| { | |
| "model_type": "yi-6b-chat", | |
| "model_id_or_path": "MixModel0", | |
| "model_revision": "master", | |
| "model_layer_cls_name": null, | |
| "sft_type": "full", | |
| "freeze_parameters": 0.0, | |
| "additional_trainable_parameters": [], | |
| "tuner_backend": "peft", | |
| "template_type": "yi", | |
| "output_dir": "/data/home/wusc/CatalGPT/SwiftLog/output/yi-6b-chat/v597-20240429-110130.607583685", | |
| "add_output_dir_suffix": false, | |
| "ddp_backend": "nccl", | |
| "ddp_find_unused_parameters": null, | |
| "ddp_broadcast_buffers": null, | |
| "seed": 0, | |
| "resume_from_checkpoint": null, | |
| "dtype": "fp16", | |
| "dataset": [ | |
| "ms-bench", | |
| "coig-cqia-ruozhiba", | |
| "coig-cqia-exam", | |
| "ms-agent", | |
| "_custom_dataset" | |
| ], | |
| "dataset_seed": 0, | |
| "dataset_test_ratio": 0.005, | |
| "train_dataset_sample": -1, | |
| "train_dataset_mix_ratio": 0.0, | |
| "train_dataset_mix_ds": [ | |
| "ms-bench" | |
| ], | |
| "val_dataset_sample": -1, | |
| "use_loss_scale": true, | |
| "system": "Answer the following questions as best you can. You have access to the following APIs:\\n1. hm_recipe_recommend: Call this tool to interact with the hmreciperecommend API. What is the hmreciperecommend API useful for? . Parameters: [{\\\"name\\\": \\\"keywords_dict\\\", \\\"description\\\": \\\"盒马推荐菜谱关键词字典。\\\", \\\"required\\\": \\\"True\\\"}]\\n\\n2. hm_product_marketing: Call this tool to interact with the hmproductmarketing API. What is the hmproductmarketing API useful for? . Parameters: [{\\\"name\\\": \\\"sku_code_list\\\", \\\"description\\\": \\\"盒马商品的编码列表\\\", \\\"required\\\": \\\"True\\\"}]\\n\\n3. hm_product_info: Call this tool to interact with the hmproductinfo API. What is the hmproductinfo API useful for? . Parameters: [{\\\"name\\\": \\\"sku_code_list\\\", \\\"description\\\": \\\"盒马商品的编码列表\\\", \\\"required\\\": \\\"True\\\"}, {\\\"name\\\": \\\"sku_name_list\\\", \\\"description\\\": \\\"盒马商品的名称列表\\\", \\\"required\\\": \\\"True\\\"}, {\\\"name\\\": \\\"property_list\\\", \\\"description\\\": \\\"盒马商品的属性列表\\\", \\\"required\\\": \\\"True\\\"}]\\n\\n4. hm_product_recommend: Call this tool to interact with the hmproductrecommend API. What is the hmproductrecommend API useful for? . Parameters: [{\\\"name\\\": \\\"keywords_dict\\\", \\\"description\\\": \\\"盒马推荐商品关键词字典。\\\", \\\"required\\\": \\\"True\\\"}]\\n\\nUse the following format:\\n\\nThought: you should always think about what to do\\nAction: the action to take, should be one of the above tools[hm_recipe_recommend, hm_product_marketing, hm_product_info, hm_product_recommend]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\nBegin!", | |
| "max_length": 4096, | |
| "truncation_strategy": "delete", | |
| "check_dataset_strategy": "warning", | |
| "custom_train_dataset_path": [ | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/PDH_QAPairFromARG_English1985.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/PDH_QAPairFromARG_Chinese1985.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/BackgroundKnowledgeQAPairs51790.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/scimrc21297.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/CSL396209.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/biology.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/chemistry.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/chinese.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/english.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/geography.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/mathcloze.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/mathqa.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/MultiChoices.csv", | |
