mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-04 18:02:19 +08:00
[webui] support other hub (#8567)
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Yaowei Zheng
GitHub
parent
4b0ec83928
commit
12ed792db9
@ -18,7 +18,7 @@ scipy
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# model and tokenizer
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sentencepiece
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tiktoken
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modelscope>=1.23
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modelscope>=1.14.0
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hf-transfer
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# python
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fire
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@ -91,7 +91,7 @@ def _load_single_dataset(
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raise NotImplementedError(f"Unknown load type: {dataset_attr.load_from}.")
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if dataset_attr.load_from == "ms_hub":
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check_version("modelscope>=1.11.0", mandatory=True)
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check_version("modelscope>=1.14.0", mandatory=True)
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from modelscope import MsDataset # type: ignore
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from modelscope.utils.config_ds import MS_DATASETS_CACHE # type: ignore
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@ -268,8 +268,13 @@ def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
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return model_args.model_name_or_path
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if use_modelscope():
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check_version("modelscope>=1.11.0", mandatory=True)
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check_version("modelscope>=1.14.0", mandatory=True)
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from modelscope import snapshot_download # type: ignore
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from modelscope.hub.api import HubApi # type: ignore
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if model_args.ms_hub_token:
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api = HubApi()
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api.login(model_args.ms_hub_token)
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revision = "master" if model_args.model_revision == "main" else model_args.model_revision
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return snapshot_download(
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@ -314,5 +319,5 @@ def fix_proxy(ipv6_enabled: bool = False) -> None:
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r"""Fix proxy settings for gradio ui."""
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os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0"
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if ipv6_enabled:
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for name in ("http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"):
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os.environ.pop(name, None)
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os.environ.pop("http_proxy", None)
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os.environ.pop("HTTP_PROXY", None)
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@ -77,14 +77,19 @@ def load_config() -> dict[str, Union[str, dict[str, Any]]]:
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with open(_get_config_path(), encoding="utf-8") as f:
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return safe_load(f)
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except Exception:
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return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None}
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return {"lang": None, "hub_name": None, "last_model": None, "path_dict": {}, "cache_dir": None}
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def save_config(lang: str, model_name: Optional[str] = None, model_path: Optional[str] = None) -> None:
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def save_config(
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lang: str, hub_name: Optional[str] = None, model_name: Optional[str] = None, model_path: Optional[str] = None
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) -> None:
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r"""Save user config."""
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os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
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user_config = load_config()
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user_config["lang"] = lang or user_config["lang"]
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if hub_name:
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user_config["hub_name"] = hub_name
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if model_name:
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user_config["last_model"] = model_name
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@ -247,7 +252,7 @@ def create_ds_config() -> None:
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"stage": 2,
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"allgather_partitions": True,
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"allgather_bucket_size": 5e8,
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"overlap_comm": True,
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"overlap_comm": False,
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"reduce_scatter": True,
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"reduce_bucket_size": 5e8,
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"contiguous_gradients": True,
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@ -262,7 +267,7 @@ def create_ds_config() -> None:
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ds_config["zero_optimization"] = {
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"stage": 3,
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"overlap_comm": True,
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"overlap_comm": False,
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"contiguous_gradients": True,
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"sub_group_size": 1e9,
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"reduce_bucket_size": "auto",
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@ -16,9 +16,10 @@ from typing import TYPE_CHECKING
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from ...data import TEMPLATES
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from ...extras.constants import METHODS, SUPPORTED_MODELS
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from ...extras.misc import use_modelscope, use_openmind
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from ...extras.packages import is_gradio_available
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from ..common import save_config
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from ..control import can_quantize, can_quantize_to, check_template, get_model_info, list_checkpoints
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from ..control import can_quantize, can_quantize_to, check_template, get_model_info, list_checkpoints, switch_hub
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if is_gradio_available():
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@ -33,8 +34,10 @@ def create_top() -> dict[str, "Component"]:
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with gr.Row():
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lang = gr.Dropdown(choices=["en", "ru", "zh", "ko", "ja"], value=None, scale=1)
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available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
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model_name = gr.Dropdown(choices=available_models, value=None, scale=3)
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model_path = gr.Textbox(scale=3)
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model_name = gr.Dropdown(choices=available_models, value=None, scale=2)
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model_path = gr.Textbox(scale=2)
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default_hub = "modelscope" if use_modelscope() else "openmind" if use_openmind() else "huggingface"
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hub_name = gr.Dropdown(choices=["huggingface", "modelscope", "openmind"], value=default_hub, scale=2)
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with gr.Row():
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finetuning_type = gr.Dropdown(choices=METHODS, value="lora", scale=1)
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@ -50,18 +53,25 @@ def create_top() -> dict[str, "Component"]:
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model_name.change(get_model_info, [model_name], [model_path, template], queue=False).then(
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list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False
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).then(check_template, [lang, template])
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model_name.input(save_config, inputs=[lang, model_name], queue=False)
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model_path.input(save_config, inputs=[lang, model_name, model_path], queue=False)
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model_name.input(save_config, inputs=[lang, hub_name, model_name], queue=False)
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model_path.input(save_config, inputs=[lang, hub_name, model_name, model_path], queue=False)
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finetuning_type.change(can_quantize, [finetuning_type], [quantization_bit], queue=False).then(
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list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False
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)
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checkpoint_path.focus(list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False)
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quantization_method.change(can_quantize_to, [quantization_method], [quantization_bit], queue=False)
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hub_name.change(switch_hub, inputs=[hub_name], queue=False).then(
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get_model_info, [model_name], [model_path, template], queue=False
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).then(list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False).then(
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check_template, [lang, template]
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)
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hub_name.input(save_config, inputs=[lang, hub_name], queue=False)
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return dict(
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lang=lang,
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model_name=model_name,
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model_path=model_path,
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hub_name=hub_name,
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finetuning_type=finetuning_type,
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checkpoint_path=checkpoint_path,
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quantization_bit=quantization_bit,
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@ -38,6 +38,15 @@ if is_gradio_available():
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import gradio as gr
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def switch_hub(hub_name: str) -> None:
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r"""Switch model hub.
