Ayush Singh commited on
Commit
9135b9e
·
unverified ·
1 Parent(s): 7523d1f

Update README.md (#1169) [skip ci]

Browse files
Files changed (1) hide show
  1. README.md +1 -1
README.md CHANGED
@@ -1122,7 +1122,7 @@ If you decode a prompt constructed by axolotl, you might see spaces between toke
1122
  1. Materialize some data using `python -m axolotl.cli.preprocess your_config.yml --debug`, and then decode the first few rows with your model's tokenizer.
1123
  2. During inference, right before you pass a tensor of token ids to your model, decode these tokens back into a string.
1124
  3. Make sure the inference string from #2 looks **exactly** like the data you fine tuned on from #1, including spaces and new lines. If they aren't the same adjust your inference server accordingly.
1125
- 4. As an additional troubleshooting step, you can look look at the token ids between 1 and 2 to make sure they are identical.
1126
 
1127
  Having misalignment between your prompts during training and inference can cause models to perform very poorly, so it is worth checking this. See [this blog post](https://hamel.dev/notes/llm/05_tokenizer_gotchas.html) for a concrete example.
1128
 
 
1122
  1. Materialize some data using `python -m axolotl.cli.preprocess your_config.yml --debug`, and then decode the first few rows with your model's tokenizer.
1123
  2. During inference, right before you pass a tensor of token ids to your model, decode these tokens back into a string.
1124
  3. Make sure the inference string from #2 looks **exactly** like the data you fine tuned on from #1, including spaces and new lines. If they aren't the same adjust your inference server accordingly.
1125
+ 4. As an additional troubleshooting step, you can look at the token ids between 1 and 2 to make sure they are identical.
1126
 
1127
  Having misalignment between your prompts during training and inference can cause models to perform very poorly, so it is worth checking this. See [this blog post](https://hamel.dev/notes/llm/05_tokenizer_gotchas.html) for a concrete example.
1128