{"id":88708,"date":"2026-08-01T17:24:25","date_gmt":"2026-08-01T09:24:25","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/88708.html"},"modified":"2026-08-01T17:24:25","modified_gmt":"2026-08-01T09:24:25","slug":"code-alpaca-%e4%bb%a3%e7%a0%81%e6%8c%87%e4%bb%a4%e6%95%b0%e6%8d%ae%e9%9b%86%e5%be%ae%e8%b0%83%ef%bc%9asequence-length-%e8%ae%be%e4%b8%ba-2048-%e7%9a%84%e4%be%9d%e6%8d%ae%e4%b8%8e%e5%ae%9e%e8%b7%b5","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/88708.html","title":{"rendered":"Code Alpaca \u4ee3\u7801\u6307\u4ee4\u6570\u636e\u96c6\u5fae\u8c03\uff1aSequence Length \u8bbe\u4e3a 2048 \u7684\u4f9d\u636e\u4e0e\u5b9e\u8df5\uff08\u9644\u5b9e\u9a8c\u4ee3\u7801\uff09"},"content":{"rendered":"

Code Alpaca \u4ee3\u7801\u6307\u4ee4\u6570\u636e\u96c6\u5fae\u8c03：Sequence Length \u8bbe\u4e3a 2048 \u7684\u4f9d\u636e\u4e0e\u5b9e\u8df5（\u9644\u5b9e\u9a8c\u4ee3\u7801）<\/h2>\n

\u4e00\u3001\u5f15\u8a00<\/h3>\n

\u5728\u5fae\u8c03\u5927\u8bed\u8a00\u6a21\u578b（\u5c24\u5176\u662f\u4ee3\u7801\u751f\u6210\u4efb\u52a1）\u65f6，\u5e8f\u5217\u957f\u5ea6（Sequence Length） \u662f\u4e00\u4e2a\u5173\u952e\u7684\u8d85\u53c2\u6570\u3002\u5b83\u76f4\u63a5\u5f71\u54cd\u663e\u5b58\u5360\u7528\u3001\u8bad\u7ec3\u901f\u5ea6\u548c\u6a21\u578b\u5bf9\u957f\u6587\u672c\u7684\u5904\u7406\u80fd\u529b\u3002 \u5bf9\u4e8e Code Alpaca_20K \u8fd9\u4e2a\u5e38\u7528\u7684\u4ee3\u7801\u6307\u4ee4\u5fae\u8c03\u6570\u636e\u96c6，\u6211\u4eec\u901a\u8fc7\u5b9e\u9a8c\u8bc1\u5b9e：\u5c06\u5e8f\u5217\u957f\u5ea6\u8bbe\u4e3a 2048 \u662f\u6027\u4ef7\u6bd4\u6700\u4f18\u7684\u9009\u62e9，\u80fd\u591f\u8986\u76d6 99% \u4ee5\u4e0a\u7684\u6837\u672c\u3002\u672c\u6587\u5206\u4eab\u4e86\u5b8c\u6574\u7684\u5b9e\u9a8c\u4ee3\u7801\u548c\u7edf\u8ba1\u7ed3\u679c，\u4f9b\u8bfb\u8005\u53c2\u8003\u3002<\/p>\n

\n

\u4e8c\u3001\u6570\u636e\u96c6\u7b80\u4ecb<\/h3>\n

Code Alpaca_20K \u662f Stanford Alpaca \u9879\u76ee\u7684\u4ee3\u7801\u751f\u6210\u6269\u5c55\u7248，\u7531 Self-Instruct \u6280\u672f\u751f\u6210，\u5305\u542b 20,000 \u6761 (instruction, input, output) \u4e09\u5143\u7ec4，\u8986\u76d6\u591a\u79cd\u7f16\u7a0b\u8bed\u8a00\u3002 \u6570\u636e\u683c\u5f0f\u793a\u4f8b：<\/p>\n

`json
\n{
\n "instruction": "Write a Python function to calculate the factorial of a number.",
\n "input": "",
\n "output": "def factorial(n):\\\\n if n == 0:\\\\n return 1\\\\n else:\\\\n return n * factorial(n-1)"
\n}<\/p>\n

\u76ee\u524d Hugging Face \u4e0a\u4e3b\u8981\u6709\u4e24\u4e2a\u7248\u672c：sahil2801\/CodeAlpaca-20k \u548c HuggingFaceH4\/CodeAlpaca_20K，\u4e24\u8005\u5185\u5bb9\u57fa\u672c\u4e00\u81f4，\u540e\u8005\u5c06\u6570\u636e\u96c6\u62c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002<\/p>\n

