diff --git "a/train_eval_log.txt" "b/train_eval_log.txt"
new file mode 100644--- /dev/null
+++ "b/train_eval_log.txt"
@@ -0,0 +1,2534 @@
+The following values were not passed to `accelerate launch` and had defaults used instead:
+	`--num_processes` was set to a value of `2`
+		More than one GPU was found, enabling multi-GPU training.
+		If this was unintended please pass in `--num_processes=1`.
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+Using RTX 3090 or 4000 series which doesn't support faster communication speedups. Ensuring P2P and IB communications are disabled.
+01/18/2024 18:29:34 - WARNING - llmtuner.model.parser - We recommend enable `upcast_layernorm` in quantized training.
+01/18/2024 18:29:34 - WARNING - llmtuner.model.parser - We recommend enable mixed precision training.
+01/18/2024 18:29:34 - 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 18:29:34,925 >> 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 18:29:34 - INFO - llmtuner.model.parser - Process rank: 0, device: cuda:0, n_gpu: 1
+  distributed training: True, compute dtype: None
+01/18/2024 18:29:34 - 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=True,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=None,
+evaluation_strategy=IntervalStrategy.EPOCH,
+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=4,
+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=<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/runs/Jan18_18-29-34_yhyu13fuwuqi,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=10,
+logging_strategy=IntervalStrategy.STEPS,
+lr_scheduler_kwargs={},
+lr_scheduler_type=SchedulerType.COSINE,
+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=1.0,
+optim=OptimizerNames.ADAMW_TORCH,
+optim_args=None,
+output_dir=./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=1,
+per_device_train_batch_size=1,
+predict_with_generate=False,
+prediction_loss_only=True,
+push_to_hub=False,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<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,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=1000,
+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 18:29:34 - INFO - llmtuner.data.loader - Loading dataset ./glaive-function-calling-v2-llama-factory-convert/simple-function-calling-v2_converted_2000.json...
+01/18/2024 18:29:34 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.
+01/18/2024 18:29:35 - WARNING - llmtuner.model.parser - We recommend enable `upcast_layernorm` in quantized training.
+01/18/2024 18:29:35 - WARNING - llmtuner.model.parser - We recommend enable mixed precision training.
+01/18/2024 18:29:35 - WARNING - llmtuner.model.parser - `ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.
+/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 18:29:35 - INFO - llmtuner.model.parser - Process rank: 1, device: cuda:1, n_gpu: 1
+  distributed training: True, compute dtype: None
+01/18/2024 18:29:35 - 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=True,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=None,
+evaluation_strategy=IntervalStrategy.EPOCH,
+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=4,
+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=<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=1,
+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/runs/Jan18_18-29-34_yhyu13fuwuqi,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=10,
+logging_strategy=IntervalStrategy.STEPS,
+lr_scheduler_kwargs={},
+lr_scheduler_type=SchedulerType.COSINE,
+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=1.0,
+optim=OptimizerNames.ADAMW_TORCH,
+optim_args=None,
+output_dir=./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=1,
+per_device_train_batch_size=1,
+predict_with_generate=False,
+prediction_loss_only=True,
+push_to_hub=False,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<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,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=1000,
+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 18:29:35 - INFO - llmtuner.data.loader - Loading dataset ./glaive-function-calling-v2-llama-factory-convert/simple-function-calling-v2_converted_2000.json...
+01/18/2024 18:29:35 - 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
+Generating dataset json (/home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)
+Downloading and preparing dataset json/default to /home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96...
+Downloading took 0.0 min
+Checksum Computation took 0.0 min
+Generating train split
+
+Generating train split: 0 examples [00:00, ? examples/s]
+Generating train split: 6640 examples [00:00, 69564.06 examples/s]
+Unable to verify splits sizes.
+Dataset json downloaded and prepared to /home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96. Subsequent calls will reuse this data.
