Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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A comprehensive testing and evaluation framework for voice agents across language models, prompts, and agent personas.
Minimize LLM token complexity to save API costs and model computations.
Automated ML tool that optimizes machine learning pipelines using genetic programming.
Create. Use. Share. ChatGPT prompts.
Agent techniques to augment your LLM and push it beyond its limits.
Example of how the neural network learns to predict the angle between two points created with Synaptic.
Suite of tools that users, both novice and advanced, can use to optimize machine learning models for deployment and execution.
Hyperparameter Optimization for TensorFlow, Keras and PyTorch.
DSPy: The framework for programming — not prompting — foundation models.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Simple and efficient library to minimize expensive and noisy black-box functions.
Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...).
Robust Bayesian Optimization framework.
Bayesian optimization in high-dimensions via random embedding.
Implementing a ChatGPT-like LLM from scratch, step by step.
File Parser optimised for LLM Ingestion with no loss. Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.
A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Open-source tool to simplify the process of creating and managing LLM workflows and prompts as a self-hosted solution.
Ask questions to your documents without an internet connection, using the power of LLMs.
A JavaScript application framework for machine learning and its engineering.
Python-based meta-heuristic optimization techniques.
Google TPU optimizations for transformers models.