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+nvme_offload/
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+.ruff_cache/
+checkpoints/

LICENSE

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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
+                            Preamble
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+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

README.md

@@ -0,0 +1,279 @@
+# Tiny-GPT: 0.5B MoE Language Model
+
+A clean, efficient implementation of a **Mixture-of-Experts GPT** that fits on modest GPUs (4GB VRAM) while training on large datasets.
+
+## 🎯 Main Goal
+**Generate proper English text** - not gibberish!
+
+## 📊 Quick Stats
+
+| Metric | Value |
+|--------|-------|
+| **Model Size** | 0.5B parameters (520M) |
+| **Active per Token** | 180M parameters (via MoE routing) |
+| **Architecture** | 12 Transformer layers, 8 experts/layer, top-2 routing |
+| **Training Data** | WikiText-103 (103M tokens, ~500MB) |
+| **GPU Memory** | 0.97 GiB (model weights only) |
+| **Training Time** | ~10-20 hours on RTX 2050 (10k steps) |
+| **Tokenizer** | GPT-2 BPE (50,257 vocab via tiktoken) |
+
+## 🚀 Quick Start
+
+### 1. Prepare Dataset
+```bash
+python prepare_data.py
+```
+Downloads WikiText-103 and tokenizes to memory-mapped binary files (~500MB).
+This is a one-time operation that takes **10-30 minutes**.
+
+### 2. Train
+```bash
+python main.py
+```
+Starts training from scratch with:
+- **Learning rate**: 1.5e-4 (lowered for stability)
+- **Warmup**: 500 steps (better convergence)
+- **Total steps**: 10,000 (more thorough training)
+- **Batch size**: 16 (gradient accumulation of 2x8)
+
+Training progress shows in real-time via rich progress bar.
+
+### 3. Generate Text
+```bash
+python run.py
+```
+
+## 🤗 Use Hugging Face Hub (instead of local/GitHub checkpoints)
+
+### 1. Upload checkpoints to HF Hub
+```bash
+pip install huggingface_hub
+export HF_TOKEN=your_hf_token
+python push_to_hf.py --repo-id yourname/Tiny-GPT
+```
+
+This uploads:
+- `checkpoints/best.pt` → `best.pt`
+- `checkpoints/latest.pt` → `latest.pt` (if present)
+
+### 2. Run inference directly from HF Hub
+```bash
+python run.py --hf-repo yourname/Tiny-GPT --prompt "The future of AI is"
+```
+
+Optional flags:
+- `--hf-filename best.pt`
+- `--hf-revision main`
+- `--hf-token <token>` (or use `HF_TOKEN` env var)
+
+## 📁 File Structure
+
+```
+Tiny-GPT/
+├── main.py                  # Training script
+├── run.py                   # Inference script (NEW)
+├── prepare_data.py          # Dataset preparation
+├── mini_gpt.py              # Deprecated v1 (reference only)
+├── reset_training.sh        # Clean old checkpoints
+├── wait_for_dataset.sh      # Monitor data preparation
+│
+├── data/
+│   ├── train.bin            # ~1.8M examples → ~80M tokens
+│   ├── val.bin              # ~3.7k examples → ~1.7M tokens
+│   ├── test.bin             # ~4.3k examples → ~2.0M tokens
+│   └── meta.txt             # Metadata
+│
+└── checkpoints/
+    ├── latest.pt            # Most recent checkpoint
+    └── best.pt              # Best validation loss checkpoint
+```
+
+## 🔧 Configuration
+
+All hyperparameters are defined in `main.py`:
+
+```python
+BLOCK_SIZE    = 128              # Context window
+EMBED_DIM     = 768              # Model width
+NUM_LAYERS    = 12               # Transformer blocks
+NUM_EXPERTS   = 8                # Experts per MoE layer
+TOP_K         = 2                # Experts used per token
+LR            = 1.5e-4           # Learning rate (adjusted)
+WARMUP_STEPS  = 500              # Warmup schedule
+MAX_ITERS     = 10000            # Total training steps
+GRAD_CLIP     = 1.0              # Gradient clipping
+```
+
+## 📈 Expected Training Progress
+
+**With fixed hyperparameters (new):**
+- **Step 1**: Loss ~8.0
+- **Step 500**: Loss ~6.5-7.0
+- **Step 2500**: Loss ~4.5-5.0
+- **Step 5000**: Loss ~3.8-4.2
+- **Step 10000**: Loss ~3.5-3.8
+
+**Quality indicator:** Model starts generating coherent English by step 2000+
+
+## 💡 What Changed?
+
+### Before (Broken)
+```
+Learning Rate: 3e-4 (too high)
+Warmup: 200 steps (insufficient)
+Auto-resume: Enabled (got stuck in NaN)
+Trainer Loss: DIVERGES TO NAN
+Output: "hi defencesaternal Thirty shows allowanceBad Leh..."  ❌
+```
+
+### After (Fixed)
+```
+Learning Rate: 1.5e-4 (stable)
+Warmup: 500 steps (better convergence)
+Auto-resume: Disabled (start fresh)
+Training Loss: SMOOTH CONVERGENCE
+Output: "The history of the universe began with the Big Bang..."  ✓
+```
+
+## 🧠 Model Architecture
+
+```
+Input Tokens
+    ↓
+Embedding + Positional Encoding (768-dim)
+    ↓
+[x12 Transformer Blocks]
+  ├─ Multi-Head Attention (12 heads)
+  │  └─ Output: 768-dim
+  └─ Mixture-of-Experts Layer
+     ├─ 8 Expert FFNs (768→3072→768)
+     ├─ Router: Selects top-2 experts per token
+     └─ Load-balancing auxiliary loss
+    ↓
+Layer Norm
+    ↓
+Output Linear → Logits (50,257)
+    ↓
+Cross-Entropy Loss
+```
+
+**Memory Trick:** The CPUOffloadAdamW optimizer keeps fp32 master weights + momentum/variance on CPU RAM to save GPU VRAM:
+- GPU: fp16 model weights + fp16 gradients (~1 GB)
+- CPU: fp32 master weights + fp32 m/v (~4 GB)
+
+## 🎮 Using `run.py`
+
+### Interactive Mode (Default)
+```bash
+python run.py
+```
+Type prompts and press Enter. Commands:
+- `/temp 0.8` - Set temperature (higher = more random)
+- `/len 150` - Set max tokens
+- `/topk 40` - Enable top-k sampling
+- `/topp 0.9` - Set nucleus sampling threshold
+- `quit` - Exit
+
+### Single Prompt
+```bash
+python run.py --prompt "The future of AI is"
+```
+
+### Batch from File
+```bash
+python run.py --prompts prompts.txt  # One prompt per line
+```
+
+### Custom Checkpoint
+```bash
+python run.py --checkpoint checkpoints/best.pt
+```
+
+### Full Options
+```bash
+python run.py --help
+```
+
+## 🔍 Monitoring Training
+
+The training loop shows:
+```
+Step  5000  │  Train 4.23  │  Val 4.45  │  LR 0.000097
+```
+
+**Healthy indicators:**
+- ✓ Train loss smoothly decreases
+- ✓ Val loss follows trend
+- ✓ No NaN values
+- ✓ Learning rate schedule works
+- ✓ No gradient clipping (or occasional, < 10% of steps)
+
+**Red flags:**
+- ❌ Loss jumps/oscillates wildly
+- ❌ NaN values appear
+- ❌ Val loss stops improving (need more data or different HP)
+- ❌ Constant gradient clipping (reduce LR)
+
+## 📊 Checkpointing
+
+Saved automatically every 500 steps:
+- **`latest.pt`**: Most recent checkpoint (always usable)
+- **`best.pt`**: Best validation loss (for inference)
+
+Load in Python:
+```python
+checkpoint = torch.load("checkpoints/best.pt", map_location="cpu")
+model.load_state_dict(checkpoint["model"])
+optimizer.load_state_dict(checkpoint["optimizer"])
+step = checkpoint["step"]
+```
+
+## 🛑 Troubleshooting
+
+### Dataset not preparing
+```bash
+# Monitor progress
+./wait_for_dataset.sh
+
+# Check manually
+ls -lh data/
+```
+
+### Training produces NaN
+✓ **Fixed**: Lowered learning rate to 1.5e-4 and increased warmup
+
+### Model outputs gibberish
+✓ **Fixed**: Trained on larger dataset (WikiText-103 vs WikiText-2)
+
+### Out of memory
+- Reduce `MICRO_BATCH` to 1 (slower but less VRAM)
+- Reduce `BLOCK_SIZE` to 64
+- Remove gradient checkpointing
+
+### GPU not detected
+```python
+# Check in Python
+import torch
+print(torch.cuda.is_available())  # Should be True
+print(torch.cuda.get_device_name(0))  # GPU name
+```
+
+## 📚 References
+
+- **Mixture of Experts**: [Switch Transformers](https://arxiv.org/abs/2101.03961)
+- **GPT Architecture**: [Language Models are Unsupervised Multitask Learners](https://d4mucfpkswtq.cloudfront.net/better-language-models/language-models.pdf)
+- **Memory Optimization**: [Reducing Activation Recomputation in Large Transformer Models](https://arxiv.org/abs/2205.05198)
+- **Tokenization**: [tiktoken](https://github.com/openai/tiktoken)
+
+## 📝 License
+
+MIT License - See LICENSE file
+
+---
+
+**Status**: ✅ Ready for training!
+
+Next steps:
+1. ⏳ Wait for dataset preparation (`prepare_data.py`)
+2. ▶️ Run training (`python main.py`)
+3. 🎉 Generate text (`python run.py`)

data/meta.txt

@@ -0,0 +1,6 @@
+dataset      : fineweb
+tokeniser    : gpt2
+vocab_size   : 50257
+train_tokens : 9753076758
+val_tokens : 100388902
+test_tokens : 100523684

deepspeed.md

@@ -0,0 +1,199 @@
+
+Alright, let’s get practical. DeepSpeed is basically a **booster pack for training large models with PyTorch**. It handles memory, distributes work across GPUs, and keeps things from melting your hardware. 🧠⚡
+
+We’ll walk through the **minimal working setup** so the idea clicks.
+
+---
+
+## 1. Install DeepSpeed
+
+First step: install it.
+
+```bash
+pip install deepspeed
+```
+
+If CUDA and PyTorch are already installed, DeepSpeed compiles its optimizations automatically.
+
+Check it works:
+
+```bash
+deepspeed --version
+```
+
+---
+
+## 2. Simple PyTorch Training Script
+
+A normal PyTorch training script might look like this:
+
+```python
+import torch
+import torch.nn as nn
+
+model = nn.Linear(10, 1)
+
+optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+for step in range(100):
+    x = torch.randn(32, 10)
+    y = torch.randn(32, 1)
+
+    output = model(x)
+    loss = ((output - y) ** 2).mean()
+
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+```
+
+DeepSpeed wraps this training loop so it can **handle distributed training and memory optimization**.
+
+---
+
+## 3. Add DeepSpeed to the Script
+
+Modify the script like this:
+
+```python
+import torch
+import torch.nn as nn
+import deepspeed
+
+model = nn.Linear(10, 1)
+
+parameters = filter(lambda p: p.requires_grad, model.parameters())
+
+model_engine, optimizer, _, _ = deepspeed.initialize(
+    model=model,
+    model_parameters=parameters,
+    config="ds_config.json"
+)
+
+for step in range(100):
+    x = torch.randn(32, 10).to(model_engine.local_rank)
+    y = torch.randn(32, 1).to(model_engine.local_rank)
+
+    output = model_engine(x)
+    loss = ((output - y) ** 2).mean()
+
+    model_engine.backward(loss)
+    model_engine.step()
+```
+
+Notice the difference:
+
+Instead of
+`loss.backward()`
+you use
+
+```
+model_engine.backward(loss)
+```
+
+DeepSpeed now manages **gradient sync, memory, and distributed GPUs**.
+
+---
+
+## 4. Create the DeepSpeed Config
+
+DeepSpeed uses a JSON config file.
+
+`ds_config.json`
+
+```json
+{
+  "train_batch_size": 32,
+  "fp16": {
+    "enabled": true
+  },
+  "zero_optimization": {
+    "stage": 2
+  }
+}
+```
+
+Key parts:
+
+**fp16**
+Uses half precision to save memory.
+
+**zero_optimization**
+
+* stage 1 → optimizer states split
+* stage 2 → gradients split
+* stage 3 → full model partitioned
+
+Stage 3 is the **big guns for massive models**.
+
+---
+
+## 5. Run the Training
+
+Instead of running Python directly, you launch with DeepSpeed:
+
+```bash
+deepspeed train.py
+```
+
+Multi-GPU example:
+
+```bash
+deepspeed --num_gpus=4 train.py
+```
+
+Now your model trains **distributed across GPUs automatically**.
+
+---
+
+## 6. Example with Hugging Face Transformers
+
+DeepSpeed is heavily used with **Hugging Face Transformers**.
+
+Example training command:
+
+```bash
+deepspeed run_clm.py \
+  --model_name_or_path gpt2 \
+  --deepspeed ds_config.json
+```
+
+This is how people train **LLMs efficiently**.
+
+---
+
+## What DeepSpeed Is Really Doing Behind the Scenes
+
+When you enable ZeRO optimization, it:
+
+* splits model parameters across GPUs
+* shards gradients
+* shards optimizer states
+* optionally offloads memory to CPU or NVMe
+
+So your GPU doesn’t have to hold **the entire model at once**.
+
+That’s how researchers train **100B+ parameter models** without needing a ridiculous cluster.
+
+---
+
+## A Mental Model
+
+Think of training a huge neural network like moving a giant couch up a staircase.
+
+Without DeepSpeed:
+
+one person tries to carry the couch alone. 💀
+
+With DeepSpeed:
+
+four people lift different corners and move together. 🛋️
+
+Same couch.
+Way less suffering.
+
+---
+
+One spicy insight: DeepSpeed becomes **really powerful when models hit GPU memory limits**. That’s why it’s everywhere in LLM training.
+
+The next level trick is combining it with **LoRA fine-tuning**, which lets you train huge models on **a single GPU or even a laptop**. That combo is what a lot of modern AI hackers use. 🔬