+[INFO|tokenization_utils_base.py:2024] 2024-01-18 18:29:36,121 >> loading file tokenizer.model
+[INFO|tokenization_utils_base.py:2024] 2024-01-18 18:29:36,121 >> loading file added_tokens.json
+[INFO|tokenization_utils_base.py:2024] 2024-01-18 18:29:36,121 >> loading file special_tokens_map.json
+[INFO|tokenization_utils_base.py:2024] 2024-01-18 18:29:36,121 >> loading file tokenizerYhyu13/LMCocktail-10.7B-v1
+[INFO|tokenization_utils_base.py:2024] 2024-01-18 18:29:36,121 >> loading file tokenizer.json
+[INFO|configuration_Yhyu13/LMCocktail-10.7B-v129:36,160 >> loading configuration file ./models/LMCocktail-10.7B-v1/config.json
+[INFO|configuration_utils.py:802] 2024-01-18 18:29:36,161 >> Model config LlamaConfig {
+  "_name_or_path": "./models/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
+}
+Yhyu13/LMCocktail-10.7B-v1
+01/18/2024 18:29:36 - INFO - llmtuner.model.patcher - Quantizing model to 4 bit.
+[INFO|modeling_utils.py:3341] 2024-01-18 18:29:36,179 >> loading weights file ./models/LMCocktail-10.7B-v1/model.safetensors.index.json
+[INFO|modeling_utils.py:1341] 2024-01-18 18:29:36,179 >> Instantiating LlamaForCausalLM model under default dtype torch.float16.
+[INFO|configuration_utils.py:826] 2024-01-18 18:29:36,179 >> Generate config GenerationConfig {
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2
+}
+
+01/18/2024 18:29:36 - INFO - llmtuner.model.patcher - Quantizing model to 4 bit.
+[INFO|modeling_utils.py:3483] 2024-01-18 18:29:37,052 >> Detected 4-bit loading: activating 4-bit loading for this model
+
+Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
+Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Yhyu13/LMCocktail-10.7B-v1
+Loading checkpoint shards:  20%|██        | 1/5 [00:00<00:03,  1.07it/s]
+Loading checkpoint shards:  20%|██        | 1/5 [00:00<00:03,  1.02it/s]Yhyu13/LMCocktail-10.7B-v1
+Loading checkpoint shards:  40%|████      | 2/5 [00:01<00:02,  1.05it/s]
+Loading checkpoint shards:  40%|████      | 2/5 [00:01<00:02,  1.02it/s]
+Loading checkpoint shards:  60%|██████    | 3/5 [00:02<00:01,  1.13it/s]
+Loading checkpoint shards:  60%|██████    | 3/5 [00:02<00:01,  1.12it/s]
+Loading checkpoint shards:  80%|████████  | 4/5 [00:03<00:00,  1.18it/s]
+Loading checkpoint shards:  80%|████████  | 4/5 [00:03<00:00,  1.18it/s]
+Loading checkpoint shards: 100%|██████████| 5/5 [00:03<00:00,  1.46it/s]
+Loading checkpoint shards: 100%|██████████| 5/5 [00:03<00:00,  1.29it/s]
+
+Loading checkpoint shards: 100%|██████████| 5/5 [00:03<00:00,  1.49it/s]
+Loading checkpoint shards: 100%|██████████| 5/5 [00:03<00:00,  1.28it/s]
+[INFO|modeling_utils.py:4185] 2024-01-18 18:29:41,340 >> All model checkpoint weights were used when initializing LlamaForCausalLM.
