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ModelHub XC 856c482bd3 初始化项目,由ModelHub XC社区提供模型
Model: GODsStrongestSoldier/distilgpt2-supernatural-occult-coder
Source: Original Platform
2026-05-26 20:21:13 +08:00

2.7 KiB

language, license, tags, base_model, datasets
language license tags base_model datasets
en
apache-2.0
text-generation
pytorch
causal-lm
occult
supernatural
magic
coding
nsfw
distilgpt2
WithinUsAI/Supernatural_25k
WithinUsAI/high_priest_occult_25k
WithinUsAI/high_priest_supernatural_magic_FACT_BASED_1M
WithinUsAI/gods_universe_codex_distill_god_seed_25k
jjmachan/NSFW-reddit

distilgpt2-supernatural-occult-coder

Model Details

Model Description

This model is a full fine-tuned version of DistilGPT2, specialized in generating a unique blend of text covering the occult, supernatural magic, coding/programming, and mature internet discourse. It was trained comprehensively on a massive merged dataset of over 1.5 million rows to synthesize these distinct themes into a single generative framework.

As a lightweight causal language model (82M parameters), it is optimized for extremely fast text generation across varied esoteric and technical domains.

  • Developed by: GODsStrongestSoldier
  • Model type: Causal Language Model (Transformer Decoder)
  • Language: English
  • License: Apache 2.0
  • Finetuned from model: distilgpt2

Datasets Used for Fine-Tuning

This model was trained on a concatenated corpus consisting of the following datasets:


Training Details

Training Procedure

The model underwent full fine-tuning (no LoRA or adapters). All layers of the base model were globally updated. Datasets were dynamically loaded, stripped of extraneous columns, converted entirely to text, concatenated, and shuffled with a fixed seed to ensure an even distribution of themes throughout the training process.

Texts were grouped into continuous sequences of 512 tokens.

Hardware

  • Environment: Kaggle
  • Accelerators: Dual NVIDIA T4 GPUs (15GB VRAM each)

Hyperparameters

  • Epochs: 1 (Due to the massive 1.5M+ row dataset size)
  • Per-Device Batch Size: 8
  • Gradient Accumulation Steps: 8
  • Effective Global Batch Size: 128
  • Learning Rate: 5e-05
  • Optimizer: Fused AdamW (adamw_torch_fused)
  • Mixed Precision: fp16
  • Gradient Checkpointing: Enabled