Remove Inference Engine Remove Large Language Models Remove Neural Network
article thumbnail

Microsoft AI Introduces Activation Steering: A Novel AI Approach to Improving Instruction-Following in Large Language Models

Marktechpost

In recent years, large language models (LLMs) have demonstrated significant progress in various applications, from text generation to question answering. However, one critical area of improvement is ensuring these models accurately follow specific instructions during tasks, such as adjusting format, tone, or content length.

article thumbnail

This AI Paper from Meta AI Highlights the Risks of Using Synthetic Data to Train Large Language Models

Marktechpost

Machine learning focuses on developing models that can learn from large datasets to improve their predictions and decision-making abilities. These models are governed by scaling laws, suggesting that increasing model size and the amount of training data enhances performance. Don’t Forget to join our 50k+ ML SubReddit.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Overcoming Cross-Platform Deployment Hurdles in the Age of AI Processing Units

Unite.AI

They are made up of thousands of small cores that can manage multiple tasks simultaneously, excelling at parallel tasks like matrix operations, making them ideal for neural network training. These specialized hardware components are designed for neural network inference tasks, prioritizing low latency and energy efficiency.

article thumbnail

PyTorch 2.5 Released: Advancing Machine Learning Efficiency and Scalability

Marktechpost

In particular, the release targets bottlenecks experienced in transformer models and LLMs (Large Language Models), the ongoing need for GPU optimizations, and the efficiency of training and inference for both research and production settings. With the latest PyTorch 2.5 Don’t Forget to join our 50k+ ML SubReddit.

article thumbnail

Setting Up a Training, Fine-Tuning, and Inferencing of LLMs with NVIDIA GPUs and CUDA

Unite.AI

According to NVIDIA's benchmarks , TensorRT can provide up to 8x faster inference performance and 5x lower total cost of ownership compared to CPU-based inference for large language models like GPT-3. Accelerating LLM Training with GPUs and CUDA. import torch import torch.nn

article thumbnail

DIFFUSEARCH: Revolutionizing Chess AI with Implicit Search and Discrete Diffusion Modeling

Marktechpost

Large Language Models (LLMs) have gained significant attention in AI research due to their impressive capabilities. Existing methods to address the challenges in AI-powered chess and decision-making systems include neural networks for chess, diffusion models, and world models.

article thumbnail

Model Kinship: The Degree of Similarity or Relatedness between LLMs, Analogous to Biological Evolution

Marktechpost

Large Language Models (LLMs) have gained significant traction in recent years, with fine-tuning pre-trained models for specific tasks becoming a common practice. However, this approach needs help in resource efficiency when deploying separate models for each task. Don’t Forget to join our 50k+ ML SubReddit.