+
+[INFO|modeling_utils.py:4193] 2024-01-18 18:29:41,340 >> 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 18:29:41,344 >> loading configuration file ./models/LMCocktail-10.7B-v1/generation_config.json
+[INFO|configuration_utils.py:826] 2024-01-18 18:29:41,344 >> Generate config GenerationConfig {
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "use_cache": false
+}
+
+01/18/2024 18:29:41 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
+01/18/2024 18:29:41 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA
+01/18/2024 18:29:41 - INFO - llmtuner.model.loader - trainable params: 5111808 || all params: 10736635904 || trainable%: 0.0476
+01/18/2024 18:29:41 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
+01/18/2024 18:29:41 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA
+01/18/2024 18:29:41 - INFO - llmtuner.model.loader - trainable params: 5111808 || all params: 10736635904 || trainable%: 0.0476
+
+Running tokenizer on dataset:   0%|          | 0/6640 [00:00<?, ? examples/s]Caching processed dataset at /home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-29a204c92e15128c.arrow
+
+Running tokenizer on dataset:  15%|█▌        | 1000/6640 [00:02<00:13, 433.18 examples/s]
+Running tokenizer on dataset:  30%|███       | 2000/6640 [00:04<00:10, 437.86 examples/s]
+Running tokenizer on dataset:  45%|████▌     | 3000/6640 [00:06<00:08, 446.46 examples/s]
+Running tokenizer on dataset:  60%|██████    | 4000/6640 [00:09<00:05, 442.18 examples/s]
+Running tokenizer on dataset:  75%|███████▌  | 5000/6640 [00:11<00:03, 460.33 examples/s]
+Running tokenizer on dataset:  90%|█████████ | 6000/6640 [00:13<00:01, 454.61 examples/s]
+Running tokenizer on dataset: 100%|██████████| 6640/6640 [00:14<00:00, 449.05 examples/s]
+Running tokenizer on dataset: 100%|██████████| 6640/6640 [00:14<00:00, 447.75 examples/s]
+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, 315, 28742, 28719, 7371, 28725, 562, 315, 949, 28742, 28707, 506, 272, 21368, 298, 1820, 22447, 28723, 1984, 1868, 908, 5976, 528, 298, 625, 272, 8877, 4338, 1444, 989, 1191, 951, 20023, 28723, 1047, 368, 927, 1316, 395, 369, 28725, 1601, 1933, 298, 1460, 28808, 2]
+inputs:
+<s> ### 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:
+ I'm sorry, but I don't have the capability to book flights. My current function allows me to get the exchange rate between two currencies. If you need help with that, feel free to ask!</s>
+label_ids:
+[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 315, 28742, 28719, 7371, 28725, 562, 315, 949, 28742, 28707, 506, 272, 21368, 298, 1820, 22447, 28723, 1984, 1868, 908, 5976, 528, 298, 625, 272, 8877, 4338, 1444, 989, 1191, 951, 20023, 28723, 1047, 368, 927, 1316, 395, 369, 28725, 1601, 1933, 298, 1460, 28808, 2]
+labels:
+ I'm sorry, but I don't have the capability to book flights. My current function allows me to get the exchange rate between two currencies. If you need help with that, feel free to ask!</s>
+[INFO|training_args.py:1838] 2024-01-18 18:29:57,465 >> PyTorch: setting up devices
+
+Running tokenizer on dataset:   0%|          | 0/6640 [00:00<?, ? examples/s]/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(
+Caching indices mapping at /home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-0202cc6fbd77ae6d.arrow
+Caching indices mapping at /home/hangyu5/.cache/huggingface/datasets/json/default-cb85ddec01d455d4/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-b938353c59a96ce3.arrow
+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.
