Update README.md

README.md

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----
-license: mit
----
+---
+license: other
+tags:
+- text-generation
+- shader
+- glsl
+- shadertoy
+library_name: pytorch
+pipeline_tag: text-generation
+model_name: Frag Loop
+---
+
+# Frag Loop
+
+Frag Loop is a decoder-only Transformer trained to generate **Shadertoy-compatible GLSL fragment shader bodies**. The model outputs the body only (functions + `mainImage`), and is intended to be wrapped by a fixed WebGL2/Shadertoy template at runtime.
+
+## Intended Use
+- **Primary**: generate novel GLSL fragment shader bodies compatible with Shadertoy-style templates.
+- **Not intended for**: general chat, safety-critical systems, or production code generation without review.
+
+## Training Data (Summary)
+- **Pretraining**: GLSL-heavy subset of **The Stack (bigcode/the-stack-dedup)**.
+- **Fine-tuning (SFT)**: **Vipitis/Shadereval-inputs** (Shadertoy-oriented subset).
+
+These datasets contain code under multiple licenses. If you redistribute outputs or data, ensure provenance and license compliance.
+
+## Tokenizer
+The tokenizer implementation is **heavily influenced by nanochat**:
+- https://github.com/karpathy/nanochat
+
+## Training Procedure (High-level)
+- Decoder-only Transformer (GPT-style) trained from scratch.
+- Pretrain on GLSL-heavy corpus, then SFT on Shadertoy-style examples.
+- Optional copy-detection and automatic rejection are supported in the runtime.
+
+## Evaluation
+- Primary evaluation is **compile + render** for WebGL2 (moderngl), with metrics for:
+  - compile success
+  - render time
+  - black / static detection
+  - NaN / Inf detection
+
+## How to Use (Recommended Runtime)
+The reference runtime + WebUI are provided in the GitHub repo:
+- https://github.com/hanasaan/FragLoop
+
+From the `publish/` bundle, you can run:
+
+```bash
+# download from HF and start inference server
+python infer/inference_server.py --hf-repo hanasaan/frag-loop
+
+# start UI server
+node ui_optional/server_node/server.js
+```
+
+Open `http://localhost:5173` in a browser.
+
+## Limitations
+- Outputs may occasionally fail to compile or render.
+- The model can still produce outputs similar to training data. Use copy-detection when displaying or distributing outputs.
+- Shader aesthetics and performance vary; manual curation is recommended for showcases.
+
+## Acknowledgements
+- Tokenizer influenced by **nanochat** (https://github.com/karpathy/nanochat).
+- All implementation work in this repository was done by **GPT-5.2-Codex (xhigh)**.
+
+## License
+This model is trained on datasets with mixed licenses. Please review and comply with dataset licensing terms when using or redistributing the model or outputs.