Tags: large language models*

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  1. A new test-time scaling method called budget forcing boosts LLM reasoning without increasing model size, outperforming OpenAI's o1-preview.

    This method, developed by researchers at Stanford University, controls the computational effort an LLM expends during inference, allowing it to either stop reasoning early or think longer. The researchers created a curated dataset called s1K to test this method and found that their model, s1-32B, outperformed OpenAI’s o1-preview model on competitive math benchmarks by up to 27%.

  2. As generative AI reshapes software development, natural language commands are replacing traditional programming syntax, but experts question if English can ever match the precision of code.

    2025-02-14 Tags: , , by klotz
  3. Learn how to use Okta FGA to secure your LangChain RAG agent in Python.

    2025-02-14 Tags: , , , , by klotz
  4. Build Agentic AI with NVIDIA NIM and NeMo. Explore optimized AI models, connect AI agents to data, and deploy anywhere with NVIDIA NIM microservices.

    2025-02-14 Tags: , , , , , by klotz
  5. Harbor is a containerized LLM toolkit that allows you to run LLMs and additional services with ease, featuring a CLI and a companion App for managing AI services.

    2025-02-14 Tags: , , , , by klotz
  6. The TC specifies a common protocol, framework and interfaces for interactions between AI agents using natural language while supporting multiple modalities.

    The This framework will also facilitate communication between non-AI systems (e.g., clients on phones) and AI agents, as well as interactions between multiple AI agents.

  7. A comprehensive guide to Large Language Models by Damien Benveniste, covering various aspects from transformer architectures to deploying LLMs.

    • Language Models Before Transformers
    • Attention Is All You Need: The Original Transformer Architecture
    • A More Modern Approach To The Transformer Architecture
    • Multi-modal Large Language Models
    • Transformers Beyond Language Models
    • Non-Transformer Language Models
    • How LLMs Generate Text
    • From Words To Tokens
    • Training LLMs to Follow Instructions
    • Scaling Model Training
    • Fine-Tuning LLMs
    • Deploying LLMs
  8. The Lucid Vision Extension integrates advanced vision models into textgen-webui, enabling contextualized conversations about images and direct communication with vision models.

  9. This tutorial guides readers on how to fine-tune the Mistral 7B large language model using QLoRA with the Axolotl library, focusing on managing limited GPU resources for efficient training. It covers environment setup, dataset creation, configuration of QLoRA hyperparameters, the fine-tuning process, and testing the fine-tuned model.

  10. This tutorial demonstrates how to fine-tune the Llama-2 7B Chat model for Python code generation using QLoRA, gradient checkpointing, and SFTTrainer with the Alpaca-14k dataset.

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