GPT2.5.5-Awakened.Thinker-0.1B/README.md

language, license, tags, base_model, datasets, model_type, pipeline_tag, library_name, widget

language	license	tags	base_model	datasets	model_type	pipeline_tag	library_name	widget
en	mit	gpt2 causal-lm text-generation distillation fine-tuned withinusai full-fine-tune thinking reasoning	openai-community/gpt2	WithinUsAI/GPT_5.5_Distilled WithinUsAI/GPT5.5_thinking_max_distill_god_seed_25K	gpt2	text-generation	transformers	text The key to understanding intelligence is text Let me think through this step by step: text Deep within the layers of thought,

GPT2.5.5-Awakened.Thinker-0.1B

Forged by WithIn Us AI — a fully fine-tuned GPT-2 awakened on distilled GPT-5.5 thinking patterns.

Model Overview

Property	Value
Architecture	GPT-2 (openai-community/gpt2)
Parameters	~124M (0.1B class)
Training Type	Full fine-tune — ALL weights updated, zero adapters
Context Window	1024 tokens
Best Eval Loss	0.4365
Best Perplexity	1.55
Creator	GODsStrongestSoldier / WithIn Us AI
Date Trained	2026-05-23
Hardware	2× NVIDIA Tesla T4 (Kaggle)
Precision	FP16 mixed precision

---

Training Methodology

Full fine-tuning — every single parameter in GPT-2 was updated. No LoRA, no QLoRA, no adapters of any kind.

Datasets

Dataset	Description
WithinUsAI/GPT_5.5_Distilled	Instruction + completion pairs distilled from GPT-5.5
WithinUsAI/GPT5.5_thinking_max_distill_god_seed_25K	25K chain-of-thought reasoning traces distilled from GPT-5.5

97 / 3 train / eval split.

Hyperparameters

Parameter	Value
Peak Learning Rate	3e-5
LR Schedule	Cosine with 6% warmup
Effective Batch Size	64 (4 × 2 GPUs × 8 grad accum)
Epochs	5
Weight Decay	0.1
Max Sequence Length	1024
Precision	FP16

---

Quick Start

from transformers import GPT2LMHeadModel, GPT2TokenizerFast
import torch

model_id  = "GODsStrongestSoldier/GPT2.5.5-Awakened.Thinker-0.1B"
tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
model     = GPT2LMHeadModel.from_pretrained(model_id, torch_dtype=torch.float16)
model.eval()

prompt = "Let me think through this carefully, step by step:"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens     = 200,
        do_sample          = True,
        temperature        = 0.7,
        top_p              = 0.9,
        repetition_penalty = 1.15,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

---

About WithIn Us AI

• HuggingFace org : WithinUsAI
• Creator : GODsStrongestSoldier

"Strength through understanding. Awakened from within."