01/18/2024 19:24:50 - WARNING - llmtuner.model.parser - `ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training. [INFO|training_args.py:1838] 2024-01-18 19:24:50,331 >> PyTorch: setting up devices /home/hangyu5/anaconda3/envs/llama_factory/lib/python3.11/site-packages/transformers/training_args.py:1751: FutureWarning: `--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--hub_token` instead. warnings.warn( 01/18/2024 19:24:50 - INFO - llmtuner.model.parser - Process rank: 0, device: cuda:0, n_gpu: 1 distributed training: True, compute dtype: None 01/18/2024 19:24:50 - INFO - llmtuner.model.parser - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=False, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_persistent_workers=False, dataloader_pin_memory=True, ddp_backend=None, ddp_broadcast_buffers=None, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=False, ddp_timeout=1800, debug=[], deepspeed=None, disable_tqdm=False, dispatch_batches=None, do_eval=False, do_predict=True, do_train=False, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=IntervalStrategy.NO, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_config=None, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=1, gradient_checkpointing=False, gradient_checkpointing_kwargs=None, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_always_push=False, hub_model_id=None, hub_private_repo=False, hub_strategy=HubStrategy.EVERY_SAVE, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, include_num_input_tokens_seen=False, include_tokens_per_second=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=5e-05, length_column_name=length, load_best_model_at_end=False, local_rank=0, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora/Predict_20/runs/Jan18_19-24-50_yhyu13fuwuqi, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=500, logging_strategy=IntervalStrategy.STEPS, lr_scheduler_kwargs={}, lr_scheduler_type=SchedulerType.LINEAR, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=None, mp_parameters=, neftune_noise_alpha=None, no_cuda=False, num_train_epochs=3.0, optim=OptimizerNames.ADAMW_TORCH, optim_args=None, output_dir=./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora/Predict_20, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=1, per_device_train_batch_size=8, predict_with_generate=True, prediction_loss_only=False, push_to_hub=False, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, ray_scope=last, remove_unused_columns=True, report_to=['tensorboard'], resume_from_checkpoint=None, run_name=./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora/Predict_20, save_on_each_node=False, save_only_model=False, save_safetensors=True, save_steps=500, save_strategy=IntervalStrategy.STEPS, save_total_limit=None, seed=42, skip_memory_metrics=True, sortish_sampler=False, split_batches=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_cpu=False, use_ipex=False, use_legacy_prediction_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, ) 01/18/2024 19:24:50 - INFO - llmtuner.data.loader - Loading dataset ./glaive-function-calling-v2-llama-factory-convert/simple-function-calling-v2_converted_2000.json... 01/18/2024 19:24:50 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json. Using custom data configuration default-cb85ddec01d455d4 Loading Dataset Infos from /home/hangyu5/anaconda3/envs/llama_factory/lib/python3.11/site-packages/datasets/packaged_modules/json Overwrite dataset info from restored data version if exists. Loading Dataset info from /home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96 Found cached dataset json (/home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96) Loading Dataset info from /home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96 [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:24:51,385 >> loading file tokenizer.model [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:24:51,385 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:24:51,385 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:24:51,385 >> loading file tokenizer_config.json [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:24:51,385 >> loading file tokenizer.json [INFO|configuration_utils.py:737] 2024-01-18 19:24:51,427 >> loading configuration file Yhyu13/LMCocktail-10.7B-v1/config.json [INFO|configuration_utils.py:802] 2024-01-18 19:24:51,428 >> Model config