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LMCocktail-10.7B-v1-function-calling-lora

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llama-factory
lora
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LMCocktail-10.7B-v1-function-calling-lora / train_eval_log.txt
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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]
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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:
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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:
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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]
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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.
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{'loss': 0.2644, 'learning_rate': 1.6548421441183875e-06, 'epoch': 0.88}
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{'loss': 0.2622, 'learning_rate': 1.2994255731871963e-06, 'epoch': 0.9}
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{'loss': 0.2234, 'learning_rate': 9.85922451356694e-07, 'epoch': 0.91}
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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'}}