--- base_model: - gss1147/GPT2.5.2-high-reasoning-codex-0.4B - openai-community/gpt2-medium model-index: - name: gpt2-finetuned results: [] datasets: - TeichAI/gpt-5.1-codex-max-1000x - TeichAI/gpt-5.2-high-reasoning-250x - jjmachan/NSFW-reddit - gss1147/GPT-2-to-GPT-5-5k - TeichAI/gpt-5-codex-250x - TeichAI/gpt-5.1-high-reasoning-1000x --- GPT2.5.2-NSFW-Codex-0.4B 📌 Model Overview Model Name: WithinUsAI/GPT2.5.2-NSFW-Codex-0.4B Organization: Within Us AI Base Model: openai-community/gpt2-medium (~0.4B parameters)  Model Type: Instruction-Tuned Code + General Text LLM Parameter Size: ~0.4B Primary Focus: Lightweight coding + uncensored responses + dataset-driven evolution This model represents a heavily evolved GPT-2 Medium, upgraded through dataset-driven training and reasoning/coding distillation. 🧬 Name Meaning “GPT2.5.2” = Evolution lineage * 2 → GPT-2 Medium base * .5.2 → GPT 5.2 Codex-style 👉 In short: A GPT-2 model pushed forward toward modern coding + reasoning behavior using curated datasets, not architecture scaling. ⸻ 🧬 Architecture & Lineage Base Foundation * Transformer architecture from GPT-2 Medium (~345M–400M parameters class)  * Dense, autoregressive language model Evolution Process This model was scaled up,evolved through data and training strategy: * Instruction tuning * Coding dataset exposure (Codex-style tasks) * Reasoning trace influence * Behavioral refinement toward modern LLM outputs ⸻ 🧠 Core Design Philosophy Don’t scale the model… evolve it. Instead of increasing parameters, this model: * Improves behavior through data quality * Mimics GPT 5.2 Codex reasoning styles * Pushes GPT-2 into modern task domains Think of it like a classic engine rebuilt with modern parts 🔧 ⸻ ⚙️ Key Capabilities 💻 Coding (Codex-Inspired) * Basic code generation (Python, JS, etc.) * Simple debugging assistance * Structured function outputs 🧠 Reasoning (Lightweight) * Step-by-step responses (limited depth) * Instruction-following improvements over base GPT-2 🔓 Uncensored Behavior * Reduced refusal tendencies * More permissive outputs compared to aligned models ⸻ 📦 Model Characteristics Attribute Value Parameters ~0.4B Architecture GPT-2 (dense transformer) Strength Efficiency Weakness Limited deep reasoning ⸻ 🚀 Intended Use ✅ Ideal Use Cases * Ultra-lightweight local LLMs * Experimental coding assistants * Dataset-driven model research * Uncensored response exploration * Edge/low-resource environments ⚠️ Limitations * Significantly weaker than modern 7B+ models * Limited context and reasoning depth ⸻ 🛠️ Example Usage (Transformers) from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "WithinUsAI/GPT2.5.2-NSFW-Codex-0.4B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) prompt = "Write a Python function to reverse a string." inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0])) ⸻ 🧪 Training Methodology Within Us AI approach: * Dataset-driven evolution (primary driver) * Instruction + coding task fine-tuning * Reasoning-style output shaping Data Sources * Proprietary datasets created by Within Us AI * Third-party datasets used without ownership claims * Likely includes: * Coding tasks * Instruction datasets * Reasoning traces ⸻ 📊 Expected Performance Profile Capability Strength Efficiency Very High Coding (basic) High Reasoning High Creativity Moderate Safety filtering Minimal ⸻ 📜 License License Type: Based on GPT-2 (OpenAI open model lineage)** Attribution Notes: * Base model: OpenAI GPT-2 Medium * Training / evolution: Within Us AI * Third-party datasets used without ownership claims * Credit belongs to original dataset and model creators ⸻ 🙏 Acknowledgements * OpenAI (GPT-2 architecture) * Hugging Face (model hosting ecosystem) * Open-source dataset contributors * Coding dataset communities ⸻ 🔗 Links * Model: https://huggingface.co/WithinUsAI/GPT2.5.2-NSFW-Codex-0.4B * Organization: https://huggingface.co/WithinUsAI ⸻ 🧩 Closing Note This model is like a time traveler from 2019 carrying tools from 2026 ⏳⚡ Same small brain… but trained to think like GPT 5.2 Codex.