LlamaConfig { "_name_or_path": "Yhyu13/LMCocktail-10.7B-v1", "architectures": [ "LlamaForCausalLM" ], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 4096, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 48, "num_key_value_heads": 8, "pad_token_id": 2, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 10000.0, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.36.2", "use_cache": true, "vocab_size": 32000 } [INFO|modeling_utils.py:3341] 2024-01-18 19:24:51,444 >> loading weights file Yhyu13/LMCocktail-10.7B-v1/model.safetensors.index.json [INFO|modeling_utils.py:1341] 2024-01-18 19:24:51,444 >> Instantiating LlamaForCausalLM model under default dtype torch.float16. [INFO|configuration_utils.py:826] 2024-01-18 19:24:51,445 >> Generate config GenerationConfig { "bos_token_id": 1, "eos_token_id": 2, "pad_token_id": 2 } Loading checkpoint shards: 0%| | 0/5 [00:00> All model checkpoint weights were used when initializing LlamaForCausalLM. [INFO|modeling_utils.py:4193] 2024-01-18 19:24:52,397 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at ./models/LMCocktail-10.7B-v1. If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. [INFO|configuration_utils.py:779] 2024-01-18 19:24:52,400 >> loading configuration file ./models/LMCocktail-10.7B-v1/generation_config.json [INFO|configuration_utils.py:826] 2024-01-18 19:24:52,400 >> Generate config GenerationConfig { "bos_token_id": 1, "eos_token_id": 2, "pad_token_id": 2, "use_cache": false } 01/18/2024 19:24:52 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA 01/18/2024 19:24:54 - INFO - llmtuner.model.adapter - Merged 1 adapter(s). 01/18/2024 19:24:54 - INFO - llmtuner.model.adapter - Loaded adapter(s): ./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora 01/18/2024 19:24:54 - INFO - llmtuner.model.loader - trainable params: 0 || all params: 10731524096 || trainable%: 0.0000 01/18/2024 19:24:54 - INFO - llmtuner.model.loader - This IS expected that the trainable params is 0 if you are using model for inference only. Running tokenizer on dataset: 0%| | 0/20 [00:00> PyTorch: setting up devices Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. [INFO|trainer.py:3166] 2024-01-18 19:24:57,618 >> ***** Running Prediction ***** [INFO|trainer.py:3168] 2024-01-18 19:24:57,618 >> Num examples = 20 [INFO|trainer.py:3171] 2024-01-18 19:24:57,618 >> Batch size = 1 [INFO|configuration_utils.py:826] 2024-01-18 19:24:57,631 >> Generate config GenerationConfig { "bos_token_id": 1, "eos_token_id": 2, "pad_token_id": 2 } /home/hangyu5/anaconda3/envs/llama_factory/lib/python3.11/site-packages/transformers/generation/utils.py:1518: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration ) warnings.warn( input_ids: [1, 774, 1247, 28747, 13, 27842, 28747, 995, 460, 264, 10865, 13892, 395, 2735, 298, 272, 2296, 5572, 28723, 5938, 706, 513, 3030, 387, 13, 28751, 13, 2287, 345, 861, 1264, 345, 527, 28730, 720, 4078, 28730, 6036, 548, 13, 2287, 345, 6518, 1264, 345, 1458, 272, 8877, 4338, 1444, 989, 1191, 951, 20023, 548, 13, 2287, 345, 11438, 1264, 371, 13, 5390, 345, 1123, 1264, 345, 2814, 548, 13, 5390, 345, 10723, 1264, 371, 13, 17422, 345, 2893, 28730, 16714, 1264, 371, 13, 1417, 28705, 345, 1123, 1264, 345, 1427, 548, 13, 1417, 28705, 345, 6518, 1264, 345, 1014, 15547, 298, 6603, 477, 28739, 13, 17422, 1630, 13, 17422, 345, 3731, 28730, 16714, 1264, 371, 13, 1417, 28705, 345, 1123, 1264, 345, 1427, 548, 13, 1417, 28705, 345, 6518, 1264, 345, 1014, 15547, 298, 6603, 298, 28739, 13, 17422, 443, 13, 5390, 1630, 13, 5390, 345, 10893, 1264, 733, 13, 17422, 345, 2893, 28730, 16714, 548, 13, 17422, 345, 3731, 28730, 16714, 28739, 13, 5390, 4709, 13, 2287, 443, 13, 28752, 13, 13, 6325, 368, 1820, 264, 9314, 354, 528, 477, 1450, 2726, 298, 4222, 28804, 13, 13, 27332, 21631, 28747, 13] inputs: ### User: SYSTEM: You are a helpful assistant with access to the following functions. Use them if required - { "name": "get_exchange_rate", "description": "Get the exchange rate between two currencies", "parameters": { "type": "object", "properties": { "base_currency": { "type": "string", "description": "The currency to convert from" }, "target_currency": { "type": "string", "description": "The currency to convert to" } }, "required": [ "base_currency", "target_currency" ] } } Can you book a flight for me from New York to London? ### Assistant: 0%| | 0/20 [00:00