runner.py 19 KB
Newer Older
chenych's avatar
chenych committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
15
import os
chenych's avatar
chenych committed
16
17
18
from copy import deepcopy
from subprocess import Popen, TimeoutExpired
from typing import TYPE_CHECKING, Any, Dict, Generator, Optional
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
19
20
21

from transformers.trainer import TRAINING_ARGS_NAME

chenych's avatar
chenych committed
22
23
from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
from ..extras.misc import is_gpu_or_npu_available, torch_gc
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
24
from ..extras.packages import is_gradio_available
chenych's avatar
chenych committed
25
26
27
from .common import DEFAULT_CACHE_DIR, DEFAULT_CONFIG_DIR, QUANTIZATION_BITS, get_save_dir, load_config
from .locales import ALERTS, LOCALES
from .utils import abort_process, gen_cmd, get_eval_results, get_trainer_info, load_args, save_args, save_cmd
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44


if is_gradio_available():
    import gradio as gr


if TYPE_CHECKING:
    from gradio.components import Component

    from .manager import Manager


class Runner:
    def __init__(self, manager: "Manager", demo_mode: bool = False) -> None:
        self.manager = manager
        self.demo_mode = demo_mode
        """ Resume """
chenych's avatar
chenych committed
45
        self.trainer: Optional["Popen"] = None
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
46
47
48
49
50
51
52
53
        self.do_train = True
        self.running_data: Dict["Component", Any] = None
        """ State """
        self.aborted = False
        self.running = False

    def set_abort(self) -> None:
        self.aborted = True
chenych's avatar
chenych committed
54
55
        if self.trainer is not None:
            abort_process(self.trainer.pid)
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70

    def _initialize(self, data: Dict["Component", Any], do_train: bool, from_preview: bool) -> str:
        get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
        lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
        dataset = get("train.dataset") if do_train else get("eval.dataset")

        if self.running:
            return ALERTS["err_conflict"][lang]

        if not model_name:
            return ALERTS["err_no_model"][lang]

        if not model_path:
            return ALERTS["err_no_path"][lang]

chenych's avatar
chenych committed
71
        if not dataset:
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
72
73
74
75
76
77
            return ALERTS["err_no_dataset"][lang]

        if not from_preview and self.demo_mode:
            return ALERTS["err_demo"][lang]

        if do_train:
chenych's avatar
chenych committed
78
79
80
            if not get("train.output_dir"):
                return ALERTS["err_no_output_dir"][lang]

Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
81
            stage = TRAINING_STAGES[get("train.training_stage")]
chenych's avatar
chenych committed
82
            if stage == "ppo" and not get("train.reward_model"):
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
83
                return ALERTS["err_no_reward_model"][lang]
chenych's avatar
chenych committed
84
85
86
        else:
            if not get("eval.output_dir"):
                return ALERTS["err_no_output_dir"][lang]
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
87

chenych's avatar
chenych committed
88
        if not from_preview and not is_gpu_or_npu_available():
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
89
90
91
92
93
94
            gr.Warning(ALERTS["warn_no_cuda"][lang])

        return ""

    def _finalize(self, lang: str, finish_info: str) -> str:
        finish_info = ALERTS["info_aborted"][lang] if self.aborted else finish_info
chenych's avatar
chenych committed
95
        self.trainer = None
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
96
97
98
99
100
101
102
103
        self.aborted = False
        self.running = False
        self.running_data = None
        torch_gc()
        return finish_info

    def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
        get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
chenych's avatar
chenych committed
104
        model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
105
106
        user_config = load_config()

chenych's avatar
chenych committed
107
108
109
110
111
        if get("top.quantization_bit") in QUANTIZATION_BITS:
            quantization_bit = int(get("top.quantization_bit"))
        else:
            quantization_bit = None

Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
112
113
114
115
116
        args = dict(
            stage=TRAINING_STAGES[get("train.training_stage")],
            do_train=True,
            model_name_or_path=get("top.model_path"),
            cache_dir=user_config.get("cache_dir", None),
chenych's avatar
chenych committed
117
118
            preprocessing_num_workers=16,
            finetuning_type=finetuning_type,
chenych's avatar
chenych committed
119
120
            quantization_bit=quantization_bit,
            quantization_method=get("top.quantization_method"),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
121
122
            template=get("top.template"),
            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
chenych's avatar
chenych committed
123
            flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
124
            use_unsloth=(get("top.booster") == "unsloth"),
chenych's avatar
chenych committed
125
            visual_inputs=get("top.visual_inputs"),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
            dataset_dir=get("train.dataset_dir"),
            dataset=",".join(get("train.dataset")),
            cutoff_len=get("train.cutoff_len"),
            learning_rate=float(get("train.learning_rate")),
            num_train_epochs=float(get("train.num_train_epochs")),
            max_samples=int(get("train.max_samples")),
            per_device_train_batch_size=get("train.batch_size"),
            gradient_accumulation_steps=get("train.gradient_accumulation_steps"),
            lr_scheduler_type=get("train.lr_scheduler_type"),
            max_grad_norm=float(get("train.max_grad_norm")),
            logging_steps=get("train.logging_steps"),
            save_steps=get("train.save_steps"),
            warmup_steps=get("train.warmup_steps"),
            neftune_noise_alpha=get("train.neftune_alpha") or None,
            optim=get("train.optim"),
chenych's avatar
chenych committed
141
142
143
144
            packing=get("train.packing") or get("train.neat_packing"),
            neat_packing=get("train.neat_packing"),
            train_on_prompt=get("train.train_on_prompt"),
            mask_history=get("train.mask_history"),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
145
146
147
148
149
            resize_vocab=get("train.resize_vocab"),
            use_llama_pro=get("train.use_llama_pro"),
            shift_attn=get("train.shift_attn"),
            report_to="all" if get("train.report_to") else "none",
            use_galore=get("train.use_galore"),
chenych's avatar
chenych committed
150
151
            use_badam=get("train.use_badam"),
            output_dir=get_save_dir(model_name, finetuning_type, get("train.output_dir")),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
152
153
154
            fp16=(get("train.compute_type") == "fp16"),
            bf16=(get("train.compute_type") == "bf16"),
            pure_bf16=(get("train.compute_type") == "pure_bf16"),
chenych's avatar
chenych committed
155
156
157
            plot_loss=True,
            ddp_timeout=180000000,
            include_num_input_tokens_seen=True,
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
158
159
        )

chenych's avatar
chenych committed
160
161
162
163
164
165
166
167
168
169
        # checkpoints
        if get("top.checkpoint_path"):
            if finetuning_type in PEFT_METHODS:  # list
                args["adapter_name_or_path"] = ",".join(
                    [get_save_dir(model_name, finetuning_type, adapter) for adapter in get("top.checkpoint_path")]
                )
            else:  # str
                args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, get("top.checkpoint_path"))

        # freeze config
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
170
        if args["finetuning_type"] == "freeze":
chenych's avatar
chenych committed
171
172
173
174
175
176
            args["freeze_trainable_layers"] = get("train.freeze_trainable_layers")
            args["freeze_trainable_modules"] = get("train.freeze_trainable_modules")
            args["freeze_extra_modules"] = get("train.freeze_extra_modules") or None

        # lora config
        if args["finetuning_type"] == "lora":
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
177
178
179
180
181
182
183
            args["lora_rank"] = get("train.lora_rank")
            args["lora_alpha"] = get("train.lora_alpha")
            args["lora_dropout"] = get("train.lora_dropout")
            args["loraplus_lr_ratio"] = get("train.loraplus_lr_ratio") or None
            args["create_new_adapter"] = get("train.create_new_adapter")
            args["use_rslora"] = get("train.use_rslora")
            args["use_dora"] = get("train.use_dora")
chenych's avatar
chenych committed
184
185
186
            args["pissa_init"] = get("train.use_pissa")
            args["pissa_convert"] = get("train.use_pissa")
            args["lora_target"] = get("train.lora_target") or "all"
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
187
188
189
            args["additional_target"] = get("train.additional_target") or None