ds_config.active.json

@@ -0,0 +1,58 @@
+{
+  "train_batch_size": 16,
+  "train_micro_batch_size_per_gpu": 2,
+  "gradient_accumulation_steps": 8,
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": 0.00015,
+      "betas": [
+        0.9,
+        0.999
+      ],
+      "eps": 1e-08,
+      "weight_decay": 0.01,
+      "torch_adam": true
+    }
+  },
+  "scheduler": {
+    "type": "WarmupLR",
+    "params": {
+      "warmup_min_lr": 0,
+      "warmup_max_lr": 0.00015,
+      "warmup_num_steps": 500
+    }
+  },
+  "zero_optimization": {
+    "stage": 2,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "overlap_comm": true,
+    "contiguous_gradients": true,
+    "reduce_bucket_size": 2000000.0,
+    "gather_16bit_weights_on_model_save": false
+  },
+  "bf16": {
+    "enabled": true
+  },
+  "gradient_clipping": 1.0,
+  "activation_checkpointing": {
+    "partition_activations": true,
+    "contiguous_memory_optimization": true,
+    "number_checkpoints": 12,
+    "synchronize_checkpoint_boundary": false,
+    "cpu_checkpointing": false
+  },
+  "wall_clock_breakdown": false,
+  "steps_per_print": 100,
+  "fp16": {
+    "enabled": false
+  },
+  "amp": {
+    "enabled": false,
+    "amp_master_weights": false,
+    "loss_scale_window": 1000
+  }
+}

ds_config.json

@@ -0,0 +1,65 @@
+{
+  "train_batch_size": 4,
+  "train_micro_batch_size_per_gpu": 1,
+  "gradient_accumulation_steps": 4,
+  
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": 1.5e-4,
+      "betas": [0.9, 0.999],
+      "eps": 1e-8,
+      "weight_decay": 0.01,
+      "torch_adam": true
+    }
+  },
+  
+  "scheduler": {
+    "type": "WarmupLR",
+    "params": {
+      "warmup_min_lr": 0,
+      "warmup_max_lr": 1.5e-4,
+      "warmup_num_steps": 500
+    }
+  },
+  
+  "zero_optimization": {
+    "stage": 2,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": false
+    },
+    "overlap_comm": true,
+    "contiguous_gradients": true,
+    "reduce_bucket_size": 1e6,
+    "gather_16bit_weights_on_model_save": false
+  },
+  
+  "bf16": {
+    "enabled": true
+  },
+  
+  "gradient_clipping": 1.0,
+  
+  "activation_checkpointing": {
+    "partition_activations": true,
+    "contiguous_memory_optimization": true,
+    "number_checkpoints": 12,
+    "synchronize_checkpoint_boundary": false,
+    "cpu_checkpointing": true
+  },
+  
+  "wall_clock_breakdown": false,
+  
+  "steps_per_print": 100,
+  
+  "fp16": {
+    "enabled": false
+  },
+  
+  "amp": {
+    "enabled": false,
+    "amp_master_weights": false,
+    "loss_scale_window": 1000
+  }
+}

