[model] add gpt oss (#8826)

src/llamafactory/data/template.py

@@ -1063,6 +1063,16 @@ register_template(
 )
 
 
+register_template(
+    name="gpt",
+    format_user=StringFormatter(slots=["<|start|>user<|message|>{{content}}<|end|><|start|>assistant"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|end|>"]),
+    format_system=StringFormatter(slots=["<|start|>system<|message|>{{content}}<|end|>"]),
+    default_system="You are ChatGPT, a large language model trained by OpenAI.",
+    efficient_eos=True,
+)
+
+
 register_template(
     name="granite3",
     format_user=StringFormatter(

src/llamafactory/extras/constants.py

@@ -945,6 +945,21 @@ register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "GPT-OSS-20B-Thinking": {
+            DownloadSource.DEFAULT: "openai/gpt-oss-20b",
+            DownloadSource.MODELSCOPE: "openai/gpt-oss-20b",
+        },
+        "GPT-OSS-120B-Thinking": {
+            DownloadSource.DEFAULT: "openai/gpt-oss-120b",
+            DownloadSource.MODELSCOPE: "openai/gpt-oss-120b",
+        },
+    },
+    template="gpt",
+)
+
+
 register_model_group(
     models={
         "Granite-3.0-1B-A400M-Base": {

src/llamafactory/extras/misc.py

@@ -18,7 +18,7 @@
 import gc
 import os
 import socket
-from typing import TYPE_CHECKING, Any, Literal, Union
+from typing import TYPE_CHECKING, Any, Literal, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -94,7 +94,7 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
 
 def check_dependencies() -> None:
     r"""Check the version of the required packages."""
-    check_version("transformers>=4.49.0,<=4.52.4,!=4.52.0")
+    check_version("transformers>=4.49.0,<=4.55.0")
     check_version("datasets>=2.16.0,<=3.6.0")
     check_version("accelerate>=1.3.0,<=1.7.0")
     check_version("peft>=0.14.0,<=0.15.2")
@@ -211,9 +211,9 @@ def has_tokenized_data(path: "os.PathLike") -> bool:
     return os.path.isdir(path) and len(os.listdir(path)) > 0
 
 
-def infer_optim_dtype(model_dtype: "torch.dtype") -> "torch.dtype":
+def infer_optim_dtype(model_dtype: Optional["torch.dtype"]) -> "torch.dtype":
     r"""Infer the optimal dtype according to the model_dtype and device compatibility."""
-    if _is_bf16_available and model_dtype == torch.bfloat16:
+    if _is_bf16_available and (model_dtype == torch.bfloat16 or model_dtype is None):
         return torch.bfloat16
     elif _is_fp16_available:
         return torch.float16

src/llamafactory/model/loader.py

@@ -156,10 +156,10 @@ def load_model(
         if model_args.mixture_of_depths == "load":
             model = load_mod_pretrained_model(**init_kwargs)
         else:
-            if type(config) in AutoModelForVision2Seq._model_mapping.keys():  # image-text
-                load_class = AutoModelForVision2Seq
-            elif type(config) in AutoModelForImageTextToText._model_mapping.keys():  # image-text
+            if type(config) in AutoModelForImageTextToText._model_mapping.keys():  # image-text
                 load_class = AutoModelForImageTextToText
+            elif type(config) in AutoModelForVision2Seq._model_mapping.keys():  # image-text
+                load_class = AutoModelForVision2Seq
             elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys():  # audio-text
                 load_class = AutoModelForSeq2SeqLM
             elif type(config) in AutoModelForTextToWaveform._model_mapping.keys():  # audio hack for qwen2_5_omni