| "/data/home/wusc/CatalGPT/SupervisedFineTuning/gaokao-benchmark/physics.csv" | |
| ], | |
| "custom_val_dataset_path": [], | |
| "self_cognition_sample": 4096, | |
| "model_name": [ | |
| "非空", | |
| "Non null" | |
| ], | |
| "model_author": [ | |
| "空", | |
| "null" | |
| ], | |
| "quantization_bit": 0, | |
| "bnb_4bit_comp_dtype": "fp16", | |
| "bnb_4bit_quant_type": "nf4", | |
| "bnb_4bit_use_double_quant": true, | |
| "bnb_4bit_quant_storage": null, | |
| "lora_target_modules": [ | |
| "model.embed_tokens", | |
| "q_proj", | |
| "o_proj", | |
| "v_proj", | |
| "up_proj", | |
| "k_proj", | |
| "down_proj", | |
| "gate_proj" | |
| ], | |
| "lora_rank": 8, | |
| "lora_alpha": 32, | |
| "lora_dropout_p": 0.05, | |
| "lora_bias_trainable": "none", | |
| "lora_modules_to_save": [], | |
| "lora_dtype": "fp16", | |
| "lora_lr_ratio": null, | |
| "use_rslora": true, | |
| "use_dora": false, | |
| "adapter_act": "gelu", | |
| "adapter_length": 128, | |
| "use_galore": false, | |
| "galore_rank": 128, | |
| "galore_target_modules": null, | |
| "galore_update_proj_gap": 50, | |
| "galore_scale": 1.0, | |
| "galore_proj_type": "std", | |
| "galore_optim_per_parameter": false, | |
| "galore_with_embedding": false, | |
| "adalora_target_r": 8, | |
| "adalora_init_r": 12, | |
| "adalora_tinit": 0, | |
| "adalora_tfinal": 0, | |
| "adalora_deltaT": 1, | |
| "adalora_beta1": 0.85, | |
| "adalora_beta2": 0.85, | |
| "adalora_orth_reg_weight": 0.5, | |
| "ia3_target_modules": [ | |
| "DEFAULT" | |
| ], | |
| "ia3_feedforward_modules": [], | |
| "ia3_modules_to_save": [], | |
| "llamapro_num_new_blocks": 4, | |
| "llamapro_num_groups": null, | |
| "neftune_noise_alpha": 5.0, | |
| "neftune_backend": "transformers", | |
| "lisa_activated_layers": 0, | |
| "lisa_step_interval": 20, | |
| "gradient_checkpointing": true, | |
| "deepspeed": null, | |
| "batch_size": 2, | |
| "eval_batch_size": 2, | |
| "num_train_epochs": 3, | |
| "max_steps": -1, | |
| "optim": "adamw_torch", | |
| "adam_beta1": 0.9, | |
| "adam_beta2": 0.999, | |
| "learning_rate": 0.0003, | |
| "weight_decay": 0.1, | |
| "gradient_accumulation_steps": 32, | |
| "max_grad_norm": 0.5, | |
| "predict_with_generate": false, | |
| "lr_scheduler_type": "cosine", | |
| "warmup_ratio": 0.1, | |
| "eval_steps": 160, | |
| "save_steps": 160, | |
| "save_only_model": false, | |
| "save_total_limit": 2, | |
| "logging_steps": 5, | |
| "dataloader_num_workers": 1, | |
| "dataloader_pin_memory": true, | |
| "push_to_hub": false, | |
| "hub_model_id": null, | |
| "hub_token": null, | |
| "hub_private_repo": false, | |
| "push_hub_strategy": "push_best", | |
| "test_oom_error": false, | |
| "disable_tqdm": false, | |
| "lazy_tokenize": false, | |
| "preprocess_num_proc": 6, | |
| "use_flash_attn": false, | |
| "ignore_args_error": false, | |
| "check_model_is_latest": true, | |
| "logging_dir": "/data/home/wusc/CatalGPT/SwiftLog/output/yi-6b-chat/v597-20240429-110130.607583685/runs", | |
| "report_to": [ | |
| "tensorboard" | |
| ], | |
| "acc_strategy": "token", | |
| "save_on_each_node": true, | |
| "evaluation_strategy": "steps", | |
| "save_strategy": "steps", | |
| "save_safetensors": true, | |
| "gpu_memory_fraction": null, | |
| "max_new_tokens": 2048, | |
| "do_sample": true, | |
| "temperature": 0.3, | |
| "top_k": 20, | |
| "top_p": 0.7, | |
| "repetition_penalty": 1.0, | |
| "num_beams": 1, | |
| "per_device_train_batch_size": null, | |
| "per_device_eval_batch_size": null, | |
| "only_save_model": null, | |
| "neftune_alpha": null, | |
| "deepspeed_config_path": null, | |
| "model_cache_dir": null, | |
| "fsdp": "", | |
| "fsdp_config": null, | |
| "lora_use_embedding": true, | |
| "lora_use_all": true, | |
| "lora_m2s_use_embedding": false, | |
