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GPT2-Chinese is a Chinese language model implementation based on the GPT-2 architecture. It provides a causal language model trainer and a natural language generation tool designed for training and generating human-like Chinese text sequences.
The main features of morizeyao/gpt2-chinese are: Decoder Architectures, Causal, Training Pipelines, Chinese Natural Language Generation, GPT-2 Implementations, Language Model Training, Natural Language Generation, Text Tokenization.
Projects with overlapping indexed features include: qwenlm/qwen-7b — Qwen-7B is a pretrained causal language model designed for natural language generation, text processing, and complex… datawhalechina/so-large-lm — This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of… openai/gpt-2 — This project is a transformer-based language model and autoregressive text generator designed to predict the next… karpathy/build-nanogpt — This is an educational implementation that builds a generative pre-trained transformer (GPT) language model from… deepseek-ai/deepseek-llm — DeepSeek-LLM is a large language model and causal language model designed for natural language generation. It… nndl/llm-beginner — This project is a collection of educational resources and technical guides focused on the development and…
Qwen-7B is a pretrained causal language model designed for natural language generation, text processing, and complex reasoning tasks. It is available as an instruction-tuned model optimized for conversational interactions and a tool-use model capable of executing function calls and interacting with external APIs. The project provides a quantized version of the model to reduce GPU memory usage and supports the development of autonomous agents that can execute code and perform functions to complete complex goals. The system covers a wide range of capabilities including model fine-tuning throug
This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of large language models. It provides a guided progression through the theory, architecture, training, and deployment of these models. The curriculum includes specialized guides on transformer architecture, model training tutorials, and frameworks for designing autonomous agents. It also provides dedicated resources for studying model safety and ethics. The material covers a wide range of technical capabilities, including distributed training strategies, parameter-efficient fine-tu
This project is a transformer-based language model and autoregressive text generator designed to predict the next token in a sequence to produce human-like prose and synthetic text. It functions as a large language model that utilizes a transformer architecture to learn linguistic patterns from large datasets for unsupervised multitask learning. The repository provides a distribution of pre-trained weights, enabling natural language processing tasks without requiring additional training. This allows the model to perform zero-shot task generalization by applying learned patterns to new tasks.
This is an educational implementation that builds a generative pre-trained transformer (GPT) language model from scratch using PyTorch. The project is structured as a step-by-step tutorial, walking through the construction of a decoder-only transformer architecture and its training loop with clean git commits and an accompanying video lecture for a hands-on learning experience. What sets this implementation apart is its focus on practical reproduction: it provides a workflow to train a 124-million-parameter model from scratch in about one hour on cloud GPU hardware, costing under ten dollars.