# togethercomputer/openchatkit **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/togethercomputer-openchatkit).** 8,981 stars · 1,000 forks · Python · Apache-2.0 ## Links - GitHub: https://github.com/togethercomputer/OpenChatKit - awesome-repositories: https://awesome-repositories.com/repository/togethercomputer-openchatkit.md ## Description OpenChatKit is a training and inference toolkit for large language models. It provides a comprehensive set of tools for managing the model lifecycle, including a fine-tuning pipeline, a model weight converter, and a command-line interface for interacting with conversational agents. The toolkit features a framework for retrieval augmented generation, allowing models to incorporate relevant context from external vector indices. It also includes utilities for converting trained model checkpoints into formats compatible with standard inference libraries. The project covers conversational AI training through instruction-tuning and context window optimization, supported by 8-bit quantized optimization to reduce memory overhead. It provides capabilities for stateful conversation tracking, metric-based training logging to monitor convergence, and shell-based model testing to evaluate hyperparameters and response quality. ## Tags ### Artificial Intelligence & ML - [Model Training Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-toolkits.md) — Provides a comprehensive toolkit for the full lifecycle of training, fine-tuning, and executing large language models. - [Conversational Memory Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/memory-context-systems/conversational-memory-systems.md) — Implements architectures to manage and store historical interaction data for maintaining context in ongoing AI conversations. ([source](https://github.com/togethercomputer/openchatkit#readme)) - [Context Management Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/context-management-tools.md) — Provides utilities for optimizing and managing the input context and conversation history for large language models. - [Conversation Context Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/conversation-context-tracking.md) — Maintains a history of previous queries and responses to provide a sliding window of context for the model. - [Instruction Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/instruction-tuning.md) — Implements instruction-tuning workflows to adapt pre-trained language models to follow specific user commands and behaviors. - [Retrieval Augmented Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation.md) — Implements a framework for grounding model responses by retrieving relevant context from external vector indices. ([source](https://github.com/togethercomputer/openchatkit#readme)) - [Language Model Querying](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-querying.md) — Provides an interface for sending natural language prompts to large language models and processing their conversational responses. ([source](https://github.com/togethercomputer/openchatkit#readme)) - [Large Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/large-language-model-fine-tuning.md) — Adapts pre-trained large language models to specific conversational tasks using custom instruction-tuning datasets. - [Model Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/model-integration-pipelines/model-inference.md) — Includes utilities for loading models and executing inference to generate text responses and evaluate trained weights. ([source](https://github.com/togethercomputer/openchatkit#readme)) - [Fine-Tuning Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/fine-tuning-pipelines.md) — Implements a workflow for optimizing base models via instruction-tuning and context window adjustments. - [Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/language-model-fine-tuning.md) — Implements specialized workflows for fine-tuning language models using instruction datasets to create chat agents. ([source](https://github.com/togethercomputer/openchatkit#readme)) - [RAG Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/rag-frameworks.md) — Provides a framework for augmenting model responses by retrieving relevant context from external vector indices. - [Command-Line](https://awesome-repositories.com/f/artificial-intelligence-ml/chat-interfaces/command-line.md) — Ships a terminal-based environment for testing model performance and interacting with conversational agents. - [Context Window Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/context-window-optimizations.md) — Includes capabilities to adjust language models to optimize performance for extended input windows. ([source](https://github.com/togethercomputer/openchatkit#readme)) - [Local Inference CLI](https://awesome-repositories.com/f/artificial-intelligence-ml/local-model-execution/local-inference-cli.md) — Ships a terminal-based environment for executing model inference and inspecting hyperparameters in real time. - [Command Line Inference Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/command-line-inference-interfaces.md) — Provides a command-line interface to run model inference for testing performance and verifying weights before deployment. - [Model Conversion Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/large-language-model-optimization/model-inference-optimizations/model-conversion-tools.md) — Includes utilities for transforming trained model checkpoints into formats compatible with standard inference libraries. ([source](https://github.com/togethercomputer/openchatkit#readme)) - [Quantized Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/quantized-fine-tuning.md) — Uses 8-bit quantized fine-tuning to reduce memory overhead by operating on low-precision base weights. - [Model Weight Converters](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-converters.md) — Provides utilities to convert trained model checkpoints into formats compatible with standard inference libraries. ### Data & Databases - [Retrieval Augmentation](https://awesome-repositories.com/f/data-databases/retrieval-augmentation.md) — Injects external data into model prompts by retrieving relevant context from vector stores to ground responses. ### Testing & Quality Assurance - [CLI Testing Interfaces](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/cli-testing-interfaces.md) — Ships a command-line interface for executing natural language queries and inspecting model hyperparameters in real time. ([source](https://github.com/togethercomputer/openchatkit#readme)) ### Part of an Awesome List - [Conversational AI Platforms](https://awesome-repositories.com/f/awesome-lists/ai/conversational-ai-platforms.md) — Toolkit for fine-tuning large language models on conversational prompts. - [Open Source Models](https://awesome-repositories.com/f/awesome-lists/ai/open-source-models.md) — Offers a framework for creating specialized conversational bots.