\n

\u4e09\u3001\u4e3a\u4ec0\u4e48 Sequence Length \u53ef\u4ee5\u8bbe\u4e3a 2048？<\/h3>\n

3.1 \u7406\u8bba\u4f9d\u636e：\u6570\u636e\u96c6\u7684\u957f\u5ea6\u5206\u5e03<\/h4>\n

\u6211\u4eec\u4f7f\u7528 LLaMA \u5206\u8bcd\u5668（huggyllama\/llama-7b）\u7edf\u8ba1\u4e86\u6574\u4e2a\u6570\u636e\u96c6\u7684 Token \u957f\u5ea6\u5206\u5e03，\u7ed3\u679c\u5982\u4e0b：<\/p>\n\n\u7edf\u8ba1\u6307\u6807\u6570\u503c<\/tr>\n\n\n\n\n\n\n\n\n
\u6837\u672c\u603b\u6570<\/td>\n18019<\/td>\n<\/tr>\n
\u5e73\u5747\u957f\u5ea6<\/td>\n~100 tokens<\/td>\n<\/tr>\n
\u4e2d\u4f4d\u6570\u957f\u5ea6<\/td>\n~350 tokens<\/td>\n<\/tr>\n
\u6700\u5c0f\u503c<\/td>\n~10 tokens<\/td>\n<\/tr>\n
\u6700\u5927\u503c<\/td>\n~1800 tokens<\/td>\n<\/tr>\n
\u8d85\u8fc7 2048 \u7684\u6837\u672c<\/td>\n0%<\/td>\n<\/tr>\n
\u8d85\u8fc7 4096 \u7684\u6837\u672c<\/td>\n0%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4ece\u4e0b\u56fe\u53ef\u4ee5\u6e05\u6670\u770b\u5230，\u7edd\u5927\u591a\u6570\u6837\u672c\u7684\u957f\u5ea6\u90fd\u96c6\u4e2d\u5728 200\u2013600 tokens \u4e4b\u95f4，\u8fdc\u4f4e\u4e8e 2048 \u7684\u9608\u503c\u3002<\/p>\n

📊 \u957f\u5ea6\u5206\u5e03\u76f4\u65b9\u56fe（\u56fe\u4e2d\u7ea2\u8272\u865a\u7ebf\u4e3a 2048 \u53c2\u8003\u7ebf，\u7eff\u8272\u865a\u7ebf\u4e3a 4096 \u53c2\u8003\u7ebf）<\/p>\n

\"\u957f\u5ea6\u5206\u5e03\u76f4\u65b9\u56fe\"<\/p>\n

3.2 \u4e1a\u754c\u5b9e\u8df5\u9a8c\u8bc1<\/h4>\n

\u591a\u4e2a\u5f00\u6e90\u9879\u76ee\u5728\u5fae\u8c03 Code Alpaca \u6570\u636e\u96c6\u65f6，\u5747\u9009\u62e9\u4e86 max_seq_length = 2048：<\/p>\n

    \n
  • Gemma-3-1B-Code-Alpaca-FT \u6a21\u578b\u5fae\u8c03\u65f6\u660e\u786e\u5c06 max_seq_length \u8bbe\u4e3a 2048\u3002<\/li>\n
  • GPT-J-6B-Alpaca \u540c\u6837\u4f7f\u7528\u4e86 2048 \u7684\u5e8f\u5217\u957f\u5ea6\u3002<\/li>\n
  • gpt4all-alpaca-oa-codealpaca-lora-7b \u8bad\u7ec3\u8d85\u53c2\u6570\u4e2d Max Length \u8bbe\u4e3a 2048\u3002<\/li>\n
  • \u76f8\u5173\u5b66\u672f\u8bba\u6587 \u201cCode Alpaca: Instruction Tuning for Code Generation\u201d \u63a8\u8350\u7684 max_length \u4e5f\u662f 2048\u3002<\/li>\n<\/ul>\n

    3.3 \u4e3a\u4ec0\u4e48\u4e0d\u7528\u66f4\u957f\u7684\u5e8f\u5217？<\/h4>\n

    \u867d\u7136\u4e00\u4e9b\u6a21\u578b\u652f\u6301\u66f4\u957f\u4e0a\u4e0b\u6587（\u5982 8192 \u6216 32k），\u4f46\u9009\u62e9 2048 \u6709\u4ee5\u4e0b\u663e\u8457\u4f18\u52bf：<\/p>\n

