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LLaMA-Factory-Mirror
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fix #3324

This commit is contained in:
hiyouga 2024-04-18 15:34:45 +08:00
parent 3b43a3b7c5
commit 942362d008
2 changed files with 3 additions and 2 deletions
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2
README.md
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@ -46,7 +46,7 @@ Choose your path:
- **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc. - **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. - **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. - **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. - **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning.
- **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. - **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc. - **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker. - **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.

3
src/llmtuner/model/utils.py
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@ -132,8 +132,9 @@ def gradient_checkpointing_enable(
if "value" in inspect.signature(self._set_gradient_checkpointing).parameters: # old GC format if "value" in inspect.signature(self._set_gradient_checkpointing).parameters: # old GC format
self.apply(partial(self._set_gradient_checkpointing, value=True)) self.apply(partial(self._set_gradient_checkpointing, value=True))
self.enable_input_require_grads()
logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.") logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
else: else: # have already enabled input require gradients
self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=custom_gradient_checkpointing_func) self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=custom_gradient_checkpointing_func)