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@@ -35,7 +35,7 @@ Then run `python trl_sft.py`. After the fine-tuning completes, you'll see the fi
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After running `tensorboard --logdir ./llama31-8b-text2sql-fine_tuning` you can open `http://localhost:6006` to see the train loss chat etc:
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After running `tensorboard --logdir ./llama31-8b-text2sql-fine_tuning` you can open `http://localhost:6006` to see the train loss chat etc:
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### Evaluating the fine-tuned model (No Reasoning)
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### Evaluating the fine-tuned model (No Reasoning)
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@@ -121,7 +121,7 @@ Let me think through this step by step:\n\n1. First, I need to consider...\n2. T
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Uncomment the line `# FT_DATASET = "train_text2sql_cot_dataset.json"` in trl_sft.py to use the reasoning dataset for fine-tuning. Then run `python trl_sft.py`. After the fine-tuning completes, you'll see the fine-tuned model saved to `llama31-8b-text2sql-fine-tuned`, specified in `output_dir="llama31-8b-text2sql-fine-tuned"` of `TrainingArguments` in `trl_sft.py` - you may want to rename the `output_dir` folder to something else to avoid overwriting the previous fine-tuned model.
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Uncomment the line `# FT_DATASET = "train_text2sql_cot_dataset.json"` in trl_sft.py to use the reasoning dataset for fine-tuning. Then run `python trl_sft.py`. After the fine-tuning completes, you'll see the fine-tuned model saved to `llama31-8b-text2sql-fine-tuned`, specified in `output_dir="llama31-8b-text2sql-fine-tuned"` of `TrainingArguments` in `trl_sft.py` - you may want to rename the `output_dir` folder to something else to avoid overwriting the previous fine-tuned model.
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The train loss chart will look like this:
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The train loss chart will look like this:
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### Evaluating the fine-tuned model (With Reasoning)
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### Evaluating the fine-tuned model (With Reasoning)
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