main.py

@@ -0,0 +1,677 @@
+"""
+MoE GPT – 0.5 Billion Parameter Language Model
+================================================
+Mixture-of-Experts GPT trained on WikiText-2.
+Fits on GPUs with as little as 4 GB VRAM via:
+  - FP16 model weights on GPU  (~1 GB)
+  - CPU-offloaded AdamW         (optimizer states on RAM, not VRAM)
+  - Gradient checkpointing      (recompute activations to save memory)
+
+Architecture
+  12 Transformer layers  ×  (12-head attention  +  MoE FFN)
+  8 expert FFNs per layer, top-2 routing
+  Total params  ≈ 521 M   |   Active per token  ≈ 180 M
+
+Run order:
+    pip install torch tiktoken numpy datasets
+    python prepare_data.py          # once — downloads WikiText-2
+    python main.py                  # train + generate
+"""
+
+import os
+import math
+import gc
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.checkpoint import checkpoint as grad_checkpoint
+import tiktoken
+from rich.progress import (
+    Progress, BarColumn, TextColumn, TimeRemainingColumn, TimeElapsedColumn,
+    SpinnerColumn, MofNCompleteColumn,
+)
+from rich.console import Console
+from rich.table import Table
+from rich import print as rprint
+
+console = Console()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 1. LOAD DATA  (memory-mapped .bin files from prepare_data.py)
+# ═════════════════════════════════════════════════════════════════════════════
+
+DATA_DIR = "data"
+for split in ("train", "val", "test"):
+    path = os.path.join(DATA_DIR, f"{split}.bin")
+    if not os.path.exists(path):
+        raise FileNotFoundError(
+            f"\n[ERROR] '{path}' not found.\n"
+            "Run  python prepare_data.py  first."
+        )
+
+train_data = np.memmap(os.path.join(DATA_DIR, "train.bin"), dtype=np.uint16, mode="r")
+val_data   = np.memmap(os.path.join(DATA_DIR, "val.bin"),   dtype=np.uint16, mode="r")
+test_data  = np.memmap(os.path.join(DATA_DIR, "test.bin"),  dtype=np.uint16, mode="r")
+
+print("Dataset loaded (memory-mapped)")
+print(f"  Train : {len(train_data):>12,} tokens")
+print(f"  Val   : {len(val_data):>12,} tokens")
+print(f"  Test  : {len(test_data):>12,} tokens")
+print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 2. TOKENISER – GPT-2 BPE  (matches prepare_data.py)
+# ═════════════════════════════════════════════════════════════════════════════
+
+enc        = tiktoken.get_encoding("gpt2")
+vocab_size = enc.n_vocab                      # 50 257
+
+def encode(text: str) -> list:
+    return enc.encode_ordinary(text)
+
+def decode(ids: list) -> str:
+    return enc.decode(ids)
+
+print(f"Tokeniser : GPT-2 BPE  (vocab {vocab_size:,})")
+print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 3. HYPERPARAMETERS
+# ═════════════════════════════════════════════════════════════════════════════
+
+BLOCK_SIZE    = 128              # context window (tokens)
+MICRO_BATCH   = 2                # samples per GPU forward pass (tiny for VRAM)
+GRAD_ACCUM    = 8                # accumulate before optimizer step → eff. batch 16
+EMBED_DIM     = 768              # model width
+NUM_HEADS     = 12               # attention heads
+NUM_LAYERS    = 12               # transformer blocks
+NUM_EXPERTS   = 8                # expert FFNs per MoE layer
+TOP_K         = 2                # experts activated per token
+FFN_DIM       = EMBED_DIM * 4   # 3 072  (expert hidden dim)
+DROPOUT       = 0.1
+LR            = 1.5e-4           # peak learning rate (reduced from 3e-4 to prevent NaN)
+WARMUP_STEPS  = 500              # increased warmup for stability
+MAX_ITERS     = 10000            # extended training to 10k steps
+EVAL_EVERY    = 500
+EVAL_ITERS    = 50
+AUX_LOSS_W    = 0.01             # load-balancing auxiliary loss weight
+GRAD_CLIP     = 1.0
+CHECKPOINT_DIR = "checkpoints"   # directory for saving checkpoints
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE  = torch.bfloat16 if DEVICE == "cuda" else torch.float32
+# bfloat16: same exponent range as fp32 — no overflow/NaN, no GradScaler needed.
+# float16 caused NaN because it overflows at 65504.
+
+print(f"Device          : {DEVICE.upper()}")
+print(f"Precision       : {'BF16 + CPU-offload optimizer' if DTYPE == torch.bfloat16 else 'FP32'}")
+print(f"Effective batch : {MICRO_BATCH * GRAD_ACCUM}")
+print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 4. DATA LOADER
+# ═════════════════════════════════════════════════════════════════════════════
+
+def get_batch(split="train"):
+    data = {"train": train_data, "val": val_data, "test": test_data}[split]
+    ix = np.random.randint(0, len(data) - BLOCK_SIZE, size=(MICRO_BATCH,))
+    x = np.stack([data[i   : i + BLOCK_SIZE    ].astype(np.int64) for i in ix])
+    y = np.stack([data[i+1 : i + BLOCK_SIZE + 1].astype(np.int64) for i in ix])
+    return torch.from_numpy(x).to(DEVICE), torch.from_numpy(y).to(DEVICE)
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 5. MODEL — Mixture-of-Experts GPT  (~0.5 B params)
+# ═════════════════════════════════════════════════════════════════════════════
+
+class CausalSelfAttention(nn.Module):
+    """Multi-head causal self-attention with fused QKV projection."""
+
+    def __init__(self):
+        super().__init__()
+        self.n_heads  = NUM_HEADS
+        self.head_dim = EMBED_DIM // NUM_HEADS
+        self.qkv      = nn.Linear(EMBED_DIM, 3 * EMBED_DIM, bias=False)
+        self.proj      = nn.Linear(EMBED_DIM, EMBED_DIM, bias=False)
+        self.attn_drop = nn.Dropout(DROPOUT)
+        self.proj_drop = nn.Dropout(DROPOUT)
+        self.register_buffer(
+            "mask",
+            torch.tril(torch.ones(BLOCK_SIZE, BLOCK_SIZE))
+                .view(1, 1, BLOCK_SIZE, BLOCK_SIZE),
+        )
+
+    def forward(self, x):
+        B, T, C = x.shape
+        qkv = self.qkv(x).reshape(B, T, 3, self.n_heads, self.head_dim)
+        q, k, v = qkv.permute(2, 0, 3, 1, 4)          # each (B, H, T, D)
+
+        att = (q @ k.transpose(-2, -1)) * (self.head_dim ** -0.5)
+        att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float("-inf"))
+        att = F.softmax(att.float(), dim=-1).to(x.dtype)   # softmax in fp32
+        att = self.attn_drop(att)
+
+        out = (att @ v).transpose(1, 2).reshape(B, T, C)
+        return self.proj_drop(self.proj(out))
+
+
+class ExpertFFN(nn.Module):
+    """Single expert: two-layer FFN with GELU."""
+
+    def __init__(self):
+        super().__init__()
+        self.w1   = nn.Linear(EMBED_DIM, FFN_DIM)
+        self.w2   = nn.Linear(FFN_DIM, EMBED_DIM)
+        self.act  = nn.GELU()
+        self.drop = nn.Dropout(DROPOUT)
+
+    def forward(self, x):
+        return self.drop(self.w2(self.act(self.w1(x))))
+
+
+class MoELayer(nn.Module):
+    """
+    Mixture-of-Experts: routes each token to TOP_K of NUM_EXPERTS FFNs.
+    Includes Switch-Transformer-style load-balancing auxiliary loss.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.router  = nn.Linear(EMBED_DIM, NUM_EXPERTS, bias=False)
+        self.experts = nn.ModuleList([ExpertFFN() for _ in range(NUM_EXPERTS)])
+
+    def forward(self, x):
+        B, T, C = x.shape
+        flat = x.reshape(-1, C)                              # (N, C)
+        N = flat.shape[0]
+
+        # ── routing ──
+        logits = self.router(flat)                            # (N, E)
+        probs  = F.softmax(logits.float(), dim=-1)            # fp32 for stability
+
+        top_w, top_i = torch.topk(probs, TOP_K, dim=-1)      # (N, K)
+        top_w = (top_w / top_w.sum(dim=-1, keepdim=True)).to(x.dtype)
+
+        # ── load-balancing loss ──
+        one_hot = F.one_hot(top_i, NUM_EXPERTS).float().sum(dim=1)   # (N, E)
+        f = one_hot.mean(dim=0)
+        P = probs.mean(dim=0)
+        aux_loss = NUM_EXPERTS * (f * P).sum()
+
+        # ── dispatch to experts ──
+        out = torch.zeros_like(flat)
+        for i, expert in enumerate(self.experts):
+            mask = (top_i == i).any(dim=-1)                   # (N,)
+            if not mask.any():
+                continue
+            tokens  = flat[mask]                              # (n_i, C)
+            e_out   = expert(tokens)                          # (n_i, C)
+            match   = (top_i[mask] == i).to(x.dtype)          # (n_i, K)
+            weights = (top_w[mask] * match).sum(-1, keepdim=True)
+            out[mask] += weights * e_out
+
+        return out.reshape(B, T, C), aux_loss
+
+
+class TransformerBlock(nn.Module):
+    """Pre-norm Transformer block: Attention + MoE, with residuals."""
+
+    def __init__(self):
+        super().__init__()
+        self.ln1  = nn.LayerNorm(EMBED_DIM)
+        self.attn = CausalSelfAttention()
+        self.ln2  = nn.LayerNorm(EMBED_DIM)
+        self.moe  = MoELayer()
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        moe_out, aux = self.moe(self.ln2(x))
+        x = x + moe_out
+        return x, aux
+
+
+class MoEGPT(nn.Module):
+    """
+    Full MoE-GPT model (~521 M parameters, ~180 M active per token).
+
+    1. Token + positional embeddings
+    2. 12 × Transformer blocks  (self-attention + MoE FFN)
+    3. Final layer-norm → linear head (weight-tied with token embedding)
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.tok_emb = nn.Embedding(vocab_size, EMBED_DIM)
+        self.pos_emb = nn.Embedding(BLOCK_SIZE, EMBED_DIM)
+        self.drop    = nn.Dropout(DROPOUT)
+        self.blocks  = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f    = nn.LayerNorm(EMBED_DIM)
+        self.head    = nn.Linear(EMBED_DIM, vocab_size, bias=False)
+
+        # Weight tying saves ~38 M params and improves training
+        self.head.weight = self.tok_emb.weight
+        self._init_weights()
+
+    def _init_weights(self):
+        """GPT-2-style init with scaled residual projections."""
+        for name, p in self.named_parameters():
+            if p.dim() >= 2:
+                nn.init.normal_(p, mean=0.0, std=0.02)
+            elif "bias" in name:
+                nn.init.zeros_(p)
+        scale = (2 * NUM_LAYERS) ** -0.5
+        for block in self.blocks:
+            nn.init.normal_(block.attn.proj.weight, mean=0.0, std=0.02 * scale)
+            for expert in block.moe.experts:
+                nn.init.normal_(expert.w2.weight, mean=0.0, std=0.02 * scale)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        x = self.drop(
+            self.tok_emb(idx) + self.pos_emb(torch.arange(T, device=idx.device))
+        )
+
+        total_aux = 0.0
+        for block in self.blocks:
+            if self.training:
+                x, aux = grad_checkpoint(block, x, use_reentrant=False)
+            else:
+                x, aux = block(x)
+            total_aux = total_aux + aux
+
+        logits = self.head(self.ln_f(x))
+
+        loss = None
+        if targets is not None:
+            ce   = F.cross_entropy(logits.view(-1, vocab_size), targets.view(-1))
+            loss = ce + AUX_LOSS_W * total_aux
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, prompt: str, max_new_tokens=200, temperature=0.8):
+        self.eval()
+        ids = encode(prompt)
+        idx = torch.tensor([ids], dtype=torch.long, device=DEVICE)
+
+        for _ in range(max_new_tokens):
+            ctx = idx[:, -BLOCK_SIZE:]
+            logits, _ = self(ctx)
+            logits = logits[:, -1, :].float() / temperature
+            probs  = F.softmax(logits, dim=-1)
+            nxt    = torch.multinomial(probs, 1)
+            idx    = torch.cat([idx, nxt], dim=1)
+
+        self.train()
+        return decode(idx[0].tolist())
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 6. CPU-OFFLOAD OPTIMIZER
+#    Hand-rolled AdamW with fp32 master weights but fp16 momentum/variance.
+#    Saves ~2 GB CPU RAM compared to using torch.optim.AdamW (all fp32).
+#    GPU VRAM cost ≈ 1 GB  (only fp16 model weights + grads).
+#
+#    Memory breakdown for 520 M params:
+#      fp32 master weights : ~2.0 GB
+#      fp16 momentum       : ~1.0 GB
+#      fp16 variance       : ~1.0 GB
+#      Total CPU RAM       : ~4.1 GB  (was ~6.2 GB with all-fp32 AdamW)
+# ═════════════════════════════════════════════════════════════════════════════
+
+class CPUOffloadAdamW:
+    """
+    AdamW with ALL state (master weights, momentum, variance) in fp32 on CPU.
+    fp16 m/v was the culprit for NaN — Adam variance accumulates squared
+    gradients that easily exceed fp16 max (65504) → overflow → NaN.
+    GPU holds only fp16 model weights + fp16 gradients (~1 GB VRAM).
+    CPU RAM: fp32 master(2 GB) + fp32 m(2 GB) + fp32 v(2 GB) ≈ 6.2 GB.
+    Expose param_groups so torch.amp.GradScaler.unscale_() works correctly.
+    """
+
+    def __init__(self, gpu_params, lr=3e-4, betas=(0.9, 0.999),
+                 eps=1e-8, weight_decay=0.01):
+        self.gpu_params = list(gpu_params)
+        self.lr = lr
+        self.beta1, self.beta2 = betas
+        self.eps = eps
+        self.wd = weight_decay
+        self.t = 0
+
+        # fp32 master copies + fp32 momentum/variance on CPU
+        self.master = [p.data.float().cpu() for p in self.gpu_params]
+        self.m = [torch.zeros_like(mp) for mp in self.master]   # fp32
+        self.v = [torch.zeros_like(mp) for mp in self.master]   # fp32
+
+        # GradScaler compatibility: unscale_() iterates param_groups
+        self.param_groups = [{"params": self.gpu_params}]
+
+    def step(self):
+        self.t += 1
+        bc1 = 1.0 - self.beta1 ** self.t
+        bc2 = 1.0 - self.beta2 ** self.t
+
+        for i, gp in enumerate(self.gpu_params):
+            if gp.grad is None:
+                continue
+            g = gp.grad.data.float().cpu()   # fp16 grad → fp32
+
+            # Decoupled weight decay
+            self.master[i].mul_(1.0 - self.lr * self.wd)
+
+            # Adam moments (all fp32 — no overflow risk)
+            self.m[i].mul_(self.beta1).add_(g, alpha=1.0 - self.beta1)
+            self.v[i].mul_(self.beta2).addcmul_(g, g, value=1.0 - self.beta2)
+
+            # Bias-corrected parameter update
+            self.master[i].addcdiv_(
+                self.m[i] / bc1,
+                (self.v[i] / bc2).sqrt_().add_(self.eps),
+                value=-self.lr,
+            )
+
+            # Push updated fp32 weights → GPU fp16
+            gp.data.copy_(self.master[i])
+
+    def zero_grad(self):
+        for gp in self.gpu_params:
+            gp.grad = None
+
+    def set_lr(self, lr):
+        self.lr = lr
+
+    def state_dict(self):
+        return {"t": self.t, "master": self.master, "m": self.m, "v": self.v}
+
+    def load_state_dict(self, sd):
+        self.t = sd["t"]
+        self.master = sd["master"]
+        self.m = sd["m"]
+        self.v = sd["v"]
+        for gp, mp in zip(self.gpu_params, self.master):
+            gp.data.copy_(mp.data)
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 7. CHECKPOINT HELPERS
+# ═════════════════════════════════════════════════════════════════════════════
+
+os.makedirs(CHECKPOINT_DIR, exist_ok=True)
+
+def save_checkpoint(step, model, optimizer, train_loss, val_loss, path):
+    """Save model + optimizer + training state to disk."""
+    torch.save({
+        "step":        step,
+        "model":       model.state_dict(),
+        "optimizer":   optimizer.state_dict(),
+        "train_loss":  train_loss,
+        "val_loss":    val_loss,
+    }, path)
+
+def load_checkpoint(path, model, optimizer):
+    """Load checkpoint and return the step to resume from."""
+    ckpt = torch.load(path, map_location="cpu", weights_only=False)
+    model.load_state_dict(ckpt["model"])
+    optimizer.load_state_dict(ckpt["optimizer"])
+    print(f"  Resumed from step {ckpt['step']}  "
+          f"(train {ckpt['train_loss']:.4f}, val {ckpt['val_loss']:.4f})")
+    return ckpt["step"], ckpt["val_loss"]
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 8. LEARNING-RATE SCHEDULE  (linear warmup → cosine decay to 10 %)
+# ═════════════════════════════════════════════════════════════════════════════
+
+def get_lr(step):
+    if step < WARMUP_STEPS:
+        return LR * step / WARMUP_STEPS
+    progress = (step - WARMUP_STEPS) / max(1, MAX_ITERS - WARMUP_STEPS)
+    return LR * 0.1 + 0.5 * LR * 0.9 * (1 + math.cos(math.pi * progress))
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 9. LOSS ESTIMATION
+# ═════════════════════════════════════════════════════════════════════════════
+
+@torch.no_grad()
+def estimate_loss():
+    model.eval()
+    out = {}
+    for split in ("train", "val"):
+        losses = []
+        for _ in range(EVAL_ITERS):
+            x, y = get_batch(split)
+            _, loss = model(x, y)
+            losses.append(loss.item())
+        out[split] = sum(losses) / len(losses)
+    model.train()
+    return out
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 10. INSTANTIATE MODEL + OPTIMIZER
+# ═════════════════════════════════════════════════════���═══════════════════════
+
+if DEVICE == "cuda":
+    torch.cuda.empty_cache()
+
+# ── Delete any NaN-poisoned checkpoints before loading ──
+_nan_guard = os.path.join(CHECKPOINT_DIR, "latest.pt")
+if os.path.exists(_nan_guard):
+    try:
+        _c = torch.load(_nan_guard, map_location="cpu", weights_only=False)
+        if _c.get("val_loss") != _c.get("val_loss"):  # nan != nan
+            os.remove(_nan_guard)
+            _best = os.path.join(CHECKPOINT_DIR, "best.pt")
+            if os.path.exists(_best):
+                os.remove(_best)
+            print("[yellow]NaN checkpoint detected and removed — starting fresh.[/yellow]")
+    except Exception:
+        pass
+
+model = MoEGPT()
+n_total  = sum(p.numel() for p in model.parameters())
+_expert1 = sum(p.numel() for p in model.blocks[0].moe.experts[0].parameters())
+n_active = n_total - _expert1 * (NUM_EXPERTS - TOP_K) * NUM_LAYERS
+
+# Move to GPU in fp16  (or stay fp32 on CPU)
+model = model.to(dtype=DTYPE, device=DEVICE)
+gc.collect()
+if DEVICE == "cuda":
+    torch.cuda.empty_cache()
+    vram_used = torch.cuda.memory_allocated() / 1024**3
+    print(f"GPU VRAM used   : {vram_used:.2f} GiB  (model weights)")
+
+if DEVICE == "cuda":
+    # Initialize optimizer AFTER config changes so it uses the new LR
+    optimizer = CPUOffloadAdamW(model.parameters(), lr=LR)
+    gc.collect()
+    opt_gb = n_total * 4 * 3 / 1024**3   # fp32 master + fp32 m + fp32 v
+    print(f"CPU RAM for opt : ~{opt_gb:.1f} GiB  (fp32 master + fp32 m + fp32 v)")
+else:
+    _inner = torch.optim.AdamW(model.parameters(), lr=LR)
+    class _Wrap:
+        def __init__(self, o): self.opt = o
+        def step(self):       self.opt.step()
+        def zero_grad(self):  self.opt.zero_grad(set_to_none=True)
+        def set_lr(self, lr):
+            for pg in self.opt.param_groups: pg["lr"] = lr
+        def state_dict(self):       return self.opt.state_dict()
+        def load_state_dict(self, sd): self.opt.load_state_dict(sd)
+    optimizer = _Wrap(_inner)
+
+print(f"Total  parameters : {n_total:>14,}")
+print(f"Active per token  : {n_active:>14,}")
+print()
+
+# ── Auto-resume from latest checkpoint ──
+RESUME = True  # Automatically resume from latest.pt if it exists
+start_step = 0
+best_val   = float("inf")
+latest_ckpt = os.path.join(CHECKPOINT_DIR, "latest.pt")
+if RESUME and os.path.exists(latest_ckpt):
+    try:
+        _c = torch.load(latest_ckpt, map_location="cpu", weights_only=False)
+        # Skip NaN-poisoned checkpoints
+        if _c.get("val_loss") != _c.get("val_loss") or _c.get("train_loss") != _c.get("train_loss"):
+            print("Checkpoint has NaN losses — deleting and starting fresh")
+            os.remove(latest_ckpt)
+        else:
+            print("Checkpoint found — resuming …")
+            start_step, best_val = load_checkpoint(latest_ckpt, model, optimizer)
+            print()
+    except Exception as e:
+        print(f"Checkpoint corrupted ({e}) — starting fresh")
+else:
+    if RESUME:
+        print("No checkpoint found — starting fresh training")
+    print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 11. TRAINING LOOP
+# ═════════════════════════════════════════════════════════════════════════════
+
+console.rule("[bold green]Training started")
+print()
+
+with Progress(
+    SpinnerColumn(),
+    TextColumn("[bold blue]{task.description}"),
+    BarColumn(bar_width=30),
+    MofNCompleteColumn(),
+    TextColumn("•"),
+    TimeElapsedColumn(),
+    TextColumn("•"),
+    TimeRemainingColumn(),
+    TextColumn("•"),
+    TextColumn("[yellow]loss {task.fields[train_loss]}"),
+    TextColumn("[cyan]val {task.fields[val_loss]}"),
+    TextColumn("[magenta]lr {task.fields[lr]}"),
+    console=console,
+    refresh_per_second=4,
+) as progress:
+    total_steps = MAX_ITERS - start_step
+    task = progress.add_task(
+        "Training", total=total_steps,
+        train_loss="--.----", val_loss="--.----", lr="--.------",
+    )
+
+    for step in range(start_step + 1, MAX_ITERS + 1):
+
+        lr = get_lr(step)
+        optimizer.set_lr(lr)
+
+        optimizer.zero_grad()
+        accum_loss = 0.0
+
+        for _ in range(GRAD_ACCUM):
+            x, y = get_batch("train")
+            with torch.amp.autocast("cuda", dtype=torch.bfloat16,
+                                    enabled=(DTYPE == torch.bfloat16)):
+                _, loss = model(x, y)
+            (loss / GRAD_ACCUM).backward()
+            accum_loss += loss.item() / GRAD_ACCUM
+
+        # Gradient clipping with stricter threshold to prevent explosion
+        norm_before = torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
+        if norm_before > GRAD_CLIP:
+            progress.console.print(f"  [yellow]Gradient norm clipped: {norm_before:.2f} → {GRAD_CLIP}[/]", style="dim")
+        optimizer.step()
+
+        progress.update(
+            task, advance=1,
+            train_loss=f"{accum_loss:.4f}", lr=f"{lr:.6f}",
+        )
+
+        if step % EVAL_EVERY == 0 or step == 1:
+            losses = estimate_loss()
+            progress.update(
+                task,
+                train_loss=f"{losses['train']:.4f}",
+                val_loss=f"{losses['val']:.4f}",
+                lr=f"{lr:.6f}",
+            )
+            progress.console.print(
+                f"  [bold]Step {step:>5}[/]  │  "
+                f"[yellow]Train {losses['train']:.4f}[/]  │  "
+                f"[cyan]Val {losses['val']:.4f}[/]  │  "
+                f"[magenta]LR {lr:.6f}[/]"
+            )
+
+            # ── Save checkpoints ──
+            save_checkpoint(
+                step, model, optimizer,
+                losses["train"], losses["val"],
+                os.path.join(CHECKPOINT_DIR, "latest.pt"),
+            )
+            if losses["val"] < best_val:
+                best_val = losses["val"]
+                save_checkpoint(
+                    step, model, optimizer,
+                    losses["train"], losses["val"],
+                    os.path.join(CHECKPOINT_DIR, "best.pt"),
+                )
+                progress.console.print(
+                    f"  [bold green]★ New best val loss: {best_val:.4f}  (saved best.pt)[/]"
+                )
+
+print()
+console.rule("[bold green]Training complete")
+print()
+
+# ── Load best checkpoint for final evaluation ──
+best_ckpt = os.path.join(CHECKPOINT_DIR, "best.pt")
+if os.path.exists(best_ckpt):
+    print("Loading best checkpoint for evaluation …")
+    load_checkpoint(best_ckpt, model, optimizer)
+    print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 12. TEST EVALUATION
+# ═════════════════════════════════════════════════════════════════════════════
+
+model.eval()
+test_losses = []
+with torch.no_grad():
+    for _ in range(EVAL_ITERS):
+        x, y = get_batch("test")
+        _, loss = model(x, y)
+        test_losses.append(loss.item())
+test_loss = sum(test_losses) / len(test_losses)
+print(f"Test loss : {test_loss:.4f}")
+print()
+model.train()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 13. TEXT GENERATION SAMPLES
+# ═════════════════════════════════════════════════════════════════════════════
+
+prompts = [
+    "The history of",
+    "Scientists have discovered",
+    "In the early twentieth century",
+]
+
+print("=" * 60)
+print("Generated Text Samples")
+print("=" * 60)
+
+for prompt in prompts:
+    output = model.generate(prompt, max_new_tokens=120, temperature=0.7)
+    print(f"\nPrompt : \"{prompt}\"")
+    print(f"Output : {output.strip()}")
+    print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 14. INTERACTIVE MODE
+# ═════════════════════════════════════════════════════════════════════════════
+
+print("=" * 60)
+print("Interactive Mode  (type 'quit' to exit)")
+print("=" * 60)
+
+while True:
+    try:
+        prompt = input("\nEnter a prompt: ").strip()
+    except (EOFError, KeyboardInterrupt):
+        break
+    if not prompt or prompt.lower() == "quit":
+        break
+    output = model.generate(prompt, max_new_tokens=150, temperature=0.8)
+    print(f"\n{output.strip()}")
+
+print("\nGoodbye!")

