Initial commit

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+# dataset
+dataset/

.python-version

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+3.12

pyproject.toml

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+[project]
+name = "fineweb"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "datasets>=4.4.1",
+    "ipykernel>=7.1.0",
+    "ipython>=9.7.0",
+    "matplotlib>=3.10.7",
+    "torch>=2.9.1",
+    "transformers>=4.57.3",
+]

scripts/download-dataset.py

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+import os
+import time
+import pyarrow as pa
+import pyarrow.parquet as pq
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+# --- Configuration ---
+FINETUNING_DATASET = "HuggingFaceFW/fineweb"
+DATASET_SPLIT = "train"
+TOKENIZER_NAME = "HuggingFaceTB/SmolLM2-1.7B"
+TARGET_TOKENS = 6_000_000_000  # 6 Billion tokens
+OUTPUT_DIR = "../dataset"
+CHUNK_SIZE = 50_000  # Number of documents to collect before writing a batch
+
+def download_and_save_in_chunks():
+    """Streams Fineweb, tokenizes, and saves to Parquet in memory-efficient chunks."""
+    
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    OUTPUT_FILE_PATH = os.path.join(OUTPUT_DIR, "data.parquet")
+
+    # 1. Load Tokenizer & Dataset in Streaming Mode
+    print(f"Loading tokenizer: {TOKENIZER_NAME}...")
+    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
+
+    print(f"Loading dataset: {FINETUNING_DATASET} in streaming mode...")
+    streaming_dataset = load_dataset(FINETUNING_DATASET, split=DATASET_SPLIT, streaming=True)
+
+    # 2. Define Schema (Crucial for ParquetWriter)
+    # Based on Fineweb's fields: 'text', 'meta', 'id'
+    schema = pa.schema([
+        pa.field('text', pa.string()),
+        pa.field('meta', pa.struct([
+            pa.field('url', pa.string()),
+            pa.field('dump', pa.string()),
+            pa.field('s_cluster', pa.int64()),
+            pa.field('token_count', pa.int64()), # Use their count, but rely on ours for stopping
+        ])),
+        pa.field('id', pa.string())
+    ])
+    
+    current_tokens = 0
+    collected_batch = []
+    
+    # Initialize Parquet Writer
+    writer = None
+    
+    print("\n--- Starting Stream and Chunked Write to Disk ---")
+    start_time = time.time()
+    
+    try:
+        for i, example in enumerate(streaming_dataset):
+            # Tokenize and check limit
+            tokens = len(tokenizer.encode(example['text']))
+            
+            if current_tokens + tokens > TARGET_TOKENS:
+                print("Token limit reached! Stopping stream.")
+                break
+                
+            collected_batch.append(example)
+            current_tokens += tokens
+
+            # 3. Write Batch to Disk when CHUNK_SIZE is reached
+            if (i + 1) % CHUNK_SIZE == 0:
+                print(f"Writing batch of {CHUNK_SIZE:,} documents...")
+                
+                # Convert list of dicts to PyArrow Table
+                # We extract the 'meta' fields to match the schema structure
+                meta_list = [d.pop('meta', {}) for d in collected_batch]
+                
+                # Flatten the data structure for PyArrow Table creation
+                flat_data = {
+                    'text': [d['text'] for d in collected_batch],
+                    'id': [d['id'] for d in collected_batch],
+                    # Recreate the structured meta column
+                    'meta': pa.array(meta_list, type=schema.field('meta').type)
+                }
+
+                # Create PyArrow Table
+                table = pa.Table.from_arrays(
+                    [pa.array(flat_data['text']), flat_data['meta'], pa.array(flat_data['id'])],
+                    schema=schema
+                )
+
+                if writer is None:
+                    # Initialize writer on first run
+                    writer = pq.ParquetWriter(OUTPUT_FILE_PATH, table.schema, compression='SNAPPY')
+                
+                # Write the batch to disk
+                writer.write_table(table)
+                
+                # Clear the batch list to free memory
+                collected_batch = []
+                
+                print(f"Total documents written so far: {i + 1:,} | Total tokens: {current_tokens:,}")
+
+    finally:
+        # 4. Final Write (any remaining documents) and Cleanup
+        if collected_batch:
+            # Handle the last, incomplete batch
+            meta_list = [d.pop('meta', {}) for d in collected_batch]
+            
+            flat_data = {
+                'text': [d['text'] for d in collected_batch],
+                'id': [d['id'] for d in collected_batch],
+                'meta': pa.array(meta_list, type=schema.field('meta').type)
+            }
+
+            table = pa.Table.from_arrays(
+                [pa.array(flat_data['text']), flat_data['meta'], pa.array(flat_data['id'])],
+                schema=schema
+            )
+
+            if writer is None:
+                # Handle the edge case where the total is less than CHUNK_SIZE
+                writer = pq.ParquetWriter(OUTPUT_FILE_PATH, table.schema, compression='SNAPPY')
+
+            writer.write_table(table)
+
+        if writer:
+            writer.close()
+        
+    end_time = time.time()
+    print("\n--- Final Result ---")
+    print(f"✅ Successfully created Parquet file: {os.path.abspath(OUTPUT_FILE_PATH)}")
+    print(f"Final token count (approx): {current_tokens:,}")
+    print(f"Total time for streaming and saving: {end_time - start_time:.2f} seconds")
+
+
+if __name__ == "__main__":
+    download_and_save_in_chunks()