the model
model = TextToImageModel()

# Define the optimizer and loss function
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss() # This is a simplification; in practice, you might need a more complex loss function

# Train the model
train(model, data_loader, optimizer, criterion, epochs=10)

```

This pseudo-code is highly simplified. Critical details, such as the implementation of the text encoder and image decoder (plus the GAN structure if you are using one), are complex topics on their own. Training such a model requires extensive computational resources, especially when dealing with large datasets and intricate neural network architectures.

I recommend studying existing text-to-image architectures like DALL·E, AttnGAN, and others to understand the depth of the subject better. Tackling such a project would also benefit from a solid foundation in deep learning principles and familiarity with the latest research in the field.

PyTorch

PyTorch

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Update ?

Did you know that @Cerinabot can now store your chat history when you use the BHAI AI or BHAI AI (UB) models? ?

This means you can have continuous conversations and build upon previous interactions. ?

To use this feature, simply tag a previous post and ask the bot a question or make a request. The bot will be able to respond based on the context of the conversation and update its response accordingly. ?

Enjoy more contextual and personalized conversations with Cerinabot! ?

N.B - It will work until you stop the bot or use another model.

# ChatHistory # BHAIAI

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2️⃣ Select "/chat" from the menu.
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Emoji-based message:

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