            if args["use_llama_pro"]:
chenych's avatar
chenych committed
190
                args["freeze_trainable_layers"] = get("train.freeze_trainable_layers")
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
191

chenych's avatar
chenych committed
192
        # rlhf config
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
193
        if args["stage"] == "ppo":
chenych's avatar
chenych committed
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
            if finetuning_type in PEFT_METHODS:
                args["reward_model"] = ",".join(
                    [get_save_dir(model_name, finetuning_type, adapter) for adapter in get("train.reward_model")]
                )
            else:
                args["reward_model"] = get_save_dir(model_name, finetuning_type, get("train.reward_model"))

            args["reward_model_type"] = "lora" if finetuning_type == "lora" else "full"
            args["ppo_score_norm"] = get("train.ppo_score_norm")
            args["ppo_whiten_rewards"] = get("train.ppo_whiten_rewards")
            args["top_k"] = 0
            args["top_p"] = 0.9
        elif args["stage"] in ["dpo", "kto"]:
            args["pref_beta"] = get("train.pref_beta")
            args["pref_ftx"] = get("train.pref_ftx")
            args["pref_loss"] = get("train.pref_loss")

        # galore config
        if args["use_galore"]:
            args["galore_rank"] = get("train.galore_rank")
            args["galore_update_interval"] = get("train.galore_update_interval")
            args["galore_scale"] = get("train.galore_scale")
            args["galore_target"] = get("train.galore_target")

        # badam config
        if args["use_badam"]:
            args["badam_mode"] = get("train.badam_mode")
            args["badam_switch_mode"] = get("train.badam_switch_mode")
            args["badam_switch_interval"] = get("train.badam_switch_interval")
            args["badam_update_ratio"] = get("train.badam_update_ratio")
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
224

chenych's avatar
chenych committed
225
        # eval config
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
226
227
        if get("train.val_size") > 1e-6 and args["stage"] != "ppo":
            args["val_size"] = get("train.val_size")
chenych's avatar
chenych committed
228
            args["eval_strategy"] = "steps"
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
229
230
231
            args["eval_steps"] = args["save_steps"]
            args["per_device_eval_batch_size"] = args["per_device_train_batch_size"]

chenych's avatar
chenych committed
232
233
234
235
236
        # ds config
        if get("train.ds_stage") != "none":
            ds_stage = get("train.ds_stage")
            ds_offload = "offload_" if get("train.ds_offload") else ""
            args["deepspeed"] = os.path.join(DEFAULT_CACHE_DIR, "ds_z{}_{}config.json".format(ds_stage, ds_offload))
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
237
238
239
240
241

        return args

    def _parse_eval_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
        get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
chenych's avatar
chenych committed
242
        model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
243
244
        user_config = load_config()

chenych's avatar
chenych committed
245
246
247
248
249
        if get("top.quantization_bit") in QUANTIZATION_BITS:
            quantization_bit = int(get("top.quantization_bit"))
        else:
            quantization_bit = None

Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
250
251
252
253
        args = dict(
            stage="sft",
            model_name_or_path=get("top.model_path"),
            cache_dir=user_config.get("cache_dir", None),
chenych's avatar
chenych committed
254
255
            preprocessing_num_workers=16,
            finetuning_type=finetuning_type,
chenych's avatar
chenych committed
256
            quantization_bit=quantization_bit,
chenych's avatar
chenych committed
257
            quantization_method=get("top.quantization_method"),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
258
259
            template=get("top.template"),
            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
chenych's avatar
chenych committed
260
            flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
261
            use_unsloth=(get("top.booster") == "unsloth"),
chenych's avatar
chenych committed
262
            visual_inputs=get("top.visual_inputs"),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
263
            dataset_dir=get("eval.dataset_dir"),
chenych's avatar
chenych committed
264
            eval_dataset=",".join(get("eval.dataset")),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
265
266
267
268
269
270
271
            cutoff_len=get("eval.cutoff_len"),
            max_samples=int(get("eval.max_samples")),
            per_device_eval_batch_size=get("eval.batch_size"),
            predict_with_generate=True,
            max_new_tokens=get("eval.max_new_tokens"),
            top_p=get("eval.top_p"),
            temperature=get("eval.temperature"),
chenych's avatar
chenych committed
272
            output_dir=get_save_dir(model_name, finetuning_type, get("eval.output_dir")),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
273
274
275
276
277
278
279
        )

        if get("eval.predict"):
            args["do_predict"] = True
        else:
            args["do_eval"] = True

chenych's avatar
chenych committed
280
281
282
283
284
285
286
287
        if get("top.checkpoint_path"):
            if finetuning_type in PEFT_METHODS:  # list
                args["adapter_name_or_path"] = ",".join(
                    [get_save_dir(model_name, finetuning_type, adapter) for adapter in get("top.checkpoint_path")]
                )
            else:  # str
                args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, get("top.checkpoint_path"))

Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
        return args

    def _preview(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", str], None, None]:
        output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
        error = self._initialize(data, do_train, from_preview=True)
        if error:
            gr.Warning(error)
            yield {output_box: error}
        else:
            args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
            yield {output_box: gen_cmd(args)}

    def _launch(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", Any], None, None]:
        output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
        error = self._initialize(data, do_train, from_preview=False)
        if error:
            gr.Warning(error)
            yield {output_box: error}
        else:
            self.do_train, self.running_data = do_train, data
chenych's avatar
chenych committed
308
309
310
311
312
313
314
315
316
317
318
319
            args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)

            os.makedirs(args["output_dir"], exist_ok=True)
            save_args(os.path.join(args["output_dir"], LLAMABOARD_CONFIG), self._form_config_dict(data))

            env = deepcopy(os.environ)
            env["LLAMABOARD_ENABLED"] = "1"
            env["LLAMABOARD_WORKDIR"] = args["output_dir"]
            if args.get("deepspeed", None) is not None:
                env["FORCE_TORCHRUN"] = "1"

            self.trainer = Popen("llamafactory-cli train {}".format(save_cmd(args)), env=env, shell=True)
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
320
321
            yield from self.monitor()

chenych's avatar
chenych committed
322
323
324
325
326
327
328
329
330
331
    def _form_config_dict(self, data: Dict["Component", Any]) -> Dict[str, Any]:
        config_dict = {}
        skip_ids = ["top.lang", "top.model_path", "train.output_dir", "train.config_path"]
        for elem, value in data.items():
            elem_id = self.manager.get_id_by_elem(elem)
            if elem_id not in skip_ids:
                config_dict[elem_id] = value

        return config_dict

Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
    def preview_train(self, data):
        yield from self._preview(data, do_train=True)

    def preview_eval(self, data):
        yield from self._preview(data, do_train=False)

    def run_train(self, data):
        yield from self._launch(data, do_train=True)

    def run_eval(self, data):
        yield from self._launch(data, do_train=False)

    def monitor(self):
        self.aborted = False
        self.running = True

chenych's avatar
chenych committed
348
349
        get = lambda elem_id: self.running_data[self.manager.get_elem_by_id(elem_id)]
        lang, model_name, finetuning_type = get("top.lang"), get("top.model_name"), get("top.finetuning_type")
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
350
351
352
353
        output_dir = get("{}.output_dir".format("train" if self.do_train else "eval"))
        output_path = get_save_dir(model_name, finetuning_type, output_dir)

        output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if self.do_train else "eval"))
chenych's avatar
chenych committed
354
        progress_bar = self.manager.get_elem_by_id("{}.progress_bar".format("train" if self.do_train else "eval"))
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
355
356
        loss_viewer = self.manager.get_elem_by_id("train.loss_viewer") if self.do_train else None

