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CodeGPT: A Code-Related Dialogue Dataset Generated by GPT and for GPT
The main features of zxx000728/codegpt are: Industry Applications, Large Language Models, Natural Language Processing.
Projects with overlapping indexed features include: allenai/mmc4 — MultimodalC4 is a multimodal extension of c4 that interleaves millions of images with text. berriai/litellm — LiteLLM is a unified gateway and proxy server designed to centralize access to over one hundred language model… aigc-audio/audiogpt — AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural… alisawuffles/ambient — Code and data associated with the AmbiEnt dataset in "We're Afraid Language Models Aren't Modeling Ambiguity" (Liu et… artidoro/qlora — This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation… bigcode-project/starcoder — Starcoder is a large language model and associated framework designed to generate, complete, and evaluate source code…
Code and data associated with the AmbiEnt dataset in "We're Afraid Language Models Aren't Modeling Ambiguity" (Liu et al., 2023)
MultimodalC4 is a multimodal extension of c4 that interleaves millions of images with text.
AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation. The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while pre
This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset