Awesome Open Source AI
Section: 7. Training & Fine-tuning Ecosystem · Distributed training framework and reference codebase for large transformer models at scale.
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Distributed training framework and reference codebase for large transformer models at scale.
Section: 7. Training & Fine-tuning Ecosystem · Distributed training framework and reference codebase for large transformer models at scale.
Section: Industry Strength Natural Language Processing · Megatron-LM is a highly optimized and efficient library for training large language models.
Section: Other · Ongoing research training transformer models at scale
Section: PyTorch · Ongoing research training transformer language models at scale, including: BERT.
The definitive curated list of machine learning frameworks, libraries and software organized by language. Covers Python, C++, Java, JavaScript, and more with comprehensive coverage of the ML ecosystem. CC0-1.0 licensed.
books: Freely available programming books
Langchain integrates various providers like Anthropic, AWS, and OpenAI, and offers tools for components such as LLMs, chat models, and data analysis, supporting functionalities from Alpha Vantage to YouTube github | docs
Curated list of artificial intelligence courses, books, video lectures, and papers for developers and researchers. MIT licensed.
Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…
(formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in…
(MIT) provides modules for structured outputs at different levels of abstraction, including output parsers for text completion endpoints, Pydantic programs for mapping prompts to structured outputs using function calling or output parsing, and pre-defined Pydantic programs for specific output types.
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