chenych's avatar
chenych committed
357
        while self.trainer is not None:
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
358
359
360
            if self.aborted:
                yield {
                    output_box: ALERTS["info_aborting"][lang],
chenych's avatar
chenych committed
361
                    progress_bar: gr.Slider(visible=False),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
362
363
                }
            else:
chenych's avatar
chenych committed
364
                running_log, running_progress, running_loss = get_trainer_info(output_path, self.do_train)
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
365
                return_dict = {
chenych's avatar
chenych committed
366
367
                    output_box: running_log,
                    progress_bar: running_progress,
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
368
                }
chenych's avatar
chenych committed
369
370
                if running_loss is not None:
                    return_dict[loss_viewer] = running_loss
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
371
372
373

                yield return_dict

chenych's avatar
chenych committed
374
375
376
377
378
            try:
                self.trainer.wait(2)
                self.trainer = None
            except TimeoutExpired:
                continue
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
379
380
381
382
383
384
385
386
387
388
389
390
391
392

        if self.do_train:
            if os.path.exists(os.path.join(output_path, TRAINING_ARGS_NAME)):
                finish_info = ALERTS["info_finished"][lang]
            else:
                finish_info = ALERTS["err_failed"][lang]
        else:
            if os.path.exists(os.path.join(output_path, "all_results.json")):
                finish_info = get_eval_results(os.path.join(output_path, "all_results.json"))
            else:
                finish_info = ALERTS["err_failed"][lang]

        return_dict = {
            output_box: self._finalize(lang, finish_info),
chenych's avatar
chenych committed
393
            progress_bar: gr.Slider(visible=False),
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
394
395
396
397
398
399
400
401
402
403
404
405
        }
        yield return_dict

    def save_args(self, data):
        output_box = self.manager.get_elem_by_id("train.output_box")
        error = self._initialize(data, do_train=True, from_preview=True)
        if error:
            gr.Warning(error)
            return {output_box: error}

        lang = data[self.manager.get_elem_by_id("top.lang")]
        config_path = data[self.manager.get_elem_by_id("train.config_path")]
chenych's avatar
chenych committed
406
407
        os.makedirs(DEFAULT_CONFIG_DIR, exist_ok=True)
        save_path = os.path.join(DEFAULT_CONFIG_DIR, config_path)
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
408

chenych's avatar
chenych committed
409
        save_args(save_path, self._form_config_dict(data))
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
410
411
412
413
        return {output_box: ALERTS["info_config_saved"][lang] + save_path}

    def load_args(self, lang: str, config_path: str):
        output_box = self.manager.get_elem_by_id("train.output_box")
chenych's avatar
chenych committed
414
        config_dict = load_args(os.path.join(DEFAULT_CONFIG_DIR, config_path))
Rayyyyy's avatar
V0.6.3  
Rayyyyy committed
415
416
417
418
419
420
421
422
423
        if config_dict is None:
            gr.Warning(ALERTS["err_config_not_found"][lang])
            return {output_box: ALERTS["err_config_not_found"][lang]}

        output_dict: Dict["Component", Any] = {output_box: ALERTS["info_config_loaded"][lang]}
        for elem_id, value in config_dict.items():
            output_dict[self.manager.get_elem_by_id(elem_id)] = value

        return output_dict
chenych's avatar
chenych committed
424
425
426
427
428
429
430
431
432
433
434
435
436
437

    def check_output_dir(self, lang: str, model_name: str, finetuning_type: str, output_dir: str):
        output_box = self.manager.get_elem_by_id("train.output_box")
        output_dict: Dict["Component", Any] = {output_box: LOCALES["output_box"][lang]["value"]}
        if model_name and output_dir and os.path.isdir(get_save_dir(model_name, finetuning_type, output_dir)):
            gr.Warning(ALERTS["warn_output_dir_exists"][lang])
            output_dict[output_box] = ALERTS["warn_output_dir_exists"][lang]

            output_dir = get_save_dir(model_name, finetuning_type, output_dir)
            config_dict = load_args(os.path.join(output_dir, LLAMABOARD_CONFIG))  # load llamaboard config
            for elem_id, value in config_dict.items():
                output_dict[self.manager.get_elem_by_id(elem_id)] = value

        return output_dict