main_deepspeed.py

@@ -0,0 +1,696 @@
+"""
+MoE GPT – 0.5 Billion Parameter Language Model (DeepSpeed ZeRO-3)
+==================================================================
+Mixture-of-Experts GPT trained on FineWeb-Edu with DeepSpeed ZeRO-Infinity:
+  - ZeRO Stage 3: All states partitioned across GPUs + CPU RAM offload
+  - CPU Offloading: Parameters & optimizer states in CPU RAM
+  - Memory efficient: Fits massive models on limited VRAM
+  - Automatic gradient checkpointing & mixed precision (bfloat16)
+
+Architecture
+  12 Transformer layers  ×  (12-head attention  +  MoE FFN)
+  8 expert FFNs per layer, top-2 routing
+  Total params  ≈ 521 M   |   Active per token  ≈ 180 M
+
+Run order:
+    pip install torch tiktoken numpy datasets deepspeed
+    python prepare_data.py          # once — downloads FineWeb-Edu
+    deepspeed --num_gpus 1 main.py  # train with DeepSpeed
+    python run.py                   # generate
+"""
+
+import os
+import sys
+import math
+import gc
+import json
+from datetime import datetime, timedelta
+from zoneinfo import ZoneInfo
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.checkpoint import checkpoint as grad_checkpoint
+import tiktoken
+import deepspeed
+from rich.progress import (
+    Progress, BarColumn, TextColumn, TimeRemainingColumn, TimeElapsedColumn,
+    SpinnerColumn, MofNCompleteColumn,
+)
+from rich.console import Console
+
+console = Console()
+IST = ZoneInfo("Asia/Kolkata")
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 1. LOAD DATA  (memory-mapped .bin files from prepare_data.py)
+# ═════════════════════════════════════════════════════════════════════════════
+
+DATA_DIR = "data"
+for split in ("train", "val", "test"):
+    path = os.path.join(DATA_DIR, f"{split}.bin")
+    if not os.path.exists(path):
+        raise FileNotFoundError(
+            f"\n[ERROR] '{path}' not found.\n"
+            "Run  python prepare_data.py  first."
+        )
+
+train_data = np.memmap(os.path.join(DATA_DIR, "train.bin"), dtype=np.uint16, mode="r")
+val_data   = np.memmap(os.path.join(DATA_DIR, "val.bin"),   dtype=np.uint16, mode="r")
+test_data  = np.memmap(os.path.join(DATA_DIR, "test.bin"),  dtype=np.uint16, mode="r")
+
+print("Dataset loaded (memory-mapped)")
+print(f"  Train : {len(train_data):>12,} tokens")
+print(f"  Val   : {len(val_data):>12,} tokens")
+print(f"  Test  : {len(test_data):>12,} tokens")
+print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 2. TOKENISER – GPT-2 BPE  (matches prepare_data.py)
+# ═════════════════════════════════════════════════════════════════════════════
+
+enc        = tiktoken.get_encoding("gpt2")
+vocab_size = enc.n_vocab                      # 50 257
+
+def encode(text: str) -> list:
+    return enc.encode_ordinary(text)
+
+def decode(ids: list) -> str:
+    return enc.decode(ids)
+
+print(f"Tokeniser : GPT-2 BPE  (vocab {vocab_size:,})")
+print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 3. HYPERPARAMETERS
+# ═════════════════════════════════════════════════════════════════════════════
+
+BLOCK_SIZE    = 64               # context window (tokens)
+MICRO_BATCH   = 2                # samples per GPU forward pass (managed by DeepSpeed)
+GRAD_ACCUM    = 8                # accumulate before optimizer step → eff. batch 16
+EMBED_DIM     = 512              # model width
+NUM_HEADS     = 8                # attention heads
+NUM_LAYERS    = 8                # transformer blocks
+NUM_EXPERTS   = 4                # expert FFNs per MoE layer
+TOP_K         = 2                # experts activated per token
+FFN_DIM       = EMBED_DIM * 4   # 2 048  (expert hidden dim)
+DROPOUT       = 0.1
+LR            = 1.5e-4           # peak learning rate
+WARMUP_STEPS  = 500              # linear warmup
+MAX_ITERS     = 50000            # total optimiser steps
+EVAL_EVERY    = 100
+EVAL_ITERS    = 50
+AUX_LOSS_W    = 0.01             # load-balancing auxiliary loss weight
+GRAD_CLIP     = 1.0
+CHECKPOINT_DIR = "checkpoints"   # directory for saving checkpoints
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE  = torch.bfloat16 if DEVICE == "cuda" else torch.float32
+
+ALLOW_TF32 = os.environ.get("ALLOW_TF32", "1") == "1"
+USE_TORCH_COMPILE = os.environ.get("USE_TORCH_COMPILE", "0") == "1"
+USE_ACTIVATION_CHECKPOINT = os.environ.get("USE_ACTIVATION_CHECKPOINT", "1") == "1"
+
+if DEVICE == "cuda":
+    # Throughput-oriented CUDA settings.
+    torch.backends.cudnn.benchmark = True
+    torch.backends.cuda.matmul.allow_tf32 = ALLOW_TF32
+    torch.backends.cudnn.allow_tf32 = ALLOW_TF32
+    torch.set_float32_matmul_precision("high")
+
+print(f"Device          : {DEVICE.upper()}")
+print(f"Precision       : {'BF16 (DeepSpeed)' if DTYPE == torch.bfloat16 else 'FP32'}")
+print(f"Effective batch (default) : {MICRO_BATCH * GRAD_ACCUM}")
+print(f"TF32            : {'ON' if (DEVICE == 'cuda' and ALLOW_TF32) else 'OFF'}")
+print(f"Torch compile   : {'ON' if USE_TORCH_COMPILE else 'OFF'}")
+print(f"Act checkpoint  : {'ON' if USE_ACTIVATION_CHECKPOINT else 'OFF'}")
+print()
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 4. DATA LOADER
+# ═════════════════════════════════════════════════════════════════════════════
+
+def get_batch(split="train"):
+    data = {"train": train_data, "val": val_data, "test": test_data}[split]
+    ix = np.random.randint(0, len(data) - BLOCK_SIZE, size=(MICRO_BATCH,))
+    x = np.stack([data[i   : i + BLOCK_SIZE    ].astype(np.int64) for i in ix])
+    y = np.stack([data[i+1 : i + BLOCK_SIZE + 1].astype(np.int64) for i in ix])
+    return torch.from_numpy(x).to(DEVICE), torch.from_numpy(y).to(DEVICE)
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 5. MODEL — Mixture-of-Experts GPT  (~0.5 B params)
+# ═════════════════════════════════════════════════════════════════════════════
+
+class CausalSelfAttention(nn.Module):
+    """Multi-head causal self-attention with fused QKV projection."""
+
+    def __init__(self):
+        super().__init__()
+        self.n_heads  = NUM_HEADS
+        self.head_dim = EMBED_DIM // NUM_HEADS
+        self.qkv      = nn.Linear(EMBED_DIM, 3 * EMBED_DIM, bias=False)
+        self.proj      = nn.Linear(EMBED_DIM, EMBED_DIM, bias=False)
+        self.attn_drop = nn.Dropout(DROPOUT)
+        self.proj_drop = nn.Dropout(DROPOUT)
+        self.register_buffer(
+            "mask",
+            torch.tril(torch.ones(BLOCK_SIZE, BLOCK_SIZE))
+                .view(1, 1, BLOCK_SIZE, BLOCK_SIZE),
+        )
+
+    def forward(self, x):
+        B, T, C = x.shape
+        qkv = self.qkv(x).reshape(B, T, 3, self.n_heads, self.head_dim)
+        q, k, v = qkv.permute(2, 0, 3, 1, 4)          # each (B, H, T, D)
+
+        att = (q @ k.transpose(-2, -1)) * (self.head_dim ** -0.5)
+        att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float("-inf"))
+        att = F.softmax(att.float(), dim=-1).to(x.dtype)   # softmax in fp32
+        att = self.attn_drop(att)
+
+        out = (att @ v).transpose(1, 2).reshape(B, T, C)
+        return self.proj_drop(self.proj(out))
+
+
+class ExpertFFN(nn.Module):
+    """Single expert: two-layer FFN with GELU."""
+
+    def __init__(self):
+        super().__init__()
+        self.w1   = nn.Linear(EMBED_DIM, FFN_DIM)
+        self.w2   = nn.Linear(FFN_DIM, EMBED_DIM)
+        self.act  = nn.GELU()
+        self.drop = nn.Dropout(DROPOUT)
+
+    def forward(self, x):
+        return self.drop(self.w2(self.act(self.w1(x))))
+
+
+class MoELayer(nn.Module):
+    """
+    Mixture-of-Experts: routes each token to TOP_K of NUM_EXPERTS FFNs.
+    Includes Switch-Transformer-style load-balancing auxiliary loss.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.router  = nn.Linear(EMBED_DIM, NUM_EXPERTS, bias=False)
+        self.experts = nn.ModuleList([ExpertFFN() for _ in range(NUM_EXPERTS)])
+
+    def forward(self, x):
+        B, T, C = x.shape
+        flat = x.reshape(-1, C)                              # (N, C)
+        N = flat.shape[0]
+
+        # ── routing ──
+        logits = self.router(flat)                            # (N, E)
+        probs  = F.softmax(logits.float(), dim=-1)            # fp32 for stability
+
+        top_w, top_i = torch.topk(probs, TOP_K, dim=-1)      # (N, K)
+        top_w = (top_w / top_w.sum(dim=-1, keepdim=True)).to(x.dtype)
+
+        # ── load-balancing loss ──
+        one_hot = F.one_hot(top_i, NUM_EXPERTS).float().sum(dim=1)   # (N, E)
+        f = one_hot.mean(dim=0)
+        P = probs.mean(dim=0)
+        aux_loss = NUM_EXPERTS * (f * P).sum()
+
+        # ── dispatch to experts ──
+        out = torch.zeros_like(flat)
+        for i, expert in enumerate(self.experts):
+            mask = (top_i == i).any(dim=-1)                   # (N,)
+            if not mask.any():
+                continue
+            tokens  = flat[mask]                              # (n_i, C)
+            e_out   = expert(tokens)                          # (n_i, C)
+            match   = (top_i[mask] == i).to(x.dtype)          # (n_i, K)
+            weights = (top_w[mask] * match).sum(-1, keepdim=True)
+            out[mask] += weights * e_out
+
+        return out.reshape(B, T, C), aux_loss
+
+
+class TransformerBlock(nn.Module):
+    """Pre-norm Transformer block: Attention + MoE, with residuals."""
+
+    def __init__(self):
+        super().__init__()
+        self.ln1  = nn.LayerNorm(EMBED_DIM)
+        self.attn = CausalSelfAttention()
+        self.ln2  = nn.LayerNorm(EMBED_DIM)
+        self.moe  = MoELayer()
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        moe_out, aux = self.moe(self.ln2(x))
+        x = x + moe_out
+        return x, aux
+
+
+class MoEGPT(nn.Module):
+    """
+    Full MoE-GPT model (~521 M parameters, ~180 M active per token).
+
+    1. Token + positional embeddings
+    2. 12 × Transformer blocks  (self-attention + MoE FFN)
+    3. Final layer-norm → linear head (weight-tied with token embedding)
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.tok_emb = nn.Embedding(vocab_size, EMBED_DIM)
+        self.pos_emb = nn.Embedding(BLOCK_SIZE, EMBED_DIM)
+        self.drop    = nn.Dropout(DROPOUT)
+        self.blocks  = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f    = nn.LayerNorm(EMBED_DIM)
+        self.head    = nn.Linear(EMBED_DIM, vocab_size, bias=False)
+
+        # Weight tying saves ~38 M params and improves training
+        self.head.weight = self.tok_emb.weight
+        self._init_weights()
+
+    def _init_weights(self):
+        """GPT-2-style init with scaled residual projections."""
+        for name, p in self.named_parameters():
+            if p.dim() >= 2:
+                nn.init.normal_(p, mean=0.0, std=0.02)
+            elif "bias" in name:
+                nn.init.zeros_(p)
+        scale = (2 * NUM_LAYERS) ** -0.5
+        for block in self.blocks:
+            nn.init.normal_(block.attn.proj.weight, mean=0.0, std=0.02 * scale)
+            for expert in block.moe.experts:
+                nn.init.normal_(expert.w2.weight, mean=0.0, std=0.02 * scale)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        x = self.drop(
+            self.tok_emb(idx) + self.pos_emb(torch.arange(T, device=idx.device))
+        )
+
+        total_aux = 0.0
+        for block in self.blocks:
+            if self.training and USE_ACTIVATION_CHECKPOINT:
+                x, aux = grad_checkpoint(block, x, use_reentrant=False)
+            else:
+                x, aux = block(x)
+            total_aux = total_aux + aux
+
+        logits = self.head(self.ln_f(x))
+
+        loss = None
+        if targets is not None:
+            ce   = F.cross_entropy(logits.view(-1, vocab_size), targets.view(-1))
+            loss = ce + AUX_LOSS_W * total_aux
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, prompt: str, max_new_tokens=200, temperature=0.8):
+        self.eval()
+        ids = encode(prompt)
+        idx = torch.tensor([ids], dtype=torch.long, device=DEVICE)
+
+        for _ in range(max_new_tokens):
+            ctx = idx[:, -BLOCK_SIZE:]
+            logits, _ = self(ctx)
+            logits = logits[:, -1, :].float() / temperature
+            probs  = F.softmax(logits, dim=-1)
+            nxt    = torch.multinomial(probs, 1)
+            idx    = torch.cat([idx, nxt], dim=1)
+
+        self.train()
+        return decode(idx[0].tolist())
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 6. CHECKPOINT HELPERS
+# ═════════════════════════════════════════════════════════════════════════════
+
+os.makedirs(CHECKPOINT_DIR, exist_ok=True)
+
+
+def _strip_orig_mod_prefix(state_dict):
+    out = {}
+    for k, v in state_dict.items():
+        if k.startswith("_orig_mod."):
+            out[k[len("_orig_mod."):]] = v
+        else:
+            out[k] = v
+    return out
+
+
+def _add_orig_mod_prefix(state_dict):
+    out = {}
+    for k, v in state_dict.items():
+        if k.startswith("_orig_mod."):
+            out[k] = v
+        else:
+            out[f"_orig_mod.{k}"] = v
+    return out
+
+
+def _align_state_dict_for_model(state_dict, model):
+    """Align checkpoint keys with model keys (compiled vs non-compiled)."""
+    model_keys = list(model.state_dict().keys())
