Search Results for "python programming language" - Page 26

Llama-2-70B-Chat is Meta’s largest fine-tuned large language model, optimized for dialogue and aligned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). It features 70 billion parameters and uses a transformer architecture with grouped-query attention (GQA) to improve inference scalability. Trained on 2 trillion tokens from publicly available sources and over a million human-annotated examples, the model outperforms most open-source chat models and rivals...

Llama-2-7B is a foundational large language model developed by Meta as part of the Llama 2 family, designed for general-purpose text generation tasks. It is a 7 billion parameter auto-regressive transformer trained on 2 trillion tokens from publicly available sources, using an optimized architecture without Grouped-Query Attention (GQA). This model is the pretrained version, intended for research and commercial use in English, and can be adapted for downstream applications such as summarization...

Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection - https://github.com/Tessellate-Imaging...

chronos-t5-small is part of Amazon’s Chronos family of time series forecasting models built on transformer-based language model architectures. It repurposes the T5 encoder-decoder design for time series data by transforming time series into discrete tokens via scaling and quantization. With 46 million parameters and a reduced vocabulary of 4096 tokens, this small variant balances performance with efficiency. Trained on both real-world and synthetic time series datasets, it supports...

..., including Reinforcement Learning with Verifiable Rewards (RLVR), to enhance alignment and performance. Fine-tuned for real-world applications, it integrates language and vision through supervised learning, DPO, and UPO techniques. It supports long contexts up to 131,072 tokens and can be deployed using FastDeploy or the Hugging Face Transformers library. This version is ideal for developers needing high-performance, scalable multimodal capabilities in chat or image-based reasoning systems.