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.
OpenInference is a set of conventions and plugins that complements OpenTelemetry to enable tracing of AI applications, with native support from arize-phoenix and compatibility with other OpenTelemetry-compatible backends.
Simon Willison introduces llm-smollm2, a plugin for LLM that includes a quantized version of the SmolLM2-135M-Instruct model. The article details how to install and use the model, discusses the process of finding, building, packaging, and publishing the plugin, and evaluates the model's capabilities.
The article by Krishan Walia provides a beginner-friendly guide on fine-tuning the DeepSeek R1 model using Python. It highlights how developers can transform a general-purpose AI model into a specialized, domain-specific language model for various applications.
This folder contains some example client scripts using our Python SDK for connecting with Llama Stack Distros. Instructions are provided for setting up dependencies and running demo scripts and apps.
An article detailing the capabilities and application of PydanticAI in building production-grade AI applications, particularly focusing on multi-agent systems.
Guidelines for using large language models to improve Python code quality in casual usage.
MarkItDown is a utility for converting various files to Markdown, including PDF, PowerPoint, Word, Excel, Images, Audio, HTML, text-based formats, and ZIP files.
An analysis showing that structured outputs can sometimes perform worse than unstructured ones in certain tasks for different LLM models, emphasizing the importance of testing both approaches.
ServiceNow releases AgentLab, an open-source Python package for developing and evaluating web agents, addressing challenges in agent development such as poor scalability and difficulty in conducting reproducible experiments.
