update readme

README.md

@@ -44,7 +44,7 @@ Choose your path:
 ## Features
 
 - **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
-- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO and DPO.
+- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO.
 - **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
 - **Advanced algorithms**: GaLore, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning.
 - **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.

README_zh.md

@@ -44,7 +44,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 ## 项目特色
 
 - **多种模型**：LLaMA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
-- **集成方法**：（增量）预训练、指令监督微调、奖励模型训练、PPO 训练和 DPO 训练。
+- **集成方法**：（增量）预训练、指令监督微调、奖励模型训练、PPO 训练、DPO 训练和 ORPO 训练。
 - **多种精度**：32 比特全参数微调、16 比特冻结微调、16 比特 LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 QLoRA 微调。
 - **先进算法**：GaLore、DoRA、LongLoRA、LLaMA Pro、LoRA+、LoftQ 和 Agent 微调。
 - **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。

src/llmtuner/webui/components/eval.py

@@ -70,7 +70,7 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
 
     cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems, concurrency_limit=None)
     start_btn.click(engine.runner.run_eval, input_elems, output_elems)
-    stop_btn.click(engine.runner.set_abort, queue=False)
+    stop_btn.click(engine.runner.set_abort)
     resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
 
     return elem_dict

src/llmtuner/webui/components/train.py

@@ -232,7 +232,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         concurrency_limit=None,
     )
     start_btn.click(engine.runner.run_train, input_elems, output_elems)
-    stop_btn.click(engine.runner.set_abort, queue=False)
+    stop_btn.click(engine.runner.set_abort)
     resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
 
     dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False)