Update README tone to third person and remove emojis

README.md

@@ -1,32 +1,31 @@
-# 🍼 Nano-GLM (GLM-5.2 Baby 120M) - From Scratch
+# Nano-GLM (GLM-5.2 Baby 120M) - From Scratch
 
-This repository contains a from-scratch implementation and pretraining script for a "baby" version (~120M parameters) of **GLM-5.2** (GLM MoE DSA). It is heavily inspired by Andrej Karpathy's `nanoGPT` and aims to be highly educational.
+This repository contains a from-scratch implementation and pretraining script for a baby version (~120M parameters) of GLM-5.2 (GLM MoE DSA). The project is heavily inspired by Andrej Karpathy's nanoGPT and aims to serve as a highly educational resource.
 
-The model is small enough to train on a single consumer laptop GPU (e.g., RTX 4050 6GB VRAM) but includes all the cutting-edge architectural innovations of modern frontier models.
+The model is designed to be small enough to train on a single consumer laptop GPU (e.g., RTX 4050 6GB VRAM) while incorporating cutting-edge architectural innovations found in modern frontier models.
 
-## ✨ Architectural Features Implemented
+## Architectural Features Implemented
 
-This isn't just a standard Transformer. It implements three major innovations from recent frontier models (like DeepSeek-V3 and GLM-5):
+This implementation goes beyond a standard Transformer by incorporating three major innovations from recent frontier models (such as DeepSeek-V3 and GLM-5):
 
-1. **MLA (Multi-Latent Attention):** Compresses the attention mechanism using LoRA-style projections to drastically save VRAM during training and inference.
+1. **MLA (Multi-Latent Attention):** Compresses the attention mechanism using LoRA-style projections to drastically reduce VRAM usage during training and inference.
 2. **DSA (DeepSeek Sparse Attention):** Selects only the most relevant tokens to attend to via a learned indexer, rather than attending to the entire context uniformly.
-3. **MoE (Mixture of Experts):** Uses a fine-grained sigmoid-routed mixture of experts alongside a shared expert, activating only a subset of parameters per token.
+3. **MoE (Mixture of Experts):** Employs a fine-grained sigmoid-routed mixture of experts alongside a shared expert, activating only a subset of parameters per token.
 
-## 🚀 Training Features
+## Training Features
 
-The training loop (`train_glm5.py`) is highly optimized for limited hardware (6GB VRAM) while maximizing throughput (~4,900 tokens/sec on an RTX 4050):
-- **Mixed Precision:** Uses `bfloat16` and TF32 Tensor Cores.
-- **Gradient Checkpointing:** Recomputes forward passes during backprop to save ~40% VRAM.
-- **Gradient Accumulation:** Achieves large effective batch sizes on a single GPU.
-- **WSD (Warmup-Stable-Decay) Learning Rate Schedule:** 
-  Supports multi-phase training by holding the learning rate at peak for a "stable" exploration phase before initiating a steep cosine decay. (Controlled via `--stable_iters`).
+The training loop (train_glm5.py) is highly optimized for limited hardware (6GB VRAM) while maximizing throughput (achieving ~4,900 tokens/sec on an RTX 4050):
+- **Mixed Precision:** Utilizes bfloat16 and TF32 Tensor Cores.
+- **Gradient Checkpointing:** Recomputes forward passes during backpropagation to reduce VRAM consumption by approximately 40%.
+- **Gradient Accumulation:** Enables large effective batch sizes on a single GPU.
+- **WSD (Warmup-Stable-Decay) Learning Rate Schedule:** Supports multi-phase training by holding the learning rate at a peak for a stable exploration phase before initiating a steep cosine decay (controlled via the --stable_iters parameter).
 
-## 📚 Educational Guide
+## Educational Guide
 
-If you are new to LLM pretraining, learning rates, loss curves, and scaling laws, check out the included beginner guide:
-👉 **[LLM Training Guide for Beginners](llm_training_guide.md)**
+For individuals new to LLM pretraining, learning rates, loss curves, and scaling laws, an included beginner guide is available:
+[LLM Training Guide for Beginners](llm_training_guide.md)
 
-## 🛠️ Usage
+## Usage
 
 ### Installation
 ```bash
@@ -34,7 +33,7 @@ pip install -r requirements.txt
 ```
 
 ### Training
-To train the model on a single GPU with the WSD schedule (holding LR stable for 217,000 steps):
+To train the model on a single GPU using the WSD schedule (holding the learning rate stable for 217,000 steps), execute the following command:
 
 ```bash
 python train_glm5.py \
@@ -51,11 +50,11 @@ python train_glm5.py \
 ```
 
 ### Generation / Sampling
-To sample text from your best trained checkpoint:
+To sample text from the best trained checkpoint:
 
 ```bash
 python train_glm5.py --eval_only --ckpt out_glm5/ckpt_best.pt --prompt "The future of AI is"
 ```
 
-## ⚖️ License
+## License
 MIT License