microsoft/LMOps
LMOps
LMOps is a research-driven operations framework for optimizing the deployment, fine-tuning, and performance of large language models. It provides a specialized toolkit for foundation model adaptation, inference acceleration, prompt optimization, and context orchestration.
The framework distinguishes itself through an inference accelerator that reduces token generation latency by verifying and copying overlapping text spans from reference documents. It also features a prompt engineering optimizer that employs reinforcement learning, beam search, and non-natural language markers to automatically refine instructions for higher output quality.
The toolkit covers broad capability areas including model tuning and adaptation for professional domains, behavior alignment using model-generated feedback, and retrieval-augmented context management for grounded responses. It further supports in-context learning scaling for long-sequence prompts and high-quality training data selection to improve fine-tuning efficiency.
Features
- Reference-Based Accelerators - Implements a specialized technology for speeding up token generation via reference document verification.
- Automatic Prompt Engineering - Transforms user inputs into preferred model prompts using reinforcement learning and beam search.
- RAG-Specific Context Optimizers - Orchestrates the retrieval of external information and scales in-context learning for grounded responses.
- Inference Acceleration - Reduces token generation latency and increases throughput during model execution.
- Retrieval Augmented Generation - Provides foundation models with grounded information using external retrieval mechanisms.
- LLM Fine-Tuning Toolsets - Provides tools for selecting high-quality training data and applying adaptation techniques for specialized tasks.
- LLM Operations Frameworks - Provides a research-driven toolkit for optimizing the deployment, fine-tuning, and performance of LLMs in production.
- Model Inference Accelerators - Speeds up token generation by copying and verifying overlapping text spans from reference documents.
- Domain Adaptation - Customizes general-purpose foundation models to increase accuracy within professional or technical fields.
- Model Fine-Tuning and Adaptation - Provides techniques and workflows for refining foundation models to specialized professional or technical domains.
- Prompt Optimizers - Automatically refines prompts and utilizes non-natural language markers to increase output quality.
- Prompt Optimizers - Refines and automatically optimizes prompts to improve the accuracy and quality of LLM outputs.
- RAG Context Retrieval - Retrieves relevant external information and scales in-context learning for grounded responses.
- Reference-Based Token Copying - Speeds up inference by copying overlapping text spans from reference documents directly into the model input.
- Domain Specific Models - Enables customizing foundation models to increase accuracy and knowledge within specific professional or technical fields.
- Foundation Model Adaptation - Provides a suite of techniques for fine-tuning and aligning foundation models to specialized domains.
- Model Alignment and Feedback - Adjusts model responses and behaviors by utilizing feedback generated from other large language models.
- Long Context Processing - Increases in-context learning capacity by processing large volumes of examples and extended prompts.
- Non-Natural Language Prompting - Delivers precise instructions to models using non-natural language markers and synthetic words.
- Non-Natural Language Markers - Delivers fine-grained instructions using non-natural language markers and synthetic words to improve precision.
- Reinforcement Learning Optimizers - Transforms user inputs into preferred model prompts using reinforcement learning and beam search.
- Training Data Subset Selection - Identifies and selects effective training data subsets to improve the efficiency of model fine-tuning.
- Subset Selection - Provides methods for selecting effective training data subsets to improve fine-tuning efficiency.
- Distillation Algorithms - Provides a framework for knowledge distillation of large models.
- Model Distillation - Provides frameworks for knowledge distillation of large language models.
- Prompt Optimization - Automatic prompt optimization using gradient-like descent.
- AI Development Resources - Research and operational guidance for managing language model workflows.
- Learning and Reference - Research and operational guides for language model workflows.
