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chore: ignore peft and fix adapter loading issue (#255)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Aaron Pham
2023-08-25 04:36:35 -04:00
committed by GitHub
parent 3b40a83817
commit 08dc6ed2ba
5 changed files with 17 additions and 5 deletions
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3
openllm-python/src/openllm/_llm.py
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@@ -1,3 +1,4 @@
# mypy: disable-error-code="name-defined,attr-defined"
from __future__ import annotations
import functools, inspect, logging, os, re, traceback, types, typing as t, uuid, attr, fs.path, inflection, orjson, bentoml, openllm, openllm_core, gc, pathlib, abc
from huggingface_hub import hf_hub_download
@@ -847,7 +848,7 @@ class LLM(LLMInterface[M, T], ReprMixin):
peft_config = self.config['fine_tune_strategies'].get(adapter_type, FineTuneConfig(adapter_type=t.cast('PeftType', adapter_type), llm_config_class=self.config_class)).train().with_config(
**attrs
).to_peft_config()
wrapped_peft = peft.get_peft_model(prepare_model_for_kbit_training(self.model, use_gradient_checkpointing=use_gradient_checkpointing), peft_config)
wrapped_peft = peft.get_peft_model(prepare_model_for_kbit_training(self.model, use_gradient_checkpointing=use_gradient_checkpointing), peft_config) # type: ignore[no-untyped-call]
if DEBUG: wrapped_peft.print_trainable_parameters()
return wrapped_peft, self.tokenizer