---
language:
- en
license: apache-2.0
tags:
- text-generation
- pytorch
- causal-lm
- reasoning
- coding
- opus-distillation
- mature
base_model: distilgpt2
---

# distilgpt2-opus-coder-reasoning

## Model Details

### Model Description
This model is a full fine-tuned version of **DistilGPT2**, exposed to an aggressive, completely uncapped curriculum of Claude Opus (4.5, 4.6, 4.7) distillation traces, Chain-of-Thought (CoT) datasets, comprehensive coding logic, and mature internet discourse. It is designed to act as a highly responsive, analytical engine capable of deep structural reasoning and complex logic emulation.

Trained natively at an accelerated maximum learning rate with a cosine decay schedule, the model synthesizes diverse programmatic and theoretical domains from a massive multi-repository corpus, processed at the model's absolute maximum context window of 1024 tokens.

- **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 comprehensively on the full, uncapped contents of the following datasets:
- [WithinUsAI/Opus4.7_thinking_max_distill_god_seed_25k](https://huggingface.co/datasets/WithinUsAI/Opus4.7_thinking_max_distill_god_seed_25k)
- [WithinUsAI/GOD_Coder_Complete_DataSet](https://huggingface.co/datasets/WithinUsAI/GOD_Coder_Complete_DataSet)
- [acheong08/nsfw_reddit](https://huggingface.co/datasets/acheong08/nsfw_reddit)
- [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x)
- [Roman1111111/claude-opus-4.6-10000x](https://huggingface.co/datasets/Roman1111111/claude-opus-4.6-10000x)
- [Crownelius/Opus-4.6-Reasoning-3300x](https://huggingface.co/datasets/Crownelius/Opus-4.6-Reasoning-3300x)
- [TeichAI/Claude-Opus-4.6-Reasoning-887x](https://huggingface.co/datasets/TeichAI/Claude-Opus-4.6-Reasoning-887x)
- [Crownelius/Opus-4.5-WritingStyle-1000x](https://huggingface.co/datasets/Crownelius/Opus-4.5-WritingStyle-1000x)
- [Crownelius/Opus-4.6-Reasoning-2100x-formatted](https://huggingface.co/datasets/Crownelius/Opus-4.6-Reasoning-2100x-formatted)
- [eddieran/opus-4.7-reasoning-cot](https://huggingface.co/datasets/eddieran/opus-4.7-reasoning-cot)
- [Farseen0/opus-4.6-reasoning-sft-12k](https://huggingface.co/datasets/Farseen0/opus-4.6-reasoning-sft-12k)

---

## Training Details

### Training Procedure
The model underwent **full fine-tuning** without the use of adapters or LoRA layers. All native parameters of the base model were globally updated. The training harness dynamically parsed heavily nested dataset repositories, enforcing a strict shape constraint to generate mathematically perfect 1024-token continuous sequences for the GPU, maxing out the DistilGPT2 context window.

To maximize adaptation to the Opus-level reasoning data, an absolute peak learning rate (`3e-4`) was utilized alongside a 5% warmup phase and a cosine scheduler.

#### Hardware
- **Environment:** Kaggle
- **Accelerators:** Dual NVIDIA T4 GPUs (15GB VRAM each)

#### Hyperparameters
- **Epochs:** 1 
- **Context Window / Block Size:** 1024
- **Per-Device Batch Size:** 4
- **Gradient Accumulation Steps:** 16
- **Effective Global Batch Size:** 128
- **Peak Learning Rate:** 3e-04
- **Learning Rate Scheduler:** Cosine
- **Warmup Ratio:** 0.05
- **Optimizer:** Fused AdamW (`adamw_torch_fused`)
- **Mixed Precision:** fp16
- **Gradient Checkpointing:** Enabled