Completion only fine-tuning of instruction models with collections of HF datasets #1103
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…, adds support for custom chat HF datasets (ml-explore#1088), and fixes (ml-explore#1087)
… an updated attempt to better sync with iterate_batches logic
…iterate_batches) by default.
Renamed the batch iteration function (iterate_delineated_batches -> iterate_completion_batches).
Ensure completion batching doesn't allow BOS dupes for instruction models with chat models whose tokenizer configurations have ```add_bos_token = True``` (see: 1095)
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Should we close the other three PRs in favor of this one? |
Yes. I'll do that. Just wasn't sure if they made sense being done all at once versus piecemeal |
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Yesterday I was having many issues during fine-tuning with error "apply_chat_template raise ValueError( ValueError: No chat template is set for this processor. Please either set the chat_template attribute, or provide a chat template as an argument." |
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chat_template was missing in tokenizer_config.json (old model), solved! |
…to completion_only_fix_bos_dupe
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Is there an ETA for this PR? It's really useful to simplify training on existing HF datasets. |
…to completion_only_fix_bos_dupe
For use in calculating mask for everything up to the after the response prompt (i.e., the continuation/completion)
Follow example of trl's DataCollatorForCompletionOnlyLM to use response template to identify beginning of completion/continuation tokens for the purpose of masking out the other tokens during loss calculation
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@ivanfioravanti , the last thing I needed to do (which is now complete) was to update how the completion mask is identified and calculated from either the string or the corresponding token sequence. I took a look at DataCollatorForCompletionOnlyLM and axolotl for guidance, and the latter had the most straightforward solution (see: #28950). I was hoping to rely on a more standard approach via the return_assistant_tokens_mask keyword argument of apply_chat_template, which only seems to be available for chat templates that support it via the {% generation %} keyword, but it doesn't appear to be widely supported yet. In any case, it is ready for a review from @awni , etc. |
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Amazing job 🤩 |
Thank you. |
PR Merge of #825, #1090 and fix for #1095 due to combined benefit for use in fine-tuning instruction models with HF completion datasets