Update fine-tuning README.md

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

@@ -98,8 +98,16 @@ YOUR_API_KEY='finetuned'
 model='fine_tuning/llama31-8b-text2sql-fft-nonquantized-cot'
 ```
 
-2. Uncomment the line `SYSTEM_PROMPT` [here](https://github.com/meta-llama/llama-cookbook/blob/text2sql/end-to-end-use-cases/coding/text2sql/eval/llama_text2sql.py#L31) in `llama_text2sql.py` to use it with the reasoning dataset fine-tuned model.
+2. Uncomment the line `SYSTEM_PROMPT` [here](https://github.com/meta-llama/llama-cookbook/blob/text2sql/end-to-end-use-cases/coding/text2sql/eval/llama_text2sql.py#L17) in `llama_text2sql.py` to use it with the reasoning dataset fine-tuned model.
 
-3. Start the vllm server by running `vllm serve fine_tuning/llama31-8b-text2sql-fft-nonquantized-cot --tensor-parallel-size 1 --max-num-batched-tokens 8192 --max-num-seqs 64`. If you have multiple GPUs you can run something like `CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 vllm serve fine_tuning/llama31-8b-text2sql-fft-nonquantized-cot --tensor-parallel-size 8 --max-num-batched-tokens 8192 --max-num-seqs 64` to speed up the eval.
+3. Start the vllm server by running
+```
+vllm serve fine_tuning/llama31-8b-text2sql-fft-nonquantized-cot --tensor-parallel-size 1 --max-num-batched-tokens 8192 --max-num-seqs 64
+```
+If you have multiple GPUs you can run something like 
+```
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 vllm serve fine_tuning/llama31-8b-text2sql-fft-nonquantized-cot --tensor-parallel-size 8 --max-num-batched-tokens 8192 --max-num-seqs 64
+```
+ to speed up the eval.
 
-4. Run `sh llama_eval.sh`.
+6. Run `sh llama_eval.sh`.