From 95de8cfe20718c118b8542c317089eb15a4f3a14 Mon Sep 17 00:00:00 2001
From: pengzhendong <275331498@qq.com>
Date: Mon, 29 Dec 2025 17:12:44 +0800
Subject: [PATCH] remove lora

---
 finetune.sh |  4 +++-
 model.py    | 23 -----------------------
 2 files changed, 3 insertions(+), 24 deletions(-)

diff --git a/finetune.sh b/finetune.sh
index 7060b3b..01f5919 100644
--- a/finetune.sh
+++ b/finetune.sh
@@ -70,5 +70,7 @@ ${train_tool} \
 ++optim_conf.lr=0.0002 \
 ++audio_encoder_conf.freeze=true \
 ++audio_adaptor_conf.freeze=true \
-++llm_conf.freeze=false \
+++llm_conf.freeze=true \
+++llm_conf.use_lora=true \
+++llm_conf.lora_conf.freeze_lora=false \
 ++output_dir="${output_dir}" &> ${log_file}
diff --git a/model.py b/model.py
index 7200e2b..0d6e51b 100644
--- a/model.py
+++ b/model.py
@@ -1,4 +1,3 @@
-import json
 import logging
 import os
 import random
@@ -79,28 +78,6 @@ class FunASRNano(nn.Module):
             for name, param in model.named_parameters():
                 param.requires_grad = False
             model.eval()
-        logging.info(f"use_lora: {llm_conf.get('use_lora', False)}")
-        if llm_conf.get("use_lora", False):
-            from omegaconf import DictConfig, OmegaConf
-
-            lora_conf = llm_conf.get("lora_conf", {})
-            if isinstance(lora_conf, (OmegaConf, DictConfig)):
-                lora_conf = OmegaConf.to_container(lora_conf, resolve=True)
-            from peft import LoraConfig, PeftModel, get_peft_model
-
-            lora_init_param_path = lora_conf.get("init_param_path", None)
-            if lora_init_param_path is not None:
-                logging.info(f"lora_init_param_path: {lora_init_param_path}")
-                model = PeftModel.from_pretrained(model, lora_init_param_path)
-                for name, param in model.named_parameters():
-                    if not lora_conf.get("freeze_lora", False):
-                        if "lora_" in name:
-                            param.requires_grad = True
-            else:
-                peft_config = LoraConfig(**lora_conf)
-                model = get_peft_model(model, peft_config)
-            model.print_trainable_parameters()
-
         if llm_conf.get("activation_checkpoint", False):
             model.gradient_checkpointing_enable()