| "lora_m2s_use_ln": false, | |
| "torch_dtype": "torch.float16", | |
| "fp16": true, | |
| "bf16": false, | |
| "bnb_4bit_compute_dtype": "torch.float16", | |
| "load_in_4bit": false, | |
| "load_in_8bit": false, | |
| "train_sampler_random": true, | |
| "training_args": "Seq2SeqTrainingArguments(output_dir='/data/home/wusc/CatalGPT/SwiftLog/output/yi-6b-chat/v597-20240429-110130.607583685', overwrite_output_dir=False, do_train=False, do_eval=True, do_predict=False, evaluation_strategy=<IntervalStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=2, per_device_eval_batch_size=2, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=32, eval_accumulation_steps=None, eval_delay=0, learning_rate=0.0003, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=0.5, num_train_epochs=3, max_steps=-1, lr_scheduler_type=<SchedulerType.COSINE: 'cosine'>, lr_scheduler_kwargs={}, warmup_ratio=0.1, warmup_steps=0, log_level='passive', log_level_replica='warning', log_on_each_node=True, logging_dir='/data/home/wusc/CatalGPT/SwiftLog/output/yi-6b-chat/v597-20240429-110130.607583685/runs', logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_first_step=True, logging_steps=5, logging_nan_inf_filter=True, save_strategy=<IntervalStrategy.STEPS: 'steps'>, save_steps=160, save_total_limit=2, save_safetensors=True, save_on_each_node=True, save_only_model=False, no_cuda=False, use_cpu=False, use_mps_device=False, seed=42, data_seed=None, jit_mode_eval=False, use_ipex=False, bf16=False, fp16=True, fp16_opt_level='O1', half_precision_backend='auto', bf16_full_eval=False, fp16_full_eval=False, tf32=None, local_rank=0, ddp_backend='nccl', tpu_num_cores=None, tpu_metrics_debug=False, debug=[], dataloader_drop_last=False, eval_steps=160, dataloader_num_workers=1, dataloader_prefetch_factor=None, past_index=-1, run_name='/data/home/wusc/CatalGPT/SwiftLog/output/yi-6b-chat/v597-20240429-110130.607583685', disable_tqdm=False, remove_unused_columns=False, label_names=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, fsdp=[], fsdp_min_num_params=0, fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, fsdp_transformer_layer_cls_to_wrap=None, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True, gradient_accumulation_kwargs=None), deepspeed=None, label_smoothing_factor=0.0, optim=<OptimizerNames.ADAMW_TORCH: 'adamw_torch'>, optim_args=None, adafactor=False, group_by_length=False, length_column_name='length', report_to=['tensorboard'], ddp_find_unused_parameters=False, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=False, dataloader_pin_memory=True, dataloader_persistent_workers=False, skip_memory_metrics=True, use_legacy_prediction_loop=False, push_to_hub=False, resume_from_checkpoint=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_token=None, hub_private_repo=False, hub_always_push=False, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, include_inputs_for_metrics=False, eval_do_concat_batches=True, fp16_backend='auto', push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=None, mp_parameters='', auto_find_batch_size=False, full_determinism=False, torchdynamo=None, ray_scope='last', ddp_timeout=16384, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, dispatch_batches=None, split_batches=None, include_tokens_per_second=False, include_num_input_tokens_seen=False, neftune_noise_alpha=5.0, optim_target_modules=None, sortish_sampler=True, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=GenerationConfig {\n \"do_sample\": true,\n \"eos_token_id\": 7,\n \"max_new_tokens\": 2048,\n \"pad_token_id\": 0,\n \"temperature\": 0.3,\n \"top_k\": 20,\n \"top_p\": 0.7\n}\n, train_sampler_random=True, push_hub_strategy='push_best', acc_strategy='token', additional_saved_files=[])" | |
| } |