Update README.md

README.md

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 This repository holds the **original** AlterEgo checkpoint, in the model's **own from-scratch architecture** - i.e. the model exactly as it was trained, before any format conversion.
 
-- **Want to just use the model?** Use the Hugging Face / `transformers`-native version instead: **[`J-bom/AlterEgo`](https://huggingface.co/J-bom/AlterEgo)**. It's a **numerically-lossless conversion** of this checkpoint to `LlamaForCausalLM` (verified, max logit difference ~1e-6), and works out of the box with `transformers`, vLLM, and GGUF tooling. Full architecture, training details, benchmarks, and usage are documented on that model card.
+- **Want to just use the model?** Use the Hugging Face / `transformers`-native version instead: **[`jbomdev/AlterEgo`](https://huggingface.co/jbomdev/AlterEgo)**. It's a **numerically-lossless conversion** of this checkpoint to `LlamaForCausalLM` (verified, max logit difference ~1e-6), and works out of the box with `transformers`, vLLM, and GGUF tooling. Full architecture, training details, benchmarks, and usage are documented on that model card.
 - **Want the raw weights / the original architecture?** That's this repo. The checkpoint is a PyTorch state dict saved under the `"model"` key. Load and run it with the model definition and inference code in the training repo: **[github.com/J-bom/AlterEgo](https://github.com/J-bom/AlterEgo)**.
 
-In short: **`alterego_raw` is the original; [`J-bom/AlterEgo`](https://huggingface.co/J-bom/AlterEgo) is the converted, ready-to-use version.**
+In short: **`alterego_raw` is the original; [`jbomdev/AlterEgo`](https://huggingface.co/jbomdev/AlterEgo) is the converted, ready-to-use version.**
 
 ## Model summary
 
-A 373M-parameter, decoder-only transformer (Llama-style: GQA, RoPE, SwiGLU, RMSNorm) pre-trained from scratch on ~10B tokens of FineWeb-Edu and instruction-tuned on UltraChat-200K (ChatML). See the [main model card](https://huggingface.co/J-bom/AlterEgo) for architecture, training curves, hyperparameters, evaluation, and limitations.
+A 373M-parameter, decoder-only transformer (Llama-style: GQA, RoPE, SwiGLU, RMSNorm) pre-trained from scratch on ~10B tokens of FineWeb-Edu and instruction-tuned on UltraChat-200K (ChatML). See the [main model card](https://huggingface.co/jbomdev/AlterEgo) for architecture, training curves, hyperparameters, evaluation, and limitations.