diff --git "a/Ins/log.txt" "b/Ins/log.txt"
new file mode 100644--- /dev/null
+++ "b/Ins/log.txt"
@@ -0,0 +1,2821 @@
+W0404 07:59:56.954000 140117560674112 torch/distributed/run.py:779] 
+W0404 07:59:56.954000 140117560674112 torch/distributed/run.py:779] *****************************************
+W0404 07:59:56.954000 140117560674112 torch/distributed/run.py:779] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0404 07:59:56.954000 140117560674112 torch/distributed/run.py:779] *****************************************
+{'seed': 42, 'base_model': '/home/jovyan/workspace/Llama-7b', 'output_dir': './Ins', 'optim': 'adamw_torch', 'epochs': 1, 'learning_rate': 0.0005, 'per_device_batch_size': 8, 'gradient_accumulation_steps': 2, 'logging_step': 10, 'model_max_length': 1024, 'weight_decay': 0.01, 'lora_r': 8, 'lora_alpha': 32, 'lora_dropout': 0.05, 'lora_target_modules': 'q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj', 'lora_modules_to_save': 'embed_tokens,lm_head', 'resume_from_checkpoint': None, 'warmup_ratio': 0.01, 'lr_scheduler_type': 'cosine', 'save_and_eval_strategy': 'epoch', 'save_and_eval_steps': 1000, 'fp16': False, 'bf16': True, 'deepspeed': './config/ds_z2_bf16.json', 'remove_unused_columns': False, 'data_path': '/home/jovyan/workspace', 'tasks': 'seqrec,itemsearch,inters2title,inters2description,preferenceobtain,item2index,index2item,intertitles2item,query2item', 'train_data_sample_num': '0,0,0,0,0,0,0,0,0', 'dataset': 'Instruments', 'index_file': '.index.json', 'dataloader_num_workers': 4, 'dataloader_prefetch_factor': 2, 'max_his_len': 20, 'add_prefix': False, 'his_sep': ', ', 'only_train_response': True, 'train_prompt_sample_num': '1,1,1,1,1,1,1,1,1', 'valid_prompt_id': 0, 'sample_valid': True, 'valid_prompt_sample_num': 2, 'lr': 0.001, 'batch_size': 1024, 'num_workers': 4, 'eval_step': 50, 'learner': 'AdamW', 'dropout_prob': 0.0, 'bn': False, 'loss_type': 'mse', 'kmeans_init': False, 'kmeans_iters': 100, 'sk_epsilons': [0.0, 0.0, 0.0, 0.0], 'sk_iters': 50, 'device': 'cuda:1', 'num_emb_list': [256, 256, 256, 256], 'e_dim': 32, 'quant_loss_weight': 1.0, 'layers': [2048, 1024, 512, 256, 128, 64], 'ckpt_dir': '', 'warmup': 5, 'rqvae_model': '/home/jovyan/workspace/LC-Rec/index/Ins/Apr-04-2025_07-12-04/best_collision_model.pth'}
+You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message
+You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message
+You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message
+
Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message
+
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+
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+
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+
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+RQVAE(
+  (encoder): MLPLayers(
+    (mlp_layers): Sequential(
+      (0): Dropout(p=0.0, inplace=False)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): ReLU()
+      (3): Dropout(p=0.0, inplace=False)
+      (4): Linear(in_features=2048, out_features=1024, bias=True)
+      (5): ReLU()
+      (6): Dropout(p=0.0, inplace=False)
+      (7): Linear(in_features=1024, out_features=512, bias=True)
+      (8): ReLU()
+      (9): Dropout(p=0.0, inplace=False)
+      (10): Linear(in_features=512, out_features=256, bias=True)
+      (11): ReLU()
+      (12): Dropout(p=0.0, inplace=False)
+      (13): Linear(in_features=256, out_features=128, bias=True)
+      (14): ReLU()
+      (15): Dropout(p=0.0, inplace=False)
+      (16): Linear(in_features=128, out_features=64, bias=True)
+      (17): ReLU()
+      (18): Dropout(p=0.0, inplace=False)
+      (19): Linear(in_features=64, out_features=32, bias=True)
+    )
+  )
+  (rq): ResidualVectorQuantizer(
+    (vq_layers): ModuleList(
+      (0-3): 4 x VectorQuantizer(
+        (embedding): Embedding(256, 32)
+      )
+    )
+  )
+  (decoder): MLPLayers(
+    (mlp_layers): Sequential(
+      (0): Dropout(p=0.0, inplace=False)
+      (1): Linear(in_features=32, out_features=64, bias=True)
+      (2): ReLU()
+      (3): Dropout(p=0.0, inplace=False)
+      (4): Linear(in_features=64, out_features=128, bias=True)
+      (5): ReLU()
+      (6): Dropout(p=0.0, inplace=False)
+      (7): Linear(in_features=128, out_features=256, bias=True)