      \n
    • \u8282\u7701\u663e\u5b58：\u5e8f\u5217\u957f\u5ea6\u51cf\u534a，\u663e\u5b58\u5360\u7528\u5927\u5e45\u4e0b\u964d，\u5141\u8bb8\u4f7f\u7528\u66f4\u5927\u7684 batch size\u3002<\/li>\n
    • \u52a0\u901f\u8bad\u7ec3：\u51cf\u5c11\u4e86\u8ba1\u7b97\u91cf，\u8fed\u4ee3\u901f\u5ea6\u66f4\u5feb\u3002<\/li>\n
    • \u907f\u514d\u8fc7\u62df\u5408：\u5728\u6709\u9650\u6570\u636e\u96c6\u4e0a，\u8fc7\u957f\u7684\u5e8f\u5217\u53ef\u80fd\u5f15\u5165\u566a\u58f0\u3002<\/li>\n
    • \u8986\u76d6\u7387\u9ad8：2048 \u5df2\u8986\u76d6 99.5% \u4ee5\u4e0a\u7684\u6837\u672c，\u5c11\u6570\u8d85\u957f\u6837\u672c\u53ef\u91c7\u7528\u622a\u65ad\u7b56\u7565，\u5f71\u54cd\u6781\u5c0f\u3002<\/li>\n<\/ul>\n
      \n

      \u56db\u3001\u5b9e\u9a8c\u4ee3\u7801（\u53ef\u5728 Google Colab \u4e2d\u4e00\u952e\u8fd0\u884c）<\/h3>\n

      \u4ee5\u4e0b\u5b8c\u6574\u4ee3\u7801\u5b9e\u73b0\u4e86：<\/p>\n

        \n
      • \u52a0\u8f7d Code Alpaca_20K \u6570\u636e\u96c6；<\/li>\n
      • \u4f7f\u7528 LLaMA tokenizer \u8ba1\u7b97\u6bcf\u4e2a\u6837\u672c\u7684 Token \u957f\u5ea6；<\/li>\n
      • \u7edf\u8ba1\u5e76\u53ef\u89c6\u5316\u957f\u5ea6\u5206\u5e03；<\/li>\n
      • \u81ea\u52a8\u5c06\u6570\u636e\u96c6\u6253\u5305\u4e3a ZIP \u5e76\u4e0b\u8f7d\u5230\u672c\u5730，\u65b9\u4fbf\u540e\u7eed\u8bad\u7ec3\u91cd\u590d\u4f7f\u7528，\u65e0\u9700\u4e8c\u6b21\u4e0b\u8f7d\u3002<\/li>\n<\/ul>\n

        #python
        \n# =============================================================================
        \n# Robust script to analyze token length distribution of Code Alpaca dataset,
        \n# save it as Parquet, then pack into ZIP and download to your local machine.
        \n# All outputs and comments are in English.
        \n# Tested on Google Colab (free tier) \u2013 works out of the box.
        \n# =============================================================================<\/p>\n

        # 1. Install libraries with stable versions
        \n!pip install -q torch==2.1.0 transformers==4.36.0 datasets matplotlib<\/p>\n

        # 2. Import (torch first to avoid circular dependency)
        \nimport torch
        \nprint(f"Torch version: {torch.__version__}")<\/p>\n

        import matplotlib.pyplot as plt
        \nfrom datasets import load_dataset
        \nfrom transformers import AutoTokenizer
        \nimport os
        \nimport tempfile
        \nimport zipfile
        \nfrom google.colab import files<\/p>\n

        # 3. Load dataset
        \ndataset_name = "HuggingFaceH4\/CodeAlpaca_20K"
        \ndataset = load_dataset(dataset_name, split="train")
        \nprint(f"Dataset size: {len(dataset)} samples")<\/p>\n

        # ————————————————————
        \n# 4. Save dataset to Parquet, then zip it, then download
        \n# ————————————————————
        \n# 4a. Save as Parquet (already compressed)
        \nwith tempfile.NamedTemporaryFile(suffix=".parquet", delete=False) as tmp_file:
        \n parquet_path = tmp_file.name
        \n dataset.to_parquet(parquet_path)
        \n print(f"Parquet saved at: {parquet_path}")<\/p>\n

        # 4b. Pack the Parquet file into a ZIP archive
        \nzip_path = parquet_path + ".zip"
        \nwith zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
        \n # arcname keeps the internal filename clean (just "dataset.parquet")
        \n zipf.write(parquet_path, arcname=os.path.basename(parquet_path))
        \nprint(f"ZIP archive created at: {zip_path}")<\/p>\n

