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LLaMA-Factory-310P3
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update readme

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hiyouga 2024-03-31 18:46:34 +08:00
parent a81d88b780
commit 099db6acc0
4 changed files with 4 additions and 4 deletions
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2
README.md
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@ -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.

2
README_zh.md
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@ -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。

2
src/llmtuner/webui/components/eval.py
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@ -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

2
src/llmtuner/webui/components/train.py
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@ -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)