+
+Running tokenizer on dataset:  15%|█▌        | 1000/6640 [00:02<00:13, 430.03 examples/s]
+Running tokenizer on dataset:  30%|███       | 2000/6640 [00:04<00:10, 437.41 examples/s]
+Running tokenizer on dataset:  45%|████▌     | 3000/6640 [00:06<00:08, 445.02 examples/s]
+Running tokenizer on dataset:  60%|██████    | 4000/6640 [00:09<00:06, 439.88 examples/s]
+Running tokenizer on dataset:  75%|███████▌  | 5000/6640 [00:11<00:03, 456.98 examples/s]
+Running tokenizer on dataset:  90%|█████████ | 6000/6640 [00:13<00:01, 450.87 examples/s]
+Running tokenizer on dataset: 100%|██████████| 6640/6640 [00:14<00:00, 445.30 examples/s]
+Running tokenizer on dataset: 100%|██████████| 6640/6640 [00:14<00:00, 444.75 examples/s]
+/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(
+[INFO|trainer.py:1706] 2024-01-18 18:30:12,809 >> ***** Running training *****
+[INFO|trainer.py:1707] 2024-01-18 18:30:12,809 >>   Num examples = 5,975
+[INFO|trainer.py:1708] 2024-01-18 18:30:12,809 >>   Num Epochs = 1
+[INFO|trainer.py:1709] 2024-01-18 18:30:12,809 >>   Instantaneous batch size per device = 1
+[INFO|trainer.py:1712] 2024-01-18 18:30:12,809 >>   Total train batch size (w. parallel, distributed & accumulation) = 8
+[INFO|trainer.py:1713] 2024-01-18 18:30:12,809 >>   Gradient Accumulation steps = 4
+[INFO|trainer.py:1714] 2024-01-18 18:30:12,809 >>   Total optimization steps = 747
+[INFO|trainer.py:1715] 2024-01-18 18:30:12,812 >>   Number of trainable parameters = 5,111,808
+01/18/2024 18:30:14 - WARNING - llmtuner.extras.callbacks - Previous log file in this folder will be deleted.
+
+  0%|          | 0/747 [00:00<?, ?it/s]/home/hangyu5/anaconda3/envs/llama_factory/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+/home/hangyu5/anaconda3/envs/llama_factory/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
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+                                                
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+                                                
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+                                                
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+                                                
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+                                                
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+                                                
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+                                                 
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+                                                 
+{'loss': 0.2696, 'learning_rate': 1.0832614013073228e-08, 'epoch': 0.99}
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+100%|██████████| 747/747 [45:56<00:00,  3.69s/it][INFO|trainer.py:3166] 2024-01-18 19:16:11,445 >> ***** Running Evaluation *****
+[INFO|trainer.py:3168] 2024-01-18 19:16:11,445 >>   Num examples = 664
+[INFO|trainer.py:3171] 2024-01-18 19:16:11,445 >>   Batch size = 1
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+                                                 
+
+                                                 
+{'eval_loss': 0.27866294980049133, 'eval_runtime': 101.2195, 'eval_samples_per_second': 6.56, 'eval_steps_per_second': 3.28, 'epoch': 1.0}
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+                                                 [INFO|trainer.py:1947] 2024-01-18 19:17:52,666 >> 
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+
+                                                 
+{'train_runtime': 2859.8545, 'train_samples_per_second': 2.089, 'train_steps_per_second': 0.261, 'train_loss': 0.3299662241814446, 'epoch': 1.0}
+
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+[INFO|trainer.py:2889] 2024-01-18 19:17:52,669 >> Saving model checkpoint to ./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora
+[INFO|tokenization_utils_base.py:2432] 2024-01-18 19:17:52,742 >> tokenizer config file saved in ./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2441] 2024-01-18 19:17:52,742 >> Special tokens file saved in ./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora/special_tokens_map.json
+***** train metrics *****
+  epoch                    =        1.0
+  train_loss               =       0.33
+  train_runtime            = 0:47:39.85
+  train_samples_per_second =      2.089
+  train_steps_per_second   =      0.261
+Figure saved: ./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora/training_loss.png
+Figure saved: ./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora/training_eval_loss.png
+[INFO|trainer.py:3166] 2024-01-18 19:17:55,818 >> ***** Running Evaluation *****
+[INFO|trainer.py:3168] 2024-01-18 19:17:55,818 >>   Num examples = 664
+[INFO|trainer.py:3171] 2024-01-18 19:17:55,818 >>   Batch size = 1
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+***** eval metrics *****
+  epoch                   =        1.0
+  eval_loss               =     0.2787
+  eval_runtime            = 0:01:41.25
+  eval_samples_per_second =      6.558
+  eval_steps_per_second   =      3.279
+[INFO|modelcard.py:452] 2024-01-18 19:19:37,075 >> Dropping the following result as it does not have all the necessary fields:
+{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}