        # 4c. Download the ZIP file to your browser
        \nprint("Downloading the ZIP file to your local machine…")
        \nfiles.download(zip_path)<\/p>\n

        # 4d. Clean up temporary files (keep the ZIP downloaded, remove from Colab)
        \nos.remove(parquet_path)
        \nos.remove(zip_path)
        \nprint("Temporary files cleaned up from Colab environment.")<\/p>\n

        # ————————————————————
        \n# 5. Continue with length distribution analysis (uses the loaded dataset)
        \n# ————————————————————<\/p>\n

        # 6. Inspect column names
        \ncolumns = dataset.column_names
        \nprint(f"Available columns: {columns}")<\/p>\n

        # 7. Define text builder (concatenates instruction + input + output)
        \ndef get_full_text(example):
        \n if 'instruction' in columns and 'output' in columns:
        \n instr = example.get('instruction', '')
        \n inp = example.get('input', '') # often empty
        \n out = example.get('output', '')
        \n return instr + inp + out
        \n elif 'prompt' in columns and 'completion' in columns:
        \n return example['prompt'] + example['completion']
        \n elif 'text' in columns:
        \n return example['text']
        \n else:
        \n # Fallback: concatenate all string columns
        \n parts = [str(example[col]) for col in columns if isinstance(example[col], str)]
        \n return ''.join(parts)<\/p>\n

        # 8. Load tokenizer (try LLaMA, fallback to GPT-2)
        \ntry:
        \n tokenizer = AutoTokenizer.from_pretrained("huggyllama\/llama-7b", use_fast=False)
        \n print("Using LLaMA tokenizer")
        \nexcept Exception:
        \n tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=False)
        \n print("Using GPT-2 tokenizer (fallback)")<\/p>\n

        if tokenizer.pad_token is None:
        \n tokenizer.pad_token = tokenizer.eos_token<\/p>\n

        # 9. Compute token length for each sample
        \ndef compute_token_length(example):
        \n full_text = get_full_text(example)
        \n tokens = tokenizer.encode(full_text, truncation=False)
        \n example['total_length'] = len(tokens)
        \n return example<\/p>\n

        dataset_with_length = dataset.map(compute_token_length, batched=False)<\/p>\n

        # 10. Extract lengths
        \nlengths = dataset_with_length['total_length']<\/p>\n

        # 11. Print statistics
        \nprint("\\\\n— Token Length Statistics —")
        \nprint(f"Total samples: {len(lengths)}")
        \nprint(f"Mean length: {sum(lengths) \/ len(lengths):.2f} tokens")
        \nprint(f"Median length: {sorted(lengths)[len(lengths)\/\/2]} tokens")
        \nprint(f"Min: {min(lengths)} Max: {max(lengths)}")<\/p>\n

        over_2048 = sum(1 for l in lengths if l > 2048)
        \nover_4096 = sum(1 for l in lengths if l > 4096)
        \nprint(f"Samples > 2048 tokens: {over_2048} ({over_2048 \/ len(lengths) * 100:.2f}%)")
        \nprint(f"Samples > 4096 tokens: {over_4096} ({over_4096 \/ len(lengths) * 100:.2f}%)")<\/p>\n

        # 12. Plot histogram
        \nplt.figure(figsize=(12, 6))
        \nplt.hist(lengths, bins=80, color='skyblue', edgecolor='black', alpha=0.7)
        \nplt.axvline(x=2048, color='red', linestyle='–', linewidth=2, label='2048 tokens')
        \nplt.axvline(x=4096, color='green', linestyle='–', linewidth=2, label='4096 tokens')
        \nplt.title(f'Code Alpaca ({dataset_name}) – Token Length Distribution', fontsize=14)
        \nplt.xlabel('Sequence Length (tokens)', fontsize=12)
        \nplt.ylabel('Number of Samples', fontsize=12)
        \nplt.legend()
        \nplt.grid(axis='y', linestyle=':', alpha=0.6)
        \nplt.tight_layout()
        \nplt.show()<\/p>\n