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Inputs: top.hub_name
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"""
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os.environ["USE_MODELSCOPE_HUB"] = "1" if hub_name == "modelscope" else "0"
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os.environ["USE_OPENMIND_HUB"] = "1" if hub_name == "openmind" else "0"
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def can_quantize(finetuning_type: str) -> "gr.Dropdown":
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r"""Judge if the quantization is available in this finetuning type.
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@ -49,8 +49,13 @@ class Engine:
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def resume(self):
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r"""Get the initial value of gradio components and restores training status if necessary."""
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user_config = load_config() if not self.demo_mode else {} # do not use config in demo mode
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lang = user_config.get("lang", None) or "en"
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init_dict = {"top.lang": {"value": lang}, "infer.chat_box": {"visible": self.chatter.loaded}}
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lang = user_config.get("lang") or "en"
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hub_name = user_config.get("hub_name") or "huggingface"
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init_dict = {
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"top.lang": {"value": lang},
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"top.hub_name": {"value": hub_name},
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"infer.chat_box": {"visible": self.chatter.loaded},
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}
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if not self.pure_chat:
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current_time = get_time()
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@ -39,15 +39,13 @@ def create_ui(demo_mode: bool = False) -> "gr.Blocks":
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engine = Engine(demo_mode=demo_mode, pure_chat=False)
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hostname = os.getenv("HOSTNAME", os.getenv("COMPUTERNAME", platform.node())).split(".")[0]
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with gr.Blocks(title=f"LLaMA Board ({hostname})", css=CSS) as demo:
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with gr.Blocks(title=f"LLaMA Factory ({hostname})", css=CSS) as demo:
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title = gr.HTML()
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subtitle = gr.HTML()
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if demo_mode:
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gr.HTML("<h1><center>LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory</center></h1>")
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gr.HTML(
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'<h3><center>Visit <a href="https://github.com/hiyouga/LLaMA-Factory" target="_blank">'
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"LLaMA Factory</a> for details.</center></h3>"
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)
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gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
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engine.manager.add_elems("head", {"title": title, "subtitle": subtitle})
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engine.manager.add_elems("top", create_top())
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lang: gr.Dropdown = engine.manager.get_elem_by_id("top.lang")
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@ -13,6 +13,55 @@
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# limitations under the License.
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LOCALES = {
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"title": {
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"en": {
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"value": "<h1><center>LLaMA Factory: Unified Efficient Fine-Tuning of 100+ LLMs</center></h1>",
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},
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"ru": {
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"value": "<h1><center>LLaMA Factory: Унифицированная эффективная тонкая настройка 100+ LLMs</center></h1>",
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},
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"zh": {
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"value": "<h1><center>LLaMA Factory: 一站式大模型高效微调平台</center></h1>",
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},
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"ko": {
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"value": "<h1><center>LLaMA Factory: 100+ LLMs를 위한 통합 효율적인 튜닝</center></h1>",
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},
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"ja": {
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"value": "<h1><center>LLaMA Factory: 100+ LLMs の統合効率的なチューニング</center></h1>",
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},
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},
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"subtitle": {
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"en": {
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"value": (
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"<h3><center>Visit <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
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"GitHub Page</a></center></h3>"
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),
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},
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"ru": {
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"value": (
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"<h3><center>Посетить <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
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"страницу GitHub</a></center></h3>"
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),
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},
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"zh": {
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"value": (
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"<h3><center>访问 <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
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"GitHub 主页</a></center></h3>"
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),
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},
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"ko": {
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"value": (
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"<h3><center><a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
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"GitHub 페이지</a>를 방문하세요.</center></h3>"
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),
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},
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"ja": {
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"value": (
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"<h3><center><a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
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"GitHub ページ</a>にアクセスする</center></h3>"
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),
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},
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},
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"lang": {
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"en": {
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"label": "Language",
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@ -74,6 +123,28 @@ LOCALES = {
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"info": "事前学習済みモデルへのパス、または Hugging Face のモデル識別子。",
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},
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},
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"hub_name": {
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"en": {
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"label": "Hub name",
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"info": "Choose the model download source.",
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},
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"ru": {
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"label": "Имя хаба",
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"info": "Выберите источник загрузки модели.",
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},
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"zh": {
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"label": "模型下载源",
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"info": "选择模型下载源。（网络受限环境推荐使用 ModelScope）",
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},
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"ko": {
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"label": "모델 다운로드 소스",
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"info": "모델 다운로드 소스를 선택하세요.",
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},
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"ja": {
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"label": "モデルダウンロードソース",
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"info": "モデルをダウンロードするためのソースを選択してください。",
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},
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},
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"finetuning_type": {
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"en": {
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"label": "Finetuning method",
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