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Model: GODsStrongestSoldier/distilgpt2-supernatural-occult-coder
Source: Original Platform

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation
+- pytorch
+- causal-lm
+- occult
+- supernatural
+- magic
+- coding
+- nsfw
+base_model: distilgpt2
+datasets:
+- 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:
+- [WithinUsAI/high_priest_occult_25k](https://huggingface.co/datasets/WithinUsAI/high_priest_occult_25k)
+- [WithinUsAI/gods_universe_codex_distill_god_seed_25k](https://huggingface.co/datasets/WithinUsAI/gods_universe_codex_distill_god_seed_25k)
+- [WithinUsAI/Supernatural_25k](https://huggingface.co/datasets/WithinUsAI/Supernatural_25k)
+- [WithinUsAI/high_priest_supernatural_magic_FACT_BASED_1M](https://huggingface.co/datasets/WithinUsAI/high_priest_supernatural_magic_FACT_BASED_1M)
+- [acheong08/nsfw_reddit](https://huggingface.co/datasets/acheong08/nsfw_reddit)
+
+---
+
+## 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