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README.md

@@ -1,57 +1,67 @@
 ---
-library_name: transformers
+language:
+- en
 license: apache-2.0
-base_model: distilgpt2
 tags:
-- generated_from_trainer
-model-index:
-- name: distilgpt2-grok-coder-reasoning
-  results: []
+- text-generation
+- pytorch
+- causal-lm
+- reasoning
+- coding
+- grok-distillation
+- mature
+base_model: distilgpt2
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # distilgpt2-grok-coder-reasoning
 
-This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
+## Model Details
 
-More information needed
+### Model Description
+This model is a full fine-tuned version of **DistilGPT2**, exposed to an aggressive, completely uncapped curriculum of Grok-4 level distillation traces, hyper-creative and logic 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.
 
-## Training and evaluation data
+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.
 
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0003
-- train_batch_size: 8
-- eval_batch_size: 16
-- seed: 42
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 128
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 0.05
-- num_epochs: 1
-- mixed_precision_training: Native AMP
-
-### Training results
+- **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/Grok4.4_heavy_max_distill_god_seed_25k](https://huggingface.co/datasets/WithinUsAI/Grok4.4_heavy_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/grok-code-fast-1-1000x](https://huggingface.co/datasets/TeichAI/grok-code-fast-1-1000x)
+- [TeichAI/brainstorm-v3.1-grok-4-fast-200x](https://huggingface.co/datasets/TeichAI/brainstorm-v3.1-grok-4-fast-200x)
+- [Crownelius/Hyper-Creative-Grok-V1](https://huggingface.co/datasets/Crownelius/Hyper-Creative-Grok-V1)
+- [Crownelius/Hyper-UltraData-Grok-V1](https://huggingface.co/datasets/Crownelius/Hyper-UltraData-Grok-V1)
 
-### Framework versions
+---
 
-- Transformers 5.0.0
-- Pytorch 2.10.0+cu128
-- Datasets 4.8.3
-- Tokenizers 0.22.2
+## 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 Grok-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