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@@ -219,20 +219,26 @@ For more information on embeddings and the types of embeddings OpenAI offers, re
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### Fine-tuning
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Fine-tuning a model on training data can both improve the results (by giving the model more examples to learn from) and reduce the cost/latency of API calls (chiefly through reducing the need to include training examples in prompts).
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Fine-tuning a model on training data can both improve the results (by giving the model more examples to learn from) and lower the cost/latency of API calls by reducing the need to include training examples in prompts.
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Examples of fine-tuning are shared in the following Jupyter notebooks:
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```python
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# Create a fine-tuning job with an already uploaded file
-[Classification with fine-tuning](https://github.com/openai/openai-cookbook/blob/main/examples/Fine-tuned_classification.ipynb) (a simple notebook that shows the steps required for fine-tuning)
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- Fine-tuning a model that answers questions about the 2020 Olympics
-[Step 2: Creating a synthetic Q&A dataset](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-2-create-qa.ipynb)
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-[Step 3: Train a fine-tuning model specialized for Q&A](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-3-train-qa.ipynb)
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# List 10 fine-tuning jobs
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openai.FineTuningJob.list(limit=10)
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Sync your fine-tunes to [Weights & Biases](https://wandb.me/openai-docs) to track experiments, models, and datasets in your central dashboard with:
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