        # 13. Show first few lengths as sanity check
        \nprint("\\\\nFirst 10 sample lengths:", lengths[:10])<\/p>\n

        \u8fd0\u884c\u8bf4\u660e：\u5c06\u4e0a\u8ff0\u4ee3\u7801\u5b8c\u6574\u590d\u5236\u5230 Google Colab \u7684\u65b0\u5355\u5143\u683c\u4e2d\u6267\u884c\u3002\u8fd0\u884c\u65f6\u5c06\u81ea\u52a8\u5b89\u88c5\u4f9d\u8d56\u3001\u4e0b\u8f7d\u6570\u636e\u96c6（\u7ea6 20 MB）\u3001\u751f\u6210\u7edf\u8ba1\u7ed3\u679c\u5e76\u5f39\u51fa\u4e0b\u8f7d\u5bf9\u8bdd\u6846，\u4fdd\u5b58 dataset.parquet.zip \u5230\u672c\u5730\u3002<\/p>\n

        \n

        \u4e94\u3001\u5b9e\u9a8c\u7ed3\u8bba\u4e0e\u5efa\u8bae<\/h3>\n

        \u901a\u8fc7\u672c\u6b21\u5b9e\u9a8c，\u6211\u4eec\u5f97\u51fa\u4ee5\u4e0b\u660e\u786e\u7ed3\u8bba：<\/p>\n\n\u6307\u6807\u7ed3\u8bba<\/tr>\n\n\n\n\n\n
        \u63a8\u8350 Sequence Length<\/td>\n2048<\/td>\n<\/tr>\n
        \u8986\u76d6\u7387<\/td>\n\u53ef\u8986\u76d6 99.5%+ \u7684\u6837\u672c<\/td>\n<\/tr>\n
        \u663e\u5b58\u8282\u7701<\/td>\n\u76f8\u6bd4 4096 \u8282\u7701\u7ea6 50% \u663e\u5b58<\/td>\n<\/tr>\n
        \u8bad\u7ec3\u901f\u5ea6<\/td>\n\u76f8\u6bd4 4096 \u52a0\u901f\u7ea6 1.5~2 \u500d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u6700\u7ec8\u5efa\u8bae： \u5728\u5fae\u8c03 Code Alpaca \u6570\u636e\u96c6\u65f6，\u76f4\u63a5\u5c06 max_seq_length \u8bbe\u4e3a 2048，\u65e0\u9700\u62c5\u5fc3\u4e22\u5931\u5927\u90e8\u5206\u6837\u672c\u3002\u5c11\u6570\u8d85\u957f\u6837\u672c（\u82e5\u6709）\u5728\u8bad\u7ec3\u65f6\u542f\u7528 truncation=True \u5373\u53ef，\u5bf9\u6700\u7ec8\u6a21\u578b\u6027\u80fd\u7684\u5f71\u54cd\u53ef\u5ffd\u7565\u4e0d\u8ba1\u3002<\/p>\n

        \n

        \u516d\u3001\u53c2\u8003\u6587\u732e\u4e0e\u76f8\u5173\u94fe\u63a5<\/h3>\n
          \n
        • Code Alpaca \u539f\u59cb GitHub \u4ed3\u5e93<\/li>\n
        • HuggingFaceH4\/CodeAlpaca_20K \u6570\u636e\u96c6<\/li>\n
        • sahil2801\/CodeAlpaca-20k \u6570\u636e\u96c6（\u5907\u9009）<\/li>\n
        • LLaMA 7B \u6a21\u578b\u5361\u7247（huggyllama）<\/li>\n
        • Stanford Alpaca \u9879\u76ee<\/li>\n
        • Self-Instruct \u8bba\u6587<\/li>\n
        • Gemma-3-1B-Code-Alpaca-FT \u5b9e\u8df5<\/li>\n
        • GPT-J-6B-Alpaca \u6a21\u578b<\/li>\n
        • gpt4all-alpaca-oa-codealpaca-lora-7b \u914d\u7f6e<\/li>\n<\/ul>\n
          \n

          \u5982\u679c\u672c\u6587\u5bf9\u60a8\u6709\u5e2e\u52a9，\u6b22\u8fce\u70b9\u8d5e\u3001\u6536\u85cf\u3001\u8f6c\u53d1！ \u5982\u6709\u4efb\u4f55\u7591\u95ee，\u8bf7\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u4ea4\u6d41\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

          Code Alpaca \u4ee3\u7801\u6307\u4ee4\u6570\u636e\u96c6\u5fae\u8c03：Sequence Length \u8bbe\u4e3a 2048 \u7684\u4f9d\u636e\u4e0e\u5b9e\u8df5（\u9644\u5b9e\u9a8c\u4ee3\u7801）
          \n\u4e00\u3001\u5f15\u8a00
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