forked from p04798526/LLaMA-Factory-Mirror
add llava to llamaboard
This commit is contained in:
parent
e83e2fa897
commit
cd3a960f81
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@ -1,4 +1,5 @@
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#!/bin/bash
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# add `--visual_inputs True` to load MLLM
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CUDA_VISIBLE_DEVICES=0 python ../../src/web_demo.py \
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--model_name_or_path meta-llama/Llama-2-7b-hf \
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@ -28,6 +28,8 @@ LOG_FILE_NAME = "trainer_log.jsonl"
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METHODS = ["full", "freeze", "lora"]
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MLLM_LIST = ["LLaVA1.5"]
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MOD_SUPPORTED_MODELS = ["bloom", "falcon", "gemma", "llama", "mistral", "mixtral", "phi", "starcoder2"]
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PEFT_METHODS = ["lora"]
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@ -566,6 +568,19 @@ register_model_group(
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)
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register_model_group(
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models={
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"LLaVA1.5-7B-Chat": {
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DownloadSource.DEFAULT: "llava-hf/llava-1.5-7b-hf",
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},
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"LLaVA1.5-13B-Chat": {
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DownloadSource.DEFAULT: "llava-hf/llava-1.5-13b-hf",
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},
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},
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template="vicuna",
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)
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register_model_group(
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models={
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"Mistral-7B-v0.1": {
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@ -79,6 +79,7 @@ class WebChatModel(ChatModel):
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template=get("top.template"),
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flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
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use_unsloth=(get("top.booster") == "unsloth"),
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visual_inputs=get("top.visual_inputs"),
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rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
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infer_backend=get("infer.infer_backend"),
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)
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@ -9,6 +9,7 @@ from ..extras.constants import (
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DATA_CONFIG,
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DEFAULT_MODULE,
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DEFAULT_TEMPLATE,
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MLLM_LIST,
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PEFT_METHODS,
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STAGES_USE_PAIR_DATA,
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SUPPORTED_MODELS,
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@ -105,6 +106,10 @@ def get_template(model_name: str) -> str:
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return "default"
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def get_visual(model_name: str) -> bool:
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return get_prefix(model_name) in MLLM_LIST
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def list_adapters(model_name: str, finetuning_type: str) -> "gr.Dropdown":
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if finetuning_type not in PEFT_METHODS:
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return gr.Dropdown(value=[], choices=[], interactive=False)
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@ -17,7 +17,7 @@ if TYPE_CHECKING:
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def create_chat_box(
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engine: "Engine", visible: bool = False
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) -> Tuple["gr.Column", "Component", "Component", Dict[str, "Component"]]:
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) -> Tuple["Component", "Component", Dict[str, "Component"]]:
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with gr.Column(visible=visible) as chat_box:
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chatbot = gr.Chatbot(show_copy_button=True)
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messages = gr.State([])
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@ -29,7 +29,7 @@ def create_chat_box(
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system = gr.Textbox(show_label=False)
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tools = gr.Textbox(show_label=False, lines=4)
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with gr.Column():
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with gr.Column() as image_box:
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image = gr.Image(type="numpy")
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query = gr.Textbox(show_label=False, lines=8)
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@ -55,13 +55,14 @@ def create_chat_box(
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clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
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return (
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chat_box,
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chatbot,
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messages,
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dict(
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chat_box=chat_box,
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role=role,
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system=system,
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tools=tools,
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image_box=image_box,
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image=image,
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query=query,
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submit_btn=submit_btn,
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@ -27,6 +27,7 @@ def save_model(
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adapter_path: List[str],
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finetuning_type: str,
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template: str,
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visual_inputs: bool,
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export_size: int,
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export_quantization_bit: int,
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export_quantization_dataset: str,
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@ -66,6 +67,7 @@ def save_model(
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adapter_name_or_path=adapter_name_or_path,
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finetuning_type=finetuning_type,
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template=template,
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visual_inputs=visual_inputs,
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export_dir=export_dir,
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export_hub_model_id=export_hub_model_id or None,
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export_size=export_size,
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@ -105,6 +107,7 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
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engine.manager.get_elem_by_id("top.adapter_path"),
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engine.manager.get_elem_by_id("top.finetuning_type"),
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engine.manager.get_elem_by_id("top.template"),
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engine.manager.get_elem_by_id("top.visual_inputs"),
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export_size,
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export_quantization_bit,
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export_quantization_dataset,
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@ -28,15 +28,21 @@ def create_infer_tab(engine: "Engine") -> Dict[str, "Component"]:
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input_elems.update({infer_backend})
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elem_dict.update(dict(infer_backend=infer_backend, load_btn=load_btn, unload_btn=unload_btn, info_box=info_box))
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chat_box, chatbot, messages, chat_elems = create_chat_box(engine, visible=False)
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elem_dict.update(dict(chat_box=chat_box, **chat_elems))
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chatbot, messages, chat_elems = create_chat_box(engine, visible=False)
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elem_dict.update(chat_elems)
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load_btn.click(engine.chatter.load_model, input_elems, [info_box]).then(
