Inference/Generation Process - Autoregressive generation is used step-by-step during inference [1] - The encoder runs once, while the decoder runs multiple times [1] - Each step utilizes previous predictions to generate the next token [1]

模型架构 - Meta Llama 模型全部使用 Attention 机制 [1] - OpenAI GPT 模型全部使用 Attention 机制 [1] - Alibaba Qwen 模型全部使用 Attention 机制 [1] - Google Gemma 模型全部使用 Attention 机制 [1]

Context Engineering Workflow - The industry focuses on building a context engineering workflow step by step [1] - The industry highlights the importance of context engineering [1]

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/XSbmekHfM6Avi Chawla (@_avichawla):Let's build a context engineering workflow, step by step: ...

In the project, we used:1) Tensorlake:- It lets you transform any unstructured doc into AI-ready data.- https://t.co/AMl8cnhtGZ2) Zep- It lets you build human-like memory for your Agents.- https://t.co/aFsgR0kqlu3) Firecrawl- It lets you power LLM apps with clean data from the web.- https://t.co/QYY3IOy7NL4) Milvus- It gives a high-performance vector DB for scalable vector search.- https://t.co/DFFDMfRDmY ...

Workflow Construction - The document focuses on building a context engineering workflow step by step [1]

Cloud Computing Solution - Coiled simplifies cloud-based Python workflows, reducing complexity [1] - Coiled automates environment synchronization, hardware provisioning, and shutdown in the cloud [2] - Coiled offers 500 free CPU hours per month for most users [2] Key Features - Coiled enables running jobs hourly, concurrently, or with specific hardware like GPUs [3] - Coiled supports running jobs in different regions for data proximity [3] - Coiled allows using different languages, packages, or binaries [3] Development Process - Users import Coiled and decorate Python functions, specifying hardware and region [2] - Coiled eliminates the need for navigating consoles, setting IAM policies, or writing YAML configs [1][2] - Coiled helps in monitoring billing spikes [1]

Cloud Computing Challenges - Cloud usage involves navigating consoles, setting IAM policies, writing YAML configs, and monitoring billing spikes [1] - Running Python workflows on the cloud can be complex [1] Coiled Solution - Coiled simplifies cloud usage for Python users [1] - Coiled enables running any workflow [1] Call to Action - The author encourages readers to reshare the content [1] - The author shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]

Start here: https://t.co/RqixpFOzmb ...

Core Offering - Coiled enables running Python workloads on the cloud with minimal code [1] - Coiled automates environment synchronization, hardware provisioning, and shutdown in the user's cloud account [2] - Coiled offers 500 free CPU hours per month for most users [2] Key Features - Supports running jobs hourly, concurrently (1000s), and with specialized hardware (GPUs, fast disks) [3] - Enables running jobs in different regions and with diverse language/package/binary requirements [3] Pain Points Addressed - Simplifies cloud usage by eliminating the need for console navigation, IAM policy configuration, and YAML configuration [1][2] - Reduces concerns about billing spikes [1] Usage - Involves importing the Coiled library and decorating Python functions [2]