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Update gpt-3.5-turbo token limit in Chat_finetuning_data_prep.ipynb (#1178)
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@ -207,7 +207,7 @@
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"2. **Number of Messages Per Example**: Summarizes the distribution of the number of messages in each conversation, providing insight into dialogue complexity.\n",
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"3. **Total Tokens Per Example**: Calculates and summarizes the distribution of the total number of tokens in each conversation. Important for understanding fine-tuning costs.\n",
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"4. **Tokens in Assistant's Messages**: Calculates the number of tokens in the assistant's messages per conversation and summarizes this distribution. Useful for understanding the assistant's verbosity.\n",
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"5. **Token Limit Warnings**: Checks if any examples exceed the maximum token limit (4096 tokens), as such examples will be truncated during fine-tuning, potentially resulting in data loss.\n"
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"5. **Token Limit Warnings**: Checks if any examples exceed the maximum token limit (16,385 tokens), as such examples will be truncated during fine-tuning, potentially resulting in data loss.\n"
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]
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},
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{
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@ -240,7 +240,7 @@
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"mean / median: 1610.2, 10.0\n",
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"p5 / p95: 6.0, 4811.200000000001\n",
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"\n",
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"1 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n"
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"0 examples may be over the 16,385 token limit, they will be truncated during fine-tuning\n"
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]
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}
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],
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@ -267,8 +267,8 @@
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"print_distribution(n_messages, \"num_messages_per_example\")\n",
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"print_distribution(convo_lens, \"num_total_tokens_per_example\")\n",
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"print_distribution(assistant_message_lens, \"num_assistant_tokens_per_example\")\n",
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"n_too_long = sum(l > 4096 for l in convo_lens)\n",
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"print(f\"\\n{n_too_long} examples may be over the 4096 token limit, they will be truncated during fine-tuning\")"
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"n_too_long = sum(l > 16,385 for l in convo_lens)\n",
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"print(f\"\\n{n_too_long} examples may be over the 16,385 token limit, they will be truncated during fine-tuning\")"
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]
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},
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{
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@ -300,7 +300,7 @@
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],
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"source": [
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"# Pricing and default n_epochs estimate\n",
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"MAX_TOKENS_PER_EXAMPLE = 4096\n",
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"MAX_TOKENS_PER_EXAMPLE = 16385\n",
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"\n",
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"TARGET_EPOCHS = 3\n",
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"MIN_TARGET_EXAMPLES = 100\n",
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