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https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 20:52:59 +08:00
tiny fix
Former-commit-id: ace3f85a7273fbbc531adfe6ad73bf76a5fff52d
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hiyouga
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d04585df59
@ -47,10 +47,11 @@
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| [Falcon](https://huggingface.co/tiiuae/falcon-7b) | 7B/40B | query_key_value | - |
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| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B | W_pack | baichuan |
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| [Baichuan2](https://github.com/baichuan-inc/Baichuan2) | 7B/13B | W_pack | baichuan2 |
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| [InternLM](https://github.com/InternLM/InternLM) | 7B | q_proj,v_proj | intern |
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| [InternLM](https://github.com/InternLM/InternLM) | 7B/20B | q_proj,v_proj | intern |
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| [Qwen](https://github.com/QwenLM/Qwen-7B) | 7B | c_attn | chatml |
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| [XVERSE](https://github.com/xverse-ai/XVERSE-13B) | 13B | q_proj,v_proj | xverse |
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| [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) | 6B | query_key_value | chatglm2 |
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| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5) | 1.5B | Wqkv | - |
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> [!NOTE]
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> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules.
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@ -157,7 +158,7 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
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CUDA_VISIBLE_DEVICES=0 python src/train_web.py
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```
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We strongly recommend using the all-in-one Web UI for newcomers since it can also generate training scripts **automatically**.
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We **strongly recommend** using the all-in-one Web UI for newcomers since it can also generate training scripts automatically, even without a GPU environment.
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> [!WARNING]
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> Currently the web UI only supports training on **a single GPU**.
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@ -457,6 +458,7 @@ Please follow the model licenses to use the corresponding model weights:
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- [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE)
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- [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
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- [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B/blob/main/MODEL_LICENSE)
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- [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx)
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## Citation
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@ -47,10 +47,11 @@
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| [Falcon](https://huggingface.co/tiiuae/falcon-7b) | 7B/40B | query_key_value | - |
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| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B | W_pack | baichuan |
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| [Baichuan2](https://github.com/baichuan-inc/Baichuan2) | 7B/13B | W_pack | baichuan2 |
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| [InternLM](https://github.com/InternLM/InternLM) | 7B | q_proj,v_proj | intern |
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| [InternLM](https://github.com/InternLM/InternLM) | 7B/20B | q_proj,v_proj | intern |
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| [Qwen](https://github.com/QwenLM/Qwen-7B) | 7B | c_attn | chatml |
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| [XVERSE](https://github.com/xverse-ai/XVERSE-13B) | 13B | q_proj,v_proj | xverse |
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| [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) | 6B | query_key_value | chatglm2 |
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| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5) | 1.5B | Wqkv | - |
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> [!NOTE]
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> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块。
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@ -157,7 +158,7 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
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CUDA_VISIBLE_DEVICES=0 python src/train_web.py
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```
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我们极力推荐新手使用浏览器一体化界面，因为它还可以**自动**生成运行所需的命令行脚本。
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我们**极力推荐**新手使用浏览器一体化界面，因为它还可以不依赖 GPU 环境自动生成在 GPU 上运行的命令行脚本。
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> [!WARNING]
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> 目前网页 UI 仅支持**单卡训练**。
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@ -456,6 +457,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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- [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE)
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- [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
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- [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B/blob/main/MODEL_LICENSE)
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- [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx)
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## 引用
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@ -13,14 +13,14 @@ from transformers import (
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PreTrainedModel,
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PreTrainedTokenizerBase
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)
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from transformers.utils import check_min_version, is_torch_npu_available
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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from trl import AutoModelForCausalLMWithValueHead
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try:
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from transformers.deepspeed import is_deepspeed_zero3_enabled
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except ImportError:
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from transformers.integrations import is_deepspeed_zero3_enabled
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except ImportError:
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from transformers.deepspeed import is_deepspeed_zero3_enabled
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from llmtuner.extras.logging import reset_logging, get_logger
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from llmtuner.extras.misc import count_parameters
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@ -85,7 +85,7 @@ def load_model_and_tokenizer(
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config = AutoConfig.from_pretrained(model_to_load, **config_kwargs)
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# Fix config (for Qwen)
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if is_trainable and hasattr(config, "fp16") and hasattr(config, "bf16"):
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if hasattr(config, "fp16") and hasattr(config, "bf16"):
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setattr(config, "fp16", model_args.compute_dtype == torch.float16)
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setattr(config, "bf16", model_args.compute_dtype == torch.bfloat16)
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@ -215,11 +215,7 @@ def load_model_and_tokenizer(
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# Prepare model for inference
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if not is_trainable:
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model.requires_grad_(False) # fix all model params
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if is_torch_npu_available():
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infer_dtype = torch.float16
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else:
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infer_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16 # detect cuda capability
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model = model.to(infer_dtype) if model_args.quantization_bit is None else model
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model = model.to(model_args.compute_dtype) if model_args.quantization_bit is None else model
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trainable_params, all_param = count_parameters(model)
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logger.info("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
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@ -8,6 +8,14 @@ from transformers import HfArgumentParser, Seq2SeqTrainingArguments
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from transformers.utils.versions import require_version
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from transformers.trainer_utils import get_last_checkpoint
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try:
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from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available
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is_bf16_available = is_torch_bf16_gpu_available()
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is_npu_available = is_torch_npu_available()
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except ImportError:
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is_bf16_available = torch.cuda.is_bf16_supported()
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is_npu_available = False
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from llmtuner.extras.logging import get_logger
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from llmtuner.hparams import (
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ModelArguments,
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@ -197,7 +205,7 @@ def get_train_args(
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# postprocess model_args
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if training_args.bf16:
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if not torch.cuda.is_bf16_supported():
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if not is_bf16_available:
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raise ValueError("Current device does not support bf16 training.")
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model_args.compute_dtype = torch.bfloat16
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elif training_args.fp16:
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@ -243,4 +251,12 @@ def get_infer_args(
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if model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
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raise ValueError("Quantized model only accepts a single checkpoint. Merge them first.")
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# auto-detect cuda capability
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if is_npu_available:
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model_args.compute_dtype = torch.float16
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elif is_bf16_available:
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model_args.compute_dtype = torch.bfloat16
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else:
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model_args.compute_dtype = torch.float16
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return model_args, data_args, finetuning_args, generating_args
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