Update README.md

README.md

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 ## Model Description
-This is a distilled language model trained using Group Relative Policy Optimization (GRPO) with VAE filtering. 
-**MICROD v1.0 (micro-distill-grpo-vae)** is a small template model designed to be built upon for custom ground up builds. It is distillated into a 
-small set of files the user can use to template their own agents. Designed for educational learning and micro scalling. 
-Use **MICROD V1.0 (micro-distill-grpo-vae)** in your own custom projects and train it from the ground up.
-
-The model's architecture details further underscore an educational niche: a hidden size of 512, 8 layers, 8 attention heads, a vocabulary of 50,257 tokens, 
-and a max sequence length of 1024. Licensed under Apache 2.0, it's openly available for modification, and its small footprint allows quantization, 
-making it runnable on modest hardware like CPUs or even browsers via TensorFlow.js integration.
-
-## Model Details
-- **Model type**: micro-distill-grpo-vae
-- **Model size**: 42M parameters
-- **Language**: English
-- **License**: Apache 2.0
-
-## Training Methodology
-- **GRPO (Group Relative Policy Optimization)**: 8 groups
-- **VAE Filtering**: 32D latent space
-- **KV-Cache Reuse**: 512 cache size
-
-## Architecture Details
-- Hidden size: 512
-- Number of layers: 8
-- Attention heads: 8
-- Vocabulary size: 50257
-- Maximum sequence length: 1024
-
-## Usage
-
--Model Distillation Training: Simulate GRPO optimization with VAE filtering for small LLMs (42M-345M params).
-
--Policy Experimentation: Test group sizes, KL penalties, cache reuse for RLHF-like training.
-
--VAE Filtering: Apply latent space compression to improve distillation quality.
-
--Sandbox Testing: Execute safe Python code with feedback masking.
-
--Export & Deployment: Generate deployable models for inference in various frameworks.
-
--Offline Usage: PWA supports offline training simulation and exports.
-
-## Citation
-
-If you use this data in research, please cite:
-
-@model{microd_v1_2025,
-  title={MICROD_v1},
-  author={webXOS]
-  year={2025},
-  publisher={webXOS},
-  url={webxos.netlify.app}
-}
-
-
-### EXAMPLE: Using Transformers
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model = AutoModelForCausalLM.from_pretrained("micro-distill-grpo-vae")
-tokenizer = AutoTokenizer.from_pretrained("micro-distill-grpo-vae")
-
-inputs = tokenizer("Hello, world!", return_tensors="pt")
-outputs = model.generate(**inputs, max_length=50)
-print(tokenizer.decode(outputs[0]))
-```
-
-### EXAMPLE: USE CASES
-
-**MICROD_v1** may not rival larger models in breadth, but a focus on accessibility and browser-based AI development opens doors for 
-innovators balancing all perspectives in the Small Language Model space.
-
-1. Prototype without Internet
-2. Offline Simulations in Black Box
-3. Simple Story Generators
-4. Custom Agentic Development
-5. Train on Custom Data
-6. Experiment with max_length
-7. AI agents for custom Games
-8. Educational Fine-Tuning
-9. Prepare Datasets
-10. Fine-tune via GRPO Trainer 
-11. Evaluate PY in Sandbox
-12. Create task-specific Variants like Code Tutors
-
-### OVERVIEW
-
-In terms of applications, small distilled models like **MICROD_v1** align with broader trends in SLMs, which prioritize efficiency, accessibility, 
-and specialization over the scale of large language models (LLMs). For example, they can be fine-tuned for targeted tasks such as customer support 
-chatbots, where quick responses on edge devices are crucial, or educational tools for teaching natural language processing concepts. In healthcare, 
-distilled models might power privacy-focused symptom checkers on mobile apps, avoiding data transmission to cloud servers. Automation and control 
-systems benefit from their low latency, as surveyed in research on tiny language models (TLMs), which use techniques like knowledge distillation and 
-quantization to enable on-device inference for robotics or IoT devices.
+UNDER DEVELOPMENT: MAJOR UPDATE UI FIX