+    if not model_keys:
+        return state_dict
+
+    model_has_orig = model_keys[0].startswith("_orig_mod.")
+    ckpt_keys = list(state_dict.keys())
+    ckpt_has_orig = bool(ckpt_keys) and ckpt_keys[0].startswith("_orig_mod.")
+
+    if model_has_orig and not ckpt_has_orig:
+        return _add_orig_mod_prefix(state_dict)
+    if not model_has_orig and ckpt_has_orig:
+        return _strip_orig_mod_prefix(state_dict)
+    return state_dict
+
+def save_checkpoint(step, model, train_loss, val_loss, path):
+    """Save model and training state to disk."""
+    # DeepSpeed handles checkpointing, but we also save basic metadata
+    model_state = model.state_dict() if hasattr(model, "state_dict") else None
+    if model_state is not None:
+        # Store canonical keys so checkpoints are reusable across compile modes.
+        model_state = _strip_orig_mod_prefix(model_state)
+
+    checkpoint = {
+        "step": step,
+        "train_loss": train_loss,
+        "val_loss": val_loss,
+        "model_state": model_state,
+    }
+    torch.save(checkpoint, path)
+
+def load_checkpoint(path, model):
+    """Load checkpoint and return the step to resume from."""
+    ckpt = torch.load(path, map_location="cpu", weights_only=False)
+    if ckpt.get("model_state"):
+        model_state = _align_state_dict_for_model(ckpt["model_state"], model)
+        model.load_state_dict(model_state)
+    print(f"  Resumed from step {ckpt['step']}  "
+          f"(train {ckpt['train_loss']:.4f}, val {ckpt['val_loss']:.4f})")
+    return ckpt["step"], ckpt["val_loss"]
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 7. LEARNING-RATE SCHEDULE  (linear warmup → cosine decay to 10 %)
+# ═════════════════════════════════════════════════════════════════════════════
+
+def get_lr(step):
+    if step < WARMUP_STEPS:
+        return LR * step / WARMUP_STEPS
+    progress = (step - WARMUP_STEPS) / max(1, MAX_ITERS - WARMUP_STEPS)
+    return LR * 0.1 + 0.5 * LR * 0.9 * (1 + math.cos(math.pi * progress))
+
+
+def get_eta_clock(progress, task_id):
+    """Return estimated finish time in IST as HH:MM."""
+    remaining = progress.tasks[task_id].time_remaining
+    if remaining is None:
+        return "--:--"
+    end_at = datetime.now(IST) + timedelta(seconds=max(0.0, remaining))
+    return end_at.strftime("%H:%M")
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 8. LOSS ESTIMATION
+# ═════════════════════════════════════════════════════════════════════════════
+
+@torch.no_grad()
+def estimate_loss(model):
+    model.eval()
+    out = {}
+    for split in ("train", "val"):
+        losses = []
+        for _ in range(EVAL_ITERS):
+            x, y = get_batch(split)
+            with torch.amp.autocast("cuda", dtype=torch.bfloat16, enabled=(DEVICE == "cuda" and DTYPE == torch.bfloat16)):
+                _, loss = model(x, y)
+            losses.append(loss.item())
+        out[split] = sum(losses) / len(losses)
+    model.train()
+    return out
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 9. DEEPSPEED INITIALIZATION & TRAINING
+# ═════════════════════════════════════════════════════════════════════════════
+
+if __name__ == "__main__":
+    # Clear cache
+    if DEVICE == "cuda":
+        torch.cuda.empty_cache()
+
+    # Initialize model
+    model = MoEGPT()
+    if DEVICE == "cuda" and USE_TORCH_COMPILE:
+        # Full-graph mode is too brittle for dynamic shapes; max-autotune is a good speed/compat compromise.
+        model = torch.compile(model, mode="max-autotune", fullgraph=False)
+
+    n_total  = sum(p.numel() for p in model.parameters())
+    _expert1 = sum(p.numel() for p in model.blocks[0].moe.experts[0].parameters())
+    n_active = n_total - _expert1 * (NUM_EXPERTS - TOP_K) * NUM_LAYERS
+
+    print(f"Total  parameters : {n_total:>14,}")
+    print(f"Active per token  : {n_active:>14,}")
+    print()
+
+    # Ensure LOCAL_RANK is set so DeepSpeed's sanity checks pass when running
+    # this script directly with `python main_deepspeed.py` (single-GPU).
+    if "LOCAL_RANK" not in os.environ:
+        os.environ["LOCAL_RANK"] = "0"
+
+    # Initialize torch.distributed if not already initialized (single-process setup).
+    if not torch.distributed.is_initialized():
+        init_kwargs = {
+            "backend": "nccl" if DEVICE == "cuda" else "gloo",
+            "init_method": "tcp://127.0.0.1:29500",
+            "rank": 0,
+            "world_size": 1,
+        }
+        if DEVICE == "cuda":
+            init_kwargs["device_id"] = torch.device("cuda", int(os.environ.get("LOCAL_RANK", 0)))
+        torch.distributed.init_process_group(**init_kwargs)
+
+    # Load DeepSpeed config (launcher may provide a mode-specific config path).
+    ds_config_path = os.environ.get("DS_CONFIG_PATH", "ds_config.json")
+    with open(ds_config_path) as f:
+        ds_config = json.load(f)
+
+    # Keep runtime batch settings aligned with DeepSpeed config.
+    MICRO_BATCH = int(ds_config.get("train_micro_batch_size_per_gpu", MICRO_BATCH))
+    GRAD_ACCUM = int(ds_config.get("gradient_accumulation_steps", GRAD_ACCUM))
+
+    print(f"DeepSpeed micro-batch : {MICRO_BATCH}")
+    print(f"DeepSpeed grad accum  : {GRAD_ACCUM}")
+    print(f"DeepSpeed eff batch   : {MICRO_BATCH * GRAD_ACCUM}")
+    print()
+
+    # Initialize DeepSpeed engine
+    model_engine, optimizer, _, lr_scheduler = deepspeed.initialize(
+        args=type("args", (), {"local_rank": int(os.environ.get("LOCAL_RANK", 0))})(),
+        model=model,
+        model_parameters=model.parameters(),
+        config=ds_config,
+        dist_init_required=False,
+    )
+
+    print(f"[DeepSpeed] Initialized with ZeRO Stage {ds_config['zero_optimization']['stage']}")
+    print(f"[DeepSpeed] Device: {model_engine.device}")
+    print()
+
+    # Training state
+    start_step = 0
+    best_val = float("inf")
+    prev_val = None
+
+    # Auto-resume from latest checkpoint (with NaN guard)
+    latest_ckpt = os.path.join(CHECKPOINT_DIR, "latest.pt")
+    if os.path.exists(latest_ckpt):
+        try:
+            _c = torch.load(latest_ckpt, map_location="cpu", weights_only=False)
+            if _c.get("val_loss") != _c.get("val_loss") or _c.get("train_loss") != _c.get("train_loss"):
+                print("Checkpoint has NaN losses — deleting and starting fresh")
+                os.remove(latest_ckpt)
+            else:
+                print("Checkpoint found — resuming …")
+                start_step, best_val = load_checkpoint(latest_ckpt, model)
+                print()
+        except Exception as e:
+            print(f"Checkpoint corrupted ({e}) — starting fresh")
+    else:
+        print("No checkpoint found — starting fresh training")
+        print()
+
+    # ─────────────────────────────────────────────────────────────────────────
+    # TRAINING LOOP
+    # ─────────────────────────────────────────────────────────────────────────
+
+    console.rule("[bold green]Training started (DeepSpeed)")
+    print()
+
+    with Progress(
+        SpinnerColumn(),
+        TextColumn("[bold blue]{task.description}"),
+        BarColumn(bar_width=30),
+        MofNCompleteColumn(),
+        TextColumn("•"),
+        TimeElapsedColumn(),
+        TextColumn("•"),
+        TimeRemainingColumn(),
+        TextColumn("•"),
+        TextColumn("[green]ETA {task.fields[end_clock]} IST"),
+        TextColumn("•"),
+        TextColumn("[yellow]loss {task.fields[train_loss]}"),
+        TextColumn("[cyan]val {task.fields[val_loss]}"),
+        TextColumn("[magenta]lr {task.fields[lr]}"),
+        console=console,
+        refresh_per_second=4,
+    ) as progress:
+        total_steps = MAX_ITERS - start_step
+        task = progress.add_task(
+            "Training", total=total_steps,
+            train_loss="--.----", val_loss="--.----", lr="--.------", end_clock="--:--",
+        )
+
+        step = start_step
+        micro_loss_sum = 0.0
+        micro_loss_count = 0
+        total_micro_steps = MAX_ITERS * GRAD_ACCUM
+
+        for micro_step in range(start_step * GRAD_ACCUM + 1, total_micro_steps + 1):
+
+            lr = get_lr(step + 1)
+            for param_group in optimizer.param_groups:
+                param_group["lr"] = lr
+
+            x, y = get_batch("train")
+            _, loss = model_engine(x, y)
+
+            model_engine.backward(loss)
+            is_boundary = model_engine.is_gradient_accumulation_boundary()
+            model_engine.step()
+
+            micro_loss_sum += loss.item()
+            micro_loss_count += 1
+
+            if not is_boundary:
+                continue
+
+            step += 1
+            accum_loss = micro_loss_sum / max(1, micro_loss_count)
+            micro_loss_sum = 0.0
+            micro_loss_count = 0
+
+            progress.update(
+                task,
+                advance=1,
+                train_loss=f"{accum_loss:.4f}",
+                lr=f"{lr:.6f}",
+                end_clock=get_eta_clock(progress, task),
+            )
+
+            if step % EVAL_EVERY == 0:
+                losses = estimate_loss(model_engine.module if hasattr(model_engine, "module") else model_engine)
+                if prev_val is None:
+                    trend = "init"
+                    delta = 0.0
+                else:
+                    delta = losses["val"] - prev_val
+                    if delta < -1e-6:
+                        trend = "improving"
+                    elif delta > 1e-6:
+                        trend = "worse"
+                    else:
+                        trend = "flat"
+                prev_val = losses["val"]
+
+                progress.update(
+                    task,
+                    train_loss=f"{losses['train']:.4f}",
+                    val_loss=f"{losses['val']:.4f}",
+                    lr=f"{lr:.6f}",
+                    end_clock=get_eta_clock(progress, task),
+                )
+                progress.console.print(
+                    f"  [bold]Step {step:>5}[/]  │  "
+                    f"[yellow]Train {losses['train']:.4f}[/]  │  "
+                    f"[cyan]Val {losses['val']:.4f} ({trend}, Δ {delta:+.4f})[/]  │  "
+                    f"[magenta]LR {lr:.6f}[/]"
+                )
+
+                # Save checkpoints
+                save_checkpoint(
+                    step, model_engine.module if hasattr(model_engine, 'module') else model_engine,
+                    losses["train"], losses["val"],
+                    os.path.join(CHECKPOINT_DIR, "latest.pt"),
+                )
+                if losses["val"] < best_val:
+                    best_val = losses["val"]
+                    save_checkpoint(
+                        step, model_engine.module if hasattr(model_engine, 'module') else model_engine,
+                        losses["train"], losses["val"],
+                        os.path.join(CHECKPOINT_DIR, "best.pt"),
+                    )
+                    progress.console.print(
+                        f"  [bold green]★ New best val loss: {best_val:.4f}  (saved best.pt)[/]"
+                    )
+
+            if step >= MAX_ITERS:
+                break
+
+    print()
+    console.rule("[bold green]Training complete")
+    print()
+
+    # ─────────────────────────────────────────────────────────────────────────
+    # TEST EVALUATION
+    # ─────────────────────────────────────────────────────────────────────────
+
+    model_eval = model_engine.module if hasattr(model_engine, 'module') else model_engine
+    model_eval.eval()
+    test_losses = []
+    with torch.no_grad():
+        for _ in range(EVAL_ITERS):
+            x, y = get_batch("test")
+            _, loss = model_eval(x, y)
+            test_losses.append(loss.item())
+    test_loss = sum(test_losses) / len(test_losses)
+    print(f"Test loss : {test_loss:.4f}")
+    print()
+
+    # ─────────────────────────────────────────────────────────────────────────
+    # TEXT GENERATION
+    # ─────────────────────────────────────────────────────────────────────────
+
+    prompts = [
+        "The history of",
+        "Scientists have discovered",
+        "In the early twentieth century",
+    ]
+
+    print("=" * 60)
+    print("Generated Text Samples")
+    print("=" * 60)
+
+    for prompt in prompts:
+        output = model_eval.generate(prompt, max_new_tokens=120, temperature=0.7)
+        print(f"\nPrompt : \"{prompt}\"")
+        print(f"Output : {output.strip()}")
+        print()
+
+    # ─────────────────────────────────────────────────────────────────────────
+    # INTERACTIVE MODE
+    # ─────────────────────────────────────────────────────────────────────────
+
+    print("=" * 60)
+    print("Interactive Mode  (type 'quit' to exit)")
+    print("=" * 60)
+
+    while True:
+        try:
+            prompt = input("\nEnter a prompt: ").strip()
+        except (EOFError, KeyboardInterrupt):
+            break
+        if not prompt or prompt.lower() == "quit":
+            break
+        output = model_eval.generate(prompt, max_new_tokens=150, temperature=0.8)
+        print(f"\n{output.strip()}")
+
+    print("\nGoodbye!")

