IN TODAY'S SIGNAL |
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📰 Top News
📌 Latitude
⚡️ Top 5 Signals
🛠️ Top of HuggingFace
🧠 Pytorch Tip
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Read Time: 3 min 42 sec |
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TOP NEWS |
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Lecture Series |
Microsoft Releases Updated 'Generative AI for Beginners' 18-Lesson Course |
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☆ 47,192 Stars |
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What's New |
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Microsoft just released the V2 of their famous (47,000+ Github stars) tutorial series "Generative AI for Beginners".
The open Github repo contains 18 Lessons teaching everything you need to know to start building Generative AI applications.
Each lesson includes
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A short video introduction to the topic
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A written lesson located in the README
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Python and TypeScript code samples supporting Azure OpenAI and OpenAI API
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Links to extra resources to continue your learning
Key Topics Covered
- Fundamentals of Generative AI: Introduction to basic concepts and functionalities of large language models.
- Prompt Engineering: Techniques for optimizing interaction with AI models.
- Application Development: Step-by-step guides to creating text generation, chat integration, and image generation applications.
- Vector Databases for Search Apps: Using embeddings to enhance search functionalities.
- Security and Ethical Use: Strategies to ensure the responsible deployment and security of AI applications.
- Advanced Coding Samples: Additional resources for more experienced developers seeking to expand their skills.
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| CHECK THE COURSE |
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Launchpad: A Revamped Container Experience for AI Engineers |
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Launchpad is Latitude.sh's purpose-built platform for AI applications, a fast solution that lets you run everything from small databases to the largest AI models.
The platform has been constantly evolving based on the feedback from the Machine-Learning community, and now has many new features available to improve your container experience:
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Different GPUs: NVIDIA's L40S (48 GB vRAM) and H100 (80 GB vRAM)
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SSH support: SSH access for debugging and development
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Filesystem volumes: Add persistent storage to multiple containers
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Per-minute billing: Pay only for what you use
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Blueprint library: Instantly launch containers from a community-built library
Create your free account today and deploy container-based applications on dedicated GPUs and CPUs in just a few seconds! |
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partner with us |
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TRENDING SIGNALS |
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Text-to-Video |
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⇧ 5622 Likes |
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Image Generation |
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⇧ 1474 Likes |
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Optimization |
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⇧ 1102 Likes |
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AI Coding |
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⇧ 402 Likes |
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Image Generation |
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⇧ 1248 Likes |
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TOP OF HUGGINGFACE |
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Models |
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stable-diffusion-3-medium: a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
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Qwen2-72B-Instruct : a 72 billion parameter open-source model, excels in language tasks and supports 131,072 token inputs. Uses Transformers >=4.37.0 and YARN for extended texts. Outperforms predecessors in benchmarks across multiple domains.
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fineweb-edu-classifier: This is a classifier for judging the educational value of web pages. It was developed to filter and curate educational content from web datasets and was trained on 450k annotations generated by LLama3-70B-instruct for web samples from FineWeb dataset.
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PYTORCH TIP |
Achieve 4x Larger Effective Batch Size with Gradient Accumulation in PyTorch |
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Gradient Accumulation allows you to effectively increase the batch size without needing more GPU memory, which can be particularly useful when training large models on limited hardware.
Here’s how to implement Gradient Accumulation in PyTorch:
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Set Accumulation Steps: Decide how many batches you want to accumulate before updating the model parameters.
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Modify Training Loop: Accumulate the gradients over several mini-batches and update the model weights after the specified number of accumulation steps.
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import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
# Dummy dataset
x, y = torch.randn(1000, 10), torch.randn(1000, 1)
train_loader = DataLoader(TensorDataset(x, y),
batch_size=32, shuffle=True)
# Simple model
model = nn.Linear(10, 1)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
accumulation_steps = 4
for epoch in range(5):
for i, (inputs, labels) in enumerate(train_loader):
loss = criterion(model(inputs), labels) / accumulation_steps
loss.backward()
if (i + 1) % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
print(f"Epoch {epoch+1}, Loss: {loss.item()}")
print("Training completed")
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By implementing Gradient Accumulation, you can train with larger effective batch sizes, potentially improving model performance and stability without requiring additional GPU memory. |
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