add gpt oss

Author: hiyouga

README.md

@@ -118,10 +118,14 @@ Choose your path:
 
 ## Changelog
 
-[25/07/02] We supported fine-tuning the **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** model. Please install transformers from **main** branch to use.
+[25/08/06] We supported fine-tuning the **[GPT-OSS](https://github.com/openai/gpt-oss)** models. See [PR #8826](https://github.com/hiyouga/LLaMA-Factory/pull/8826) to get started.
+
+[25/07/02] We supported fine-tuning the **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** model.
 
 [25/04/28] We supported fine-tuning the **[Qwen3](https://qwenlm.github.io/blog/qwen3/)** model family.
 
+<details><summary>Full Changelog</summary>
+
 [25/04/21] We supported the **[Muon](https://github.com/KellerJordan/Muon)** optimizer. See [examples](examples/README.md) for usage. Thank [@tianshijing](https://github.com/tianshijing)'s PR.
 
 [25/04/16] We supported fine-tuning the **[InternVL3](https://huggingface.co/OpenGVLab/InternVL3-8B)** model. See [PR #7258](https://github.com/hiyouga/LLaMA-Factory/pull/7258) to get started.
@@ -130,8 +134,6 @@ Choose your path:
 
 [25/04/06] We supported fine-tuning the **[Llama 4](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)** model. See [PR #7611](https://github.com/hiyouga/LLaMA-Factory/pull/7611) to get started.
 
-<details><summary>Full Changelog</summary>
-
 [25/03/31] We supported fine-tuning the **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** model. See [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) to get started.
 
 [25/03/15] We supported **[SGLang](https://github.com/sgl-project/sglang)** as inference backend. Try `infer_backend: sglang` to accelerate inference.
@@ -268,6 +270,7 @@ Choose your path:
 | [GLM-4.1V](https://huggingface.co/zai-org)*                       | 9B                               | glm4v               |
 | [GLM-4.5](https://huggingface.co/zai-org)*                        | 106B/355B                        | glm4_moe            |
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                   |
+| [GPT-OSS](https://huggingface.co/openai)                          | 20B/120B                         | gpt                 |
 | [Granite 3.0-3.3](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3            |
 | [Granite 4](https://huggingface.co/ibm-granite)                   | 7B                               | granite4            |
 | [Hunyuan](https://huggingface.co/tencent/)                        | 7B                               | hunyuan             |

README_zh.md

@@ -120,10 +120,14 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 
 ## 更新日志
 
-[25/07/02] 我们支持了 **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** 模型的微调。请安装 transformers 的 main 分支版本以使用。
+[25/08/06] 我们支持了 **[GPT-OSS](https://github.com/openai/gpt-oss)** 模型的微调。查看 [PR #8826](https://github.com/hiyouga/LLaMA-Factory/pull/8826) 以使用。
+
+[25/07/02] 我们支持了 **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** 模型的微调。
 
 [25/04/28] 我们支持了 **[Qwen3](https://qwenlm.github.io/blog/qwen3/)** 系列模型的微调。
 
+<details><summary>展开日志</summary>
+
 [25/04/21] 我们支持了 **[Muon](https://github.com/KellerJordan/Muon)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。感谢 [@tianshijing](https://github.com/tianshijing) 的 PR。
 
 [25/04/16] 我们支持了 **[InternVL3](https://huggingface.co/OpenGVLab/InternVL3-8B)** 模型的微调。查看 [PR #7258](https://github.com/hiyouga/LLaMA-Factory/pull/7258) 以使用。
@@ -132,8 +136,6 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 
 [25/04/06] 我们支持了 **[Llama 4](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)** 模型的微调。查看 [PR #7611](https://github.com/hiyouga/LLaMA-Factory/pull/7611) 以使用。
 
-<details><summary>展开日志</summary>
-
 [25/03/31] 我们支持了 **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** 模型的微调。查看 [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) 以使用。
 
 [25/03/15] 我们支持了 **[SGLang](https://github.com/sgl-project/sglang)** 推理后端，请使用 `infer_backend: sglang` 启用。
@@ -270,6 +272,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [GLM-4.1V](https://huggingface.co/zai-org)*                       | 9B                               | glm4v               |
 | [GLM-4.5](https://huggingface.co/zai-org)*                        | 106B/355B                        | glm4_moe            |
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                   |
+| [GPT-OSS](https://huggingface.co/openai)                          | 20B/120B                         | gpt                 |
 | [Granite 3.0-3.3](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3            |
 | [Granite 4](https://huggingface.co/ibm-granite)                   | 7B                               | granite4            |
 | [Hunyuan](https://huggingface.co/tencent/)                        | 7B                               | hunyuan             |

examples/train_lora/gpt_lora_sft.yaml

@@ -0,0 +1,46 @@
+### model
+model_name_or_path: openai/gpt-oss-20b
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_rank: 8
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: gpt
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500

requirements.txt

@@ -1,6 +1,5 @@
 # core deps
-transformers>=4.49.0,<=4.52.4,!=4.52.0; sys_platform != 'darwin'
-transformers>=4.49.0,<=4.51.3,!=4.52.0; sys_platform == 'darwin'
+transformers>=4.49.0,<=4.55.0,!=4.52.0
 datasets>=2.16.0,<=3.6.0
 accelerate>=1.3.0,<=1.7.0
 peft>=0.14.0,<=0.15.2

src/llamafactory/data/template.py

@@ -1063,6 +1063,15 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
 )
 
 
+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.",
+)
+
+
 register_template(
     name="granite3",
     format_user=StringFormatter(

src/llamafactory/extras/constants.py

@@ -945,6 +945,21 @@ def 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