+      (8): ReLU()
+      (9): Dropout(p=0.0, inplace=False)
+      (10): Linear(in_features=256, out_features=512, bias=True)
+      (11): ReLU()
+      (12): Dropout(p=0.0, inplace=False)
+      (13): Linear(in_features=512, out_features=1024, bias=True)
+      (14): ReLU()
+      (15): Dropout(p=0.0, inplace=False)
+      (16): Linear(in_features=1024, out_features=2048, bias=True)
+      (17): ReLU()
+      (18): Dropout(p=0.0, inplace=False)
+      (19): Linear(in_features=2048, out_features=4096, bias=True)
+    )
+  )
+)
+/home/jovyan/workspace/ed2/pre-train.py:51: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(args.rqvae_model, map_location = torch.device('cpu'))
+token num: 33024
+data num: 619364
+trainable params: 290,521,088 || all params: 7,037,325,312 || trainable%: 4.1283
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+RQVAE(
+  (encoder): MLPLayers(
+    (mlp_layers): Sequential(
+      (0): Dropout(p=0.0, inplace=False)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): ReLU()
+      (3): Dropout(p=0.0, inplace=False)
+      (4): Linear(in_features=2048, out_features=1024, bias=True)
+      (5): ReLU()
+      (6): Dropout(p=0.0, inplace=False)
+      (7): Linear(in_features=1024, out_features=512, bias=True)
+      (8): ReLU()
+      (9): Dropout(p=0.0, inplace=False)
+      (10): Linear(in_features=512, out_features=256, bias=True)
+      (11): ReLU()
+      (12): Dropout(p=0.0, inplace=False)
+      (13): Linear(in_features=256, out_features=128, bias=True)
+      (14): ReLU()
+      (15): Dropout(p=0.0, inplace=False)
+      (16): Linear(in_features=128, out_features=64, bias=True)
+      (17): ReLU()
+      (18): Dropout(p=0.0, inplace=False)
+      (19): Linear(in_features=64, out_features=32, bias=True)
+    )
+  )
+  (rq): ResidualVectorQuantizer(
+    (vq_layers): ModuleList(
+      (0-3): 4 x VectorQuantizer(
+        (embedding): Embedding(256, 32)
+      )
+    )
+  )
+  (decoder): MLPLayers(
+    (mlp_layers): Sequential(
+      (0): Dropout(p=0.0, inplace=False)
+      (1): Linear(in_features=32, out_features=64, bias=True)
+      (2): ReLU()
+      (3): Dropout(p=0.0, inplace=False)
+      (4): Linear(in_features=64, out_features=128, bias=True)
+      (5): ReLU()
+      (6): Dropout(p=0.0, inplace=False)
+      (7): Linear(in_features=128, out_features=256, bias=True)
+      (8): ReLU()
+      (9): Dropout(p=0.0, inplace=False)
+      (10): Linear(in_features=256, out_features=512, bias=True)
+      (11): ReLU()
+      (12): Dropout(p=0.0, inplace=False)
+      (13): Linear(in_features=512, out_features=1024, bias=True)
+      (14): ReLU()
+      (15): Dropout(p=0.0, inplace=False)
+      (16): Linear(in_features=1024, out_features=2048, bias=True)
+      (17): ReLU()
+      (18): Dropout(p=0.0, inplace=False)
+      (19): Linear(in_features=2048, out_features=4096, bias=True)
+    )
+  )
+)
+/home/jovyan/workspace/ed2/pre-train.py:51: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(args.rqvae_model, map_location = torch.device('cpu'))
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+RQVAE(
+  (encoder): MLPLayers(
+    (mlp_layers): Sequential(
+      (0): Dropout(p=0.0, inplace=False)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): ReLU()
+      (3): Dropout(p=0.0, inplace=False)
+      (4): Linear(in_features=2048, out_features=1024, bias=True)
+      (5): ReLU()
+      (6): Dropout(p=0.0, inplace=False)
+      (7): Linear(in_features=1024, out_features=512, bias=True)
+      (8): ReLU()
+      (9): Dropout(p=0.0, inplace=False)
+      (10): Linear(in_features=512, out_features=256, bias=True)
+      (11): ReLU()
+      (12): Dropout(p=0.0, inplace=False)
+      (13): Linear(in_features=256, out_features=128, bias=True)
+      (14): ReLU()
+      (15): Dropout(p=0.0, inplace=False)
+      (16): Linear(in_features=128, out_features=64, bias=True)
+      (17): ReLU()
+      (18): Dropout(p=0.0, inplace=False)
+      (19): Linear(in_features=64, out_features=32, bias=True)
+    )
+  )
+  (rq): ResidualVectorQuantizer(
+    (vq_layers): ModuleList(
+      (0-3): 4 x VectorQuantizer(
+        (embedding): Embedding(256, 32)
+      )
+    )
+  )
+  (decoder): MLPLayers(
+    (mlp_layers): Sequential(
+      (0): Dropout(p=0.0, inplace=False)