A compact GPT-2 Small model (~117M params) trained from scratch on historical London texts (1500–1850). Fast to run on CPU, and supports NVIDIA (CUDA) and AMD (ROCm) GPUs. > Note: This model was trained from scratch — not fine-tuned from existing models. > This page includes simple virtual-env setup, install choices for CPU/CUDA/ROCm, and an auto-device inference example so anyone can get going quickly. Use cases: historical-style narrative generation, prompt-based exploration of London themes (1500–1850), creative writing aids. Limitations: may produce anachronisms or historically inaccurate statements; smaller models have less complex reasoning than larger LLMs. Validate outputs before downstream use. This snippet picks the best device (CUDA/ROCm if available, else CPU) and uses sensible generation defaults for this SLM. Need more headroom later? Load with 🤗 Accelerate and devicemap=»auto»` to spread layers across available devices/CPU automatically. Architecture: GPT-2 Small (12 layers, hidden size 768) Params: ~117M Tokenizer: custom historical tokenizer (~30k vocab) with London-specific and historical tokens Data: historical London corpus (1500–1850) Steps/Epochs: 30,000…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: bahree
Теги: gpt2, historical, london, slm, small-language-model, history, english, en
Лайков: 3 | Загрузок: 28
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
