Update README.md

README.md

@@ -63,8 +63,8 @@ print(f"Validation tokens: {len(val_data):,}")
 from transformers import PreTrainedTokenizerFast
 
 tokenizer = PreTrainedTokenizerFast.from_pretrained(
-    "ifkash/fineweb-6b",
-    subfolder="tokenized"
+  "ifkash/fineweb-6b",
+  subfolder="tokenized"
 )
 
 # Example usage
@@ -84,6 +84,39 @@ print(f"Decoded: {tokenizer.decode(tokens)}")
 - **`tokenized/tokenizer.json`**: Tokenizer vocabulary and merges
 - **`tokenized/tokenizer_config.json`**: Tokenizer configuration
 - **`tokenized/special_tokens_map.json`**: Special tokens mapping
+- **`distillation/`**: Knowledge distillation data (see below)
+
+### Distillation Data
+
+The `distillation/` directory contains precomputed teacher logits from [SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) for knowledge distillation:
+
+| File | Description | Size (6B tokens) |
+|------|-------------|------------------|
+| `metadata.json` | Configuration and vocab info | ~1 KB |
+| `train_tokens.bin` | Token IDs (uint16) | ~11.2 GB |
+| `train_topk_ids.bin` | Top-128 token indices | ~1.4 GB |
+| `train_topk_probs.bin` | Top-128 probabilities (float16) | ~1.4 GB |
+| `val_tokens.bin` | Validation token IDs | ~56 MB |
+| `val_topk_ids.bin` | Validation top-128 indices | ~7 MB |
+| `val_topk_probs.bin` | Validation top-128 probs | ~7 MB |
+
+**Loading distillation data:**
+```python
+import numpy as np
+import json
+
+# Load metadata
+with open("distillation/metadata.json") as f:
+  metadata = json.load(f)
+
+# Load memory-mapped files
+tokens = np.memmap("distillation/train_tokens.bin", dtype=np.uint16, mode="r")
+topk_ids = np.memmap("distillation/train_topk_ids.bin", dtype=np.uint16, mode="r").reshape(-1, 128)
+topk_probs = np.memmap("distillation/train_topk_probs.bin", dtype=np.float16, mode="r").reshape(-1, 128)
+
+print(f"Tokens: {len(tokens):,}")
+print(f"Teacher model: {metadata['teacher_model']}")
+```
 
 ### Data Fields
 
@@ -104,18 +137,18 @@ import numpy as np
 import torch
 
 def get_batch(split='train', batch_size=64, block_size=2048):
-    data = np.memmap(f'tokenized/{split}.bin', dtype=np.uint16, mode='r')
-    ix = torch.randint(len(data) - block_size, (batch_size,))
-    x = torch.stack([torch.from_numpy(data[i:i+block_size].astype(np.int64)) for i in ix])
-    y = torch.stack([torch.from_numpy(data[i+1:i+1+block_size].astype(np.int64)) for i in ix])
-    return x.cuda(), y.cuda()
+  data = np.memmap(f'tokenized/{split}.bin', dtype=np.uint16, mode='r')
+  ix = torch.randint(len(data) - block_size, (batch_size,))
+  x = torch.stack([torch.from_numpy(data[i:i+block_size].astype(np.int64)) for i in ix])
+  y = torch.stack([torch.from_numpy(data[i+1:i+1+block_size].astype(np.int64)) for i in ix])
+  return x.cuda(), y.cuda()
 
 # Training loop
 for step in range(num_steps):
-    x, y = get_batch('train')
-    logits, loss = model(x, y)
-    loss.backward()
-    optimizer.step()
+  x, y = get_batch('train')
+  logits, loss = model(x, y)
+  loss.backward()
+  optimizer.step()
 ```
 
 ## Tokenizer Details