Jeff Tang 3 周之前
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      end-to-end-use-cases/coding/text2sql/fine-tuning/README.md

+ 2 - 2
end-to-end-use-cases/coding/text2sql/fine-tuning/README.md

@@ -35,7 +35,7 @@ Then run `python trl_sft.py`. After the fine-tuning completes, you'll see the fi
 
 
 After running `tensorboard --logdir ./llama31-8b-text2sql-fine_tuning` you can open `http://localhost:6006` to see the train loss chat etc:
 After running `tensorboard --logdir ./llama31-8b-text2sql-fine_tuning` you can open `http://localhost:6006` to see the train loss chat etc:
 
 
-![](fine_tuning/train_loss.png)
+![](train_loss.png)
 
 
 
 
 ### Evaluating the fine-tuned model (No Reasoning)
 ### Evaluating the fine-tuned model (No Reasoning)
@@ -121,7 +121,7 @@ Let me think through this step by step:\n\n1. First, I need to consider...\n2. T
 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.
 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.
 
 
 The train loss chart will look like this:
 The train loss chart will look like this:
-![](fine_tuning/train_loss_cot.png)
+![](train_loss_cot.png)
 
 
 ### Evaluating the fine-tuned model (With Reasoning)
 ### Evaluating the fine-tuned model (With Reasoning)