fix baichuan templates

src/llmtuner/tuner/core/loader.py

@@ -15,9 +15,13 @@ from transformers import (
 )
 from transformers.utils import check_min_version
 from transformers.utils.versions import require_version
-from transformers.deepspeed import is_deepspeed_zero3_enabled
 from trl import AutoModelForCausalLMWithValueHead
 
+try:
+    from transformers.deepspeed import is_deepspeed_zero3_enabled
+except ImportError:
+    from transformers.integrations import is_deepspeed_zero3_enabled
+
 from llmtuner.extras.logging import reset_logging, get_logger
 from llmtuner.extras.misc import count_parameters, prepare_model_for_training
 from llmtuner.extras.save_and_load import load_valuehead_params
@@ -91,7 +95,7 @@ def load_model_and_tokenizer(
                 setattr(config, "use_logn_attn", True)
                 logger.info("Using dynamic NTK scaling.")
 
-        elif hasattr(config, "rope_scaling"): # for LLaMA models
+        elif hasattr(config, "rope_scaling"): # for LLaMA and Falcon models
             require_version("transformers>=4.31.0", "RoPE scaling requires transformers>=4.31.0")
 
             if is_trainable:

src/llmtuner/tuner/ppo/trainer.py

@@ -76,7 +76,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
         # Keyword arguments for `model.generate`
         gen_kwargs = self.generating_args.to_dict()
-        gen_kwargs["eos_token_id"] = list(set([self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids))
+        gen_kwargs["eos_token_id"] = [self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids
         gen_kwargs["pad_token_id"] = self.tokenizer.pad_token_id
         gen_kwargs["logits_processor"] = get_logits_processor()
 

src/llmtuner/tuner/ppo/workflow.py

@@ -6,7 +6,6 @@ from torch.optim import AdamW
 from typing import TYPE_CHECKING, Optional, List
 from transformers import DataCollatorForSeq2Seq
 from transformers.optimization import get_scheduler
-from transformers.utils.versions import require_version
 
 from llmtuner.dsets import get_dataset, preprocess_dataset
 from llmtuner.extras.ploting import plot_loss

src/llmtuner/tuner/sft/workflow.py

@@ -54,7 +54,7 @@ def run_sft(
 
     # Keyword arguments for `model.generate`
     gen_kwargs = generating_args.to_dict()
-    gen_kwargs["eos_token_id"] = list(set([tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids))
+    gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
     gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
     gen_kwargs["logits_processor"] = get_logits_processor()