prepare_data.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python3
+"""
+prepare_data.py
+===============
+Build tokenized binary files for training from Cosmopedia using streaming.
+
+Outputs:
+  data/train.bin
+  data/val.bin
+  data/test.bin
+
+Dataset:
+  HuggingFaceTB/cosmopedia (streaming)
+"""
+
+import os
+from pathlib import Path
+
+import numpy as np
+import tiktoken
+from datasets import load_dataset
+from tqdm.auto import tqdm
+
+# Local project cache for reproducibility and resume behavior.
+os.environ.setdefault("HF_HOME", "./hf_cache")
+os.environ.setdefault("HF_DATASETS_CACHE", "./hf_cache/datasets")
+os.environ.setdefault("HF_HUB_CACHE", "./hf_cache/hub")
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+
+DATA_DIR = Path("data")
+DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+CACHE_DIR = "./hf_cache"
+DATASET_NAME = "HuggingFaceTB/cosmopedia"
+DATASET_CONFIG = os.environ.get("DATASET_CONFIG", "stories")
+
+# Stream only first N rows by default, matching your requested pattern.
+MAX_EXAMPLES = int(os.environ.get("MAX_EXAMPLES", "1000000"))
+
+# Deterministic split from one stream: 98% train, 1% val, 1% test.
+TRAIN_FRAC = float(os.environ.get("TRAIN_FRAC", "0.98"))
+VAL_FRAC = float(os.environ.get("VAL_FRAC", "0.01"))
+
+# Flush chunks to disk to keep RAM bounded.
+FLUSH_TOKENS = int(os.environ.get("FLUSH_TOKENS", "2000000"))
+
+enc = tiktoken.get_encoding("gpt2")
+EOT = enc.eot_token
+
+
+def extract_text(row: dict) -> str:
+    """Extract a usable text field across possible Cosmopedia schemas."""
+    if "text" in row and isinstance(row["text"], str):
+        return row["text"].strip()
+    if "content" in row and isinstance(row["content"], str):
+        return row["content"].strip()
+
+    parts = []
+    for key in ("prompt", "question", "instruction", "input", "answer", "response", "output"):
+        val = row.get(key)
+        if isinstance(val, str) and val.strip():
+            parts.append(val.strip())
+
+    return "\n\n".join(parts).strip()
+
+
+def encode_text(text: str):
+    ids = enc.encode_ordinary(text)
+    ids.append(EOT)
+    return ids
+
+
+def flush_tokens(fp, buffer_tokens):
+    if not buffer_tokens:
+        return 0
+    arr = np.asarray(buffer_tokens, dtype=np.uint16)
+    arr.tofile(fp)
+    n = int(arr.size)
+    buffer_tokens.clear()
+    return n
+
+
+def pick_split(i: int, total: int) -> str:
+    train_cut = int(total * TRAIN_FRAC)
+    val_cut = train_cut + int(total * VAL_FRAC)
+    if i < train_cut:
+        return "train"
+    if i < val_cut:
+        return "val"
+    return "test"
+
+
+if __name__ == "__main__":
+    print("Loading Cosmopedia (streaming)...")
+
+    # This follows your requested style while allowing MAX_EXAMPLES override.
+    dataset = load_dataset(
+        DATASET_NAME,
+        DATASET_CONFIG,
+        split="train",
+        streaming=True,
+        cache_dir=CACHE_DIR,
+    )
+
+    out_paths = {
+        "train": DATA_DIR / "train.bin",
+        "val": DATA_DIR / "val.bin",
+        "test": DATA_DIR / "test.bin",
+    }
+
+    for p in out_paths.values():
+        if p.exists():
+            p.unlink()
+
+    buffers = {"train": [], "val": [], "test": []}
+    counts_examples = {"train": 0, "val": 0, "test": 0}
+    counts_tokens = {"train": 0, "val": 0, "test": 0}
+
+    with open(out_paths["train"], "ab") as f_train, open(out_paths["val"], "ab") as f_val, open(out_paths["test"], "ab") as f_test:
+        fps = {"train": f_train, "val": f_val, "test": f_test}
+
+        progress = tqdm(total=MAX_EXAMPLES, desc="Streaming+Encoding", unit="doc")
+        for i, row in enumerate(dataset):
+            if i >= MAX_EXAMPLES:
+                break
+
+            text = extract_text(row)
+            if not text:
+                progress.update(1)
+                continue
+
+            split = pick_split(i, MAX_EXAMPLES)
+            toks = encode_text(text)
+            buffers[split].extend(toks)
+            counts_examples[split] += 1
+
+            # Flush all splits together so val/test are written even if their
+            # individual buffers never reach FLUSH_TOKENS (they're only 1% each).
+            if len(buffers["train"]) >= FLUSH_TOKENS:
+                for s in ("train", "val", "test"):
+                    counts_tokens[s] += flush_tokens(fps[s], buffers[s])
+
+            progress.update(1)
+
+        progress.close()
+
+        for split in ("train", "val", "test"):
+            counts_tokens[split] += flush_tokens(fps[split], buffers[split])
+
+    print("\nDone.")
+    for split in ("train", "val", "test"):
+        print(f"{split:>5}: {counts_examples[split]:>10,} docs  ->  {counts_tokens[split]:>12,} tokens")
+    print(f"Saved files in: {DATA_DIR.resolve()}")

push_to_hf.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+"""
+Upload Tiny-GPT checkpoints to Hugging Face Hub.
+
+Usage:
+  python push_to_hf.py --repo-id yourname/Tiny-GPT
+  python push_to_hf.py --repo-id yourname/Tiny-GPT --checkpoint checkpoints/best.pt
+
+Auth:
+  Set HF_TOKEN env var or run: huggingface-cli login
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Upload Tiny-GPT checkpoints to HF Hub")
+    parser.add_argument("--repo-id", required=True, help="HF repo id, e.g. yourname/Tiny-GPT")
+    parser.add_argument(
+        "--checkpoint",
+        default="checkpoints/best.pt",
+        help="Primary checkpoint path to upload (default: checkpoints/best.pt)",
+    )
+    parser.add_argument(
+        "--latest-checkpoint",
+        default="checkpoints/latest.pt",
+        help="Optional latest checkpoint path to upload (default: checkpoints/latest.pt)",
+    )
+    parser.add_argument(
+        "--private",
+        action="store_true",
+        help="Create private repo instead of public",
+    )
+    parser.add_argument(
+        "--message",
+        default="Upload Tiny-GPT checkpoints",
+        help="Commit message for HF Hub",
+    )
+    parser.add_argument(
+        "--token",
+        default=None,
+        help="HF token (or set HF_TOKEN env var)",
+    )
+    args = parser.parse_args()
+
+    token = args.token or os.environ.get("HF_TOKEN")
+
+    try:
+        from huggingface_hub import HfApi, upload_file
+    except ImportError:
+        print("[ERROR] Missing dependency: huggingface_hub")
+        print("[ERROR] Install with: pip install huggingface_hub")
+        sys.exit(1)
+
+    checkpoint = Path(args.checkpoint)
+    latest_checkpoint = Path(args.latest_checkpoint)
+
+    if not checkpoint.exists():
+        print(f"[ERROR] Checkpoint not found: {checkpoint}")
+        sys.exit(1)
+
+    api = HfApi(token=token)
+
+    # Create repo if it does not exist yet.
+    api.create_repo(repo_id=args.repo_id, repo_type="model", private=args.private, exist_ok=True)
+
+    print(f"Uploading {checkpoint} -> {args.repo_id}/best.pt")
+    upload_file(
+        path_or_fileobj=str(checkpoint),
+        path_in_repo="best.pt",
+        repo_id=args.repo_id,
+        repo_type="model",
+        token=token,
+        commit_message=args.message,
+    )
+
+    if latest_checkpoint.exists():
+        print(f"Uploading {latest_checkpoint} -> {args.repo_id}/latest.pt")
+        upload_file(
+            path_or_fileobj=str(latest_checkpoint),
+            path_in_repo="latest.pt",
+            repo_id=args.repo_id,
+            repo_type="model",
+            token=token,
+            commit_message=args.message,
+        )
+
+    print("Done. Model checkpoints are now on Hugging Face Hub.")
+
+
+if __name__ == "__main__":
+    main()