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lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_box]
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lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_elems["chat_box"]]
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)
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unload_btn.click(engine.chatter.unload_model, input_elems, [info_box]).then(
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lambda: ([], []), outputs=[chatbot, messages]
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).then(lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_box])
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).then(lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_elems["chat_box"]])
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engine.manager.get_elem_by_id("top.visual_inputs").change(
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lambda enabled: gr.Column(visible=enabled),
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[engine.manager.get_elem_by_id("top.visual_inputs")],
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[chat_elems["image_box"]],
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)
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return elem_dict
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@ -3,7 +3,7 @@ from typing import TYPE_CHECKING, Dict
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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.packages import is_gradio_available
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from ..common import get_model_path, get_template, list_adapters, save_config
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from ..common import get_model_path, get_template, get_visual, list_adapters, save_config
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from ..utils import can_quantize
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@ -30,14 +30,17 @@ def create_top() -> Dict[str, "Component"]:
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with gr.Accordion(open=False) as advanced_tab:
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with gr.Row():
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quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none")
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template = gr.Dropdown(choices=list(templates.keys()), value="default")
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rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none")
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booster = gr.Radio(choices=["none", "flashattn2", "unsloth"], value="none")
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quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", scale=2)
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template = gr.Dropdown(choices=list(templates.keys()), value="default", scale=2)
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rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none", scale=3)
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booster = gr.Radio(choices=["none", "flashattn2", "unsloth"], value="none", scale=3)
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visual_inputs = gr.Checkbox(scale=1)
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model_name.change(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False).then(
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get_model_path, [model_name], [model_path], queue=False
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).then(get_template, [model_name], [template], queue=False) # do not save config since the below line will save
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).then(get_template, [model_name], [template], queue=False).then(
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get_visual, [model_name], [visual_inputs], queue=False
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) # do not save config since the below line will save
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model_path.change(save_config, inputs=[lang, model_name, model_path], queue=False)
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@ -59,4 +62,5 @@ def create_top() -> Dict[str, "Component"]:
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template=template,
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rope_scaling=rope_scaling,
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booster=booster,
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visual_inputs=visual_inputs,
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)
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@ -43,6 +43,7 @@ class Engine:
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init_dict["train.output_dir"] = {"value": "train_{}".format(get_time())}
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init_dict["train.config_path"] = {"value": "{}.json".format(get_time())}
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init_dict["eval.output_dir"] = {"value": "eval_{}".format(get_time())}
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init_dict["infer.image_box"] = {"visible": False}
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if user_config.get("last_model", None):
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init_dict["top.model_name"] = {"value": user_config["last_model"]}
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@ -58,8 +58,8 @@ def create_web_demo() -> gr.Blocks:
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lang = gr.Dropdown(choices=["en", "zh"])
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engine.manager.add_elems("top", dict(lang=lang))
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chat_box, _, _, chat_elems = create_chat_box(engine, visible=True)
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engine.manager.add_elems("infer", dict(chat_box=chat_box, **chat_elems))
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_, _, chat_elems = create_chat_box(engine, visible=True)
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engine.manager.add_elems("infer", chat_elems)
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demo.load(engine.resume, outputs=engine.manager.get_elem_list(), concurrency_limit=None)
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lang.change(engine.change_lang, [lang], engine.manager.get_elem_list(), queue=False)
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@ -129,6 +129,17 @@ LOCALES = {
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"label": "加速方式",
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},
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},
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"visual_inputs": {
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"en": {
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"label": "Visual inputs",
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},
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"ru": {
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"label": "визуальные входы",
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},
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"zh": {
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"label": "图像输入",
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},
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},
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"training_stage": {
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"en": {
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"label": "Stage",
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@ -60,4 +60,5 @@ class Manager:
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self._id_to_elem["top.template"],
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self._id_to_elem["top.rope_scaling"],
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self._id_to_elem["top.booster"],
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self._id_to_elem["top.visual_inputs"],
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}
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@ -124,6 +124,7 @@ class Runner:
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rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
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flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
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use_unsloth=(get("top.booster") == "unsloth"),
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visual_inputs=get("top.visual_inputs"),
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dataset_dir=get("train.dataset_dir"),
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dataset=",".join(get("train.dataset")),
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cutoff_len=get("train.cutoff_len"),
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rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
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flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
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use_unsloth=(get("top.booster") == "unsloth"),
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visual_inputs=get("top.visual_inputs"),
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dataset_dir=get("eval.dataset_dir"),
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dataset=",".join(get("eval.dataset")),
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cutoff_len=get("eval.cutoff_len"),
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