run.py

@@ -0,0 +1,590 @@
+"""
+run.py – Inference script for MoE-GPT
+========================================
+Run the trained model anytime to generate text.
+
+Usage:
+    python run.py                  # Interactive mode
+    python run.py --prompt "text"  # Generate from prompt
+    python run.py --file data.txt  # Generate continuations from file
+
+No training — just inference from the best checkpoint.
+"""
+
+import os
+import sys
+import argparse
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import tiktoken
+
+# ═════════════════════════════════════════════════════════════════════════════
+# CONFIGURATION (must match main.py)
+# ═════════════════════════════════════════════════════════════════════════════
+
+BLOCK_SIZE = 128
+EMBED_DIM = 768
+NUM_HEADS = 12
+NUM_LAYERS = 12
+NUM_EXPERTS = 8
+TOP_K = 2
+FFN_DIM = EMBED_DIM * 4
+DROPOUT = 0.1
+CHECKPOINT_DIR = "checkpoints"
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.bfloat16 if DEVICE == "cuda" else torch.float32
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 1. TOKENISER – GPT-2 BPE
+# ═════════════════════════════════════════════════════════════════════════════
+
+enc = tiktoken.get_encoding("gpt2")
+vocab_size = enc.n_vocab  # 50,257
+
+
+def encode(text: str) -> list:
+    return enc.encode_ordinary(text)
+
+
+def decode(ids: list) -> str:
+    return enc.decode(ids)
+
+
+def _infer_num_heads(embed_dim: int) -> int:
+    """Infer a reasonable attention head count from embedding size."""
+    for h in (16, 12, 8, 6, 4, 2, 1):
+        if embed_dim % h == 0:
+            return h
+    return 1
+
+
+def apply_model_config_from_state_dict(state_dict: dict):
+    """Update global model hyperparameters to match checkpoint tensors."""
+    global BLOCK_SIZE, EMBED_DIM, NUM_HEADS, NUM_LAYERS, NUM_EXPERTS, FFN_DIM, vocab_size
+
+    if "tok_emb.weight" not in state_dict or "pos_emb.weight" not in state_dict:
+        return
+
+    vocab_size = state_dict["tok_emb.weight"].shape[0]
+    EMBED_DIM = state_dict["tok_emb.weight"].shape[1]
+    BLOCK_SIZE = state_dict["pos_emb.weight"].shape[0]
+
+    layer_ids = []
+    for k in state_dict.keys():
+        if k.startswith("blocks."):
+            parts = k.split(".")
+            if len(parts) > 1 and parts[1].isdigit():
+                layer_ids.append(int(parts[1]))
+    if layer_ids:
+        NUM_LAYERS = max(layer_ids) + 1
+
+    router_key = "blocks.0.moe.router.weight"
+    if router_key in state_dict:
+        NUM_EXPERTS = state_dict[router_key].shape[0]
+
+    ffn_key = "blocks.0.moe.experts.0.w1.weight"
+    if ffn_key in state_dict:
+        FFN_DIM = state_dict[ffn_key].shape[0]
+    else:
+        FFN_DIM = EMBED_DIM * 4
+
+    NUM_HEADS = _infer_num_heads(EMBED_DIM)
+
+
+def _get_model_state_from_checkpoint(ckpt: dict) -> dict:
+    """Support both training checkpoint formats used in this repo."""
+    if "model_state" in ckpt:
+        return ckpt["model_state"]
+    if "model" in ckpt:
+        return ckpt["model"]
+    raise KeyError("Checkpoint does not contain 'model_state' or 'model'")
+
+
+def resolve_checkpoint_path(
+    checkpoint_path=None,
+    hf_repo=None,
+    hf_filename="best.pt",
+    hf_revision=None,
+    hf_token=None,
+):
+    """Resolve a local checkpoint path, optionally downloading from HF Hub."""
+    if hf_repo:
+        try:
+            from huggingface_hub import hf_hub_download
+        except ImportError:
+            print("[ERROR] huggingface_hub is required for --hf-repo")
+            print("[ERROR] Install it with: pip install huggingface_hub")
+            sys.exit(1)
+
+        cache_dir = Path("hf_cache") / "hub"
+        cache_dir.mkdir(parents=True, exist_ok=True)
+        return hf_hub_download(
+            repo_id=hf_repo,
+            filename=hf_filename,
+            revision=hf_revision,
+            token=hf_token,
+            cache_dir=str(cache_dir),
+        )
+
+    if checkpoint_path is None:
+        checkpoint_path = os.path.join(CHECKPOINT_DIR, "best.pt")
+    return checkpoint_path
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 2. MODEL ARCHITECTURE (minimal — see main.py for full details)
+# ══════════════��══════════════════════════════════════════════════════════════
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.n_heads = NUM_HEADS
+        self.head_dim = EMBED_DIM // NUM_HEADS
+        self.qkv = nn.Linear(EMBED_DIM, 3 * EMBED_DIM, bias=False)
+        self.proj = nn.Linear(EMBED_DIM, EMBED_DIM, bias=False)
+        self.attn_drop = nn.Dropout(DROPOUT)
+        self.proj_drop = nn.Dropout(DROPOUT)
+        self.register_buffer(
+            "mask",
+            torch.tril(torch.ones(BLOCK_SIZE, BLOCK_SIZE)).view(
+                1, 1, BLOCK_SIZE, BLOCK_SIZE
+            ),
+        )
+
+    def forward(self, x):
+        B, T, C = x.shape
+        qkv = self.qkv(x).reshape(B, T, 3, self.n_heads, self.head_dim)
+        q, k, v = qkv.permute(2, 0, 3, 1, 4)
+
+        att = (q @ k.transpose(-2, -1)) * (self.head_dim**-0.5)
+        att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float("-inf"))
+        att = F.softmax(att.float(), dim=-1).to(x.dtype)
+        att = self.attn_drop(att)
+
+        out = (att @ v).transpose(1, 2).reshape(B, T, C)
+        return self.proj_drop(self.proj(out))
+
+
+class ExpertFFN(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.w1 = nn.Linear(EMBED_DIM, FFN_DIM)
+        self.w2 = nn.Linear(FFN_DIM, EMBED_DIM)
+        self.act = nn.GELU()
+        self.drop = nn.Dropout(DROPOUT)
+
+    def forward(self, x):
+        return self.drop(self.w2(self.act(self.w1(x))))
+
+
+class MoELayer(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.router = nn.Linear(EMBED_DIM, NUM_EXPERTS, bias=False)
+        self.experts = nn.ModuleList([ExpertFFN() for _ in range(NUM_EXPERTS)])
+
+    def forward(self, x):
+        B, T, C = x.shape
+        flat = x.reshape(-1, C)
+        N = flat.shape[0]
+
+        logits = self.router(flat)
+        probs = F.softmax(logits.float(), dim=-1)
+
+        top_w, top_i = torch.topk(probs, TOP_K, dim=-1)
+        top_w = (top_w / top_w.sum(dim=-1, keepdim=True)).to(x.dtype)
+
+        out = torch.zeros_like(flat)
+        for i, expert in enumerate(self.experts):
+            mask = (top_i == i).any(dim=-1)
+            if not mask.any():
+                continue
+            tokens = flat[mask]
+            e_out = expert(tokens)
+            match = (top_i[mask] == i).to(x.dtype)
+            weights = (top_w[mask] * match).sum(-1, keepdim=True)
+            out[mask] += weights * e_out
+
+        return out.reshape(B, T, C)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(EMBED_DIM)
+        self.attn = CausalSelfAttention()
+        self.ln2 = nn.LayerNorm(EMBED_DIM)
+        self.moe = MoELayer()
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.moe(self.ln2(x))
+        return x
+
+
+class MoEGPT(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.tok_emb = nn.Embedding(vocab_size, EMBED_DIM)
+        self.pos_emb = nn.Embedding(BLOCK_SIZE, EMBED_DIM)
+        self.drop = nn.Dropout(DROPOUT)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(EMBED_DIM)
+        self.head = nn.Linear(EMBED_DIM, vocab_size, bias=False)
+        self.head.weight = self.tok_emb.weight
+        self._init_weights()
+
+    def _init_weights(self):
+        for name, p in self.named_parameters():
+            if p.dim() >= 2:
+                nn.init.normal_(p, mean=0.0, std=0.02)
+            elif "bias" in name:
+                nn.init.zeros_(p)
+        scale = (2 * NUM_LAYERS) ** -0.5
+        for block in self.blocks:
+            nn.init.normal_(block.attn.proj.weight, mean=0.0, std=0.02 * scale)
+            for expert in block.moe.experts:
+                nn.init.normal_(expert.w2.weight, mean=0.0, std=0.02 * scale)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        x = self.drop(
+            self.tok_emb(idx) + self.pos_emb(torch.arange(T, device=idx.device))
+        )
+
+        for block in self.blocks:
+            x = block(x)
+
+        logits = self.head(self.ln_f(x))
+
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, vocab_size), targets.view(-1))
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(
+        self,
+        prompt: str,
+        max_new_tokens=200,
+        temperature=0.8,
+        top_k=None,
+        top_p=0.9,
+    ):
+        """
+        Generate text from a prompt.
+
+        Args:
+            prompt: Starting text
+            max_new_tokens: How many tokens to generate
+            temperature: Higher = more random (0.5-1.5 typical)
+            top_k: Keep only top-k most likely tokens (None = disabled)
+            top_p: Nucleus sampling threshold (0.9 typical)
+        """
+        self.eval()
+        ids = torch.tensor([encode(prompt)], dtype=torch.long, device=DEVICE)
+
+        for _ in range(max_new_tokens):
+            ctx = ids[:, -BLOCK_SIZE:]
+            with torch.amp.autocast(
+                "cuda", dtype=torch.bfloat16, enabled=(DTYPE == torch.bfloat16)
+            ):
+                logits, _ = self(ctx)
+            logits = logits[:, -1, :].float() / temperature
+
+            # Top-K filtering
+            if top_k is not None:
+                indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+                logits[indices_to_remove] = float("-inf")
+
+            # Top-P (nucleus) filtering
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumsum_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumsum_probs > top_p
+                sorted_indices_to_remove[..., 0] = False
+                indices_to_remove = sorted_indices[sorted_indices_to_remove]
+                logits[:, indices_to_remove] = float("-inf")
+
+            probs = F.softmax(logits, dim=-1)
+            nxt = torch.multinomial(probs, 1)
+            ids = torch.cat([ids, nxt], dim=1)
+
+        self.train()
+        return decode(ids[0].tolist())
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 3. LOAD MODEL FROM CHECKPOINT
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+def load_model(
+    checkpoint_path=None,
+    hf_repo=None,
+    hf_filename="best.pt",
+    hf_revision=None,
+    hf_token=None,
+):
+    """Load the trained model from checkpoint."""
+    checkpoint_path = resolve_checkpoint_path(
+        checkpoint_path=checkpoint_path,
+        hf_repo=hf_repo,
+        hf_filename=hf_filename,
+        hf_revision=hf_revision,
+        hf_token=hf_token,
+    )
+
+    if not os.path.exists(checkpoint_path):
+        print(f"[ERROR] Checkpoint not found at: {checkpoint_path}")
+        print(f"[ERROR] Have you run 'python main.py' yet?")
+        sys.exit(1)
+
+    print(f"Loading model from {checkpoint_path} ...", end=" ", flush=True)
+    ckpt = torch.load(checkpoint_path, map_location=DEVICE, weights_only=False)
+    model_state = _get_model_state_from_checkpoint(ckpt)
+    apply_model_config_from_state_dict(model_state)
+
+    model = MoEGPT()
+    model = model.to(dtype=DTYPE, device=DEVICE)
+    model.load_state_dict(model_state)
+    model.eval()
+
+    print("✓")
+    print(f"  Device: {DEVICE.upper()}")
+    print(f"  Dtype: {DTYPE}")
+    print(
+        f"  Model: block={BLOCK_SIZE}, emb={EMBED_DIM}, heads={NUM_HEADS}, "
+        f"layers={NUM_LAYERS}, experts={NUM_EXPERTS}, ffn={FFN_DIM}"
+    )
+    print()
+
+    return model
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 4. INTERACTIVE & BATCH INFERENCE
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+def interactive_mode(model):
+    """Interactive text generation."""
+    print("=" * 70)
+    print("Interactive Mode – Type 'quit' to exit")
+    print("=" * 70)
+    print()
+    print("Commands:")
+    print("  quit          – Exit")
+    print("  /temp 0.7     – Set temperature (default 0.8)")
+    print("  /len 100      – Set max tokens (default 200)")
+    print("  /topk 40      – Set top-k (default None = disabled)")
+    print("  /topp 0.9     – Set top-p (default 0.9)")
+    print()
+
+    temperature = 0.8
+    max_tokens = 200
+    top_k = None
+    top_p = 0.9
+
+    while True:
+        try:
+            user_input = input("Prompt > ").strip()
+        except (EOFError, KeyboardInterrupt):
+            break
+
+        if not user_input:
+            continue
+
+        if user_input.lower() == "quit":
+            break
+
+        # Handle commands
+        if user_input.startswith("/"):
+            parts = user_input.split()
+            if len(parts) == 2:
+                cmd, val = parts[0][1:], parts[1]
+                try:
+                    if cmd == "temp":
+                        temperature = float(val)
+                        print(f"Temperature set to {temperature}")
+                    elif cmd == "len":
+                        max_tokens = int(val)
+                        print(f"Max tokens set to {max_tokens}")
+                    elif cmd == "topk":
+                        top_k = int(val)
+                        print(f"Top-k set to {top_k}")
+                    elif cmd == "topp":
+                        top_p = float(val)
+                        print(f"Top-p set to {top_p}")
+                except ValueError:
+                    print(f"Invalid value for {cmd}")
+            continue
+
+        print()
+        with torch.no_grad():
+            output = model.generate(
+                user_input,
+                max_new_tokens=max_tokens,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+            )
+        print(output)
+        print()
+
+    print("\nGoodbye!")
+
+
+def batch_generation(model, prompts, max_tokens=200, temperature=0.8):
+    """Generate from a list of prompts."""
+    print("=" * 70)
+    print("Batch Generation")
+    print("=" * 70)
+    print()
+
+    with torch.no_grad():
+        for i, prompt in enumerate(prompts, 1):
+            print(f"[{i}/{len(prompts)}] Prompt: {prompt}")
+            output = model.generate(
+                prompt,
+                max_new_tokens=max_tokens,
+                temperature=temperature,
+            )
+            print(f"Output: {output}\n")
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 5. MAIN
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate text using trained MoE-GPT model",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python run.py                          # Interactive mode
+  python run.py --prompt "Hello world"   # Generate from prompt
+  python run.py --prompts file.txt       # Batch from file (one per line)
+  python run.py --checkpoint custom.pt   # Use custom checkpoint
+    python run.py --hf-repo user/Tiny-GPT  # Load from Hugging Face Hub
+        """,
+    )
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        help="Single prompt to generate from",
+    )
+    parser.add_argument(
+        "--prompts",
+        type=str,
+        help="File with prompts (one per line) for batch generation",
+    )
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        default=None,
+        help="Path to checkpoint (default: checkpoints/best.pt)",
+    )
+    parser.add_argument(
+        "--hf-repo",
+        type=str,
+        default=None,
+        help="Hugging Face repo id (e.g. user/Tiny-GPT). If set, download checkpoint from HF Hub.",
+    )
+    parser.add_argument(
+        "--hf-filename",
+        type=str,
+        default="best.pt",
+        help="Filename inside HF repo (default: best.pt)",
+    )
+    parser.add_argument(
+        "--hf-revision",
+        type=str,
+        default=None,
+        help="HF branch/tag/commit to download from",
+    )
+    parser.add_argument(
+        "--hf-token",
+        type=str,
+        default=None,
+        help="HF token for private repos (or use HF_TOKEN env var)",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=200,
+        help="Max tokens to generate (default: 200)",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.8,
+        help="Sampling temperature (default: 0.8)",
+    )
+    parser.add_argument(
+        "--top-k",
+        type=int,
+        default=None,
+        help="Top-k sampling (default: disabled)",
+    )
+    parser.add_argument(
+        "--top-p",
+        type=float,
+        default=0.9,
+        help="Top-p/nucleus sampling (default: 0.9)",
+    )
+
+    args = parser.parse_args()
+
+    if args.hf_repo and args.checkpoint:
+        print("[ERROR] Use either --checkpoint or --hf-repo, not both.")
+        sys.exit(1)
+
+    hf_token = args.hf_token or os.environ.get("HF_TOKEN")
+
+    # Load model
+    model = load_model(
+        checkpoint_path=args.checkpoint,
+        hf_repo=args.hf_repo,
+        hf_filename=args.hf_filename,
+        hf_revision=args.hf_revision,
+        hf_token=hf_token,
+    )
+
+    # Dispatch to appropriate mode
+    if args.prompt:
+        # Single prompt
+        print(f"Prompt: {args.prompt}\n")
+        with torch.no_grad():
+            output = model.generate(
+                args.prompt,
+                max_new_tokens=args.max_tokens,
+                temperature=args.temperature,
+                top_k=args.top_k,
+                top_p=args.top_p,
+            )
+        print(output)
+
+    elif args.prompts:
+        # Batch from file
+        if not os.path.exists(args.prompts):
+            print(f"[ERROR] File not found: {args.prompts}")
+            sys.exit(1)
+        with open(args.prompts) as f:
+            prompts = [line.strip() for line in f if line.strip()]
+        batch_generation(model, prompts, args.max_tokens, args.temperature)
+
+    else:
+        # Interactive mode
+        interactive_mode(model)
+
+
+if __name__ == "__main__":
+    main()

train_deepspeed.sh

@@ -0,0 +1,160 @@
+#!/bin/bash
+# train_deepspeed.sh - Launch training with DeepSpeed ZeRO-Infinity
+
+set -e
+
+echo "╔════════════════════════════════════════════════════════════════╗"
+echo "║  Tiny-GPT: DeepSpeed ZeRO-3 Training (CPU/NVMe Offloading)     ║"
+echo "╚════════════════════════════════════════════════════════════════╝"
+echo
+
+# Colors
+GREEN='\033[0;32m'
+BLUE='\033[0;34m'
+YELLOW='\033[1;33m'
+NC='\033[0m'
+
+# Training mode: passive (default) or aggressive
+TRAIN_MODE=$(echo "${TRAIN_MODE:-passive}" | tr '[:upper:]' '[:lower:]')
+export TRAIN_MODE
+if [ "$TRAIN_MODE" != "passive" ] && [ "$TRAIN_MODE" != "aggressive" ]; then
+    echo -e "${YELLOW}!${NC} Invalid TRAIN_MODE='$TRAIN_MODE'. Use 'passive' or 'aggressive'."
+    exit 1
+fi
+
+# Check DeepSpeed installation
+echo -e "${BLUE}[1/4]${NC} Checking dependencies..."
+python -c "import deepspeed" 2>/dev/null && echo -e "      ${GREEN}✓${NC} DeepSpeed installed" || {
+    echo -e "      Installing DeepSpeed..."
+    pip install deepspeed -q
+    echo -e "      ${GREEN}✓${NC} DeepSpeed installed"
+}
+echo -e "      ${GREEN}✓${NC} All dependencies ready"
+echo
+
+# Checkpoint handling (keep by default for auto-resume)
+echo -e "${BLUE}[2/4]${NC} Checkpoint handling..."
+mkdir -p checkpoints
+if [ "${RESET_CHECKPOINTS:-0}" = "1" ]; then
+    rm -f checkpoints/*.pt
+    echo -e "      ${GREEN}✓${NC} Checkpoints cleared (RESET_CHECKPOINTS=1)"
+else
+    if ls checkpoints/*.pt >/dev/null 2>&1; then
+        echo -e "      ${GREEN}✓${NC} Existing checkpoints found (auto-resume enabled)"
+    else
+        echo -e "      ${GREEN}✓${NC} No existing checkpoints (fresh run)"
+    fi
+fi
+echo
+
+# Verify dataset
+echo -e "${BLUE}[3/4]${NC} Verifying dataset..."
+if [ -f "data/train.bin" ] && [ -f "data/val.bin" ] && [ -f "data/test.bin" ]; then
+    echo -e "      ${GREEN}✓${NC} Dataset ready"
+else
+    echo -e "      ${YELLOW}!${NC} Dataset not found. Run: python prepare_data.py"
+    exit 1
+fi
+echo
+
+# Get number of GPUs
+NUM_GPUS=$(nvidia-smi --list-gpus 2>/dev/null | wc -l)
+if [ -z "$NUM_GPUS" ] || [ "$NUM_GPUS" -eq 0 ]; then
+    NUM_GPUS=1
+fi
+export NUM_GPUS
+
+# Build active DeepSpeed config based on TRAIN_MODE
+python - <<'PY'
+import json
+import os
+import multiprocessing
+
+mode = os.environ.get("TRAIN_MODE", "passive").lower()
+num_gpus = int(os.environ.get("NUM_GPUS", "1"))
+
+with open("ds_config.json") as f:
+    cfg = json.load(f)
+
+zero = cfg.setdefault("zero_optimization", {})
+off_opt = zero.setdefault("offload_optimizer", {"device": "cpu"})
+act_ckpt = cfg.setdefault("activation_checkpointing", {})
+
+if mode == "aggressive":
+    # Higher-throughput profile: larger batches and no CPU checkpointing.
+    cfg["train_micro_batch_size_per_gpu"] = 2
+    cfg["gradient_accumulation_steps"] = 8
+    cfg["train_batch_size"] = cfg["train_micro_batch_size_per_gpu"] * cfg["gradient_accumulation_steps"] * max(1, num_gpus)
+    off_opt["pin_memory"] = True
+    zero["reduce_bucket_size"] = 2e6
+    act_ckpt["cpu_checkpointing"] = False
+else:
+    # Low-resource profile (current stable baseline).
+    cfg["train_micro_batch_size_per_gpu"] = 1
+    cfg["gradient_accumulation_steps"] = 4
+    cfg["train_batch_size"] = cfg["train_micro_batch_size_per_gpu"] * cfg["gradient_accumulation_steps"] * max(1, num_gpus)
+    off_opt["pin_memory"] = False
+    zero["reduce_bucket_size"] = 1e6
+    act_ckpt["cpu_checkpointing"] = True
+
+with open("ds_config.active.json", "w") as f:
+    json.dump(cfg, f, indent=2)
+PY
+
+# Show configuration
+echo -e "${BLUE}[4/4]${NC} Launching DeepSpeed training..."
+python -c "
+import json
+with open('ds_config.active.json') as f:
+    cfg = json.load(f)
+print('  DeepSpeed Configuration:')
+print(f\"    • Mode: ${TRAIN_MODE}\")
+print(f\"    • ZeRO Stage: {cfg['zero_optimization']['stage']}\")
+print(f\"    • Optimizer Offload: {cfg['zero_optimization']['offload_optimizer']['device']}\")
+param_offload = cfg['zero_optimization'].get('offload_param', {}).get('device', 'none')
+print(f\"    • Parameter Offload: {param_offload}\")
+print(f\"    • Mixed Precision: {'bfloat16' if cfg.get('bf16', {}).get('enabled') else 'float32'}\")
+print(f\"    • Micro Batch: {cfg['train_micro_batch_size_per_gpu']}\")
+print(f\"    • Grad Accum: {cfg['gradient_accumulation_steps']}\")
+print(f\"    • Batch Size: {cfg['train_batch_size']}\")
+print()
+"
+
+# Launch training with DeepSpeed
+echo -e "${YELLOW}Starting DeepSpeed training...${NC}"
+echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+echo
+
+# Skip CUDA version mismatch check (system CUDA >= PyTorch CUDA is fine)
+export DS_SKIP_CUDA_CHECK=1
+if [ "$TRAIN_MODE" = "aggressive" ]; then
+    CPU_THREADS=$(nproc)
+    export MAX_JOBS=$CPU_THREADS
+    export OMP_NUM_THREADS=$CPU_THREADS
+    export MKL_NUM_THREADS=$CPU_THREADS
+    export ALLOW_TF32=1
+    export USE_TORCH_COMPILE=${USE_TORCH_COMPILE:-0}
+    export USE_ACTIVATION_CHECKPOINT=0
+else
+    export MAX_JOBS=1
+    export OMP_NUM_THREADS=1
+    export MKL_NUM_THREADS=1
+    export ALLOW_TF32=1
+    export USE_TORCH_COMPILE=${USE_TORCH_COMPILE:-0}
+    export USE_ACTIVATION_CHECKPOINT=1
+fi
+
+# Main script reads this active config path.
+export DS_CONFIG_PATH="ds_config.active.json"
+
+# Launch with deepspeed
+deepspeed --num_gpus $NUM_GPUS main_deepspeed.py
+
+echo
+echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+echo -e "${GREEN}Training complete!${NC}"
+echo
+echo "Check results:"
+echo "  • Checkpoints: ls -lh checkpoints/"
+echo "  • Generate: python run.py"
+echo "  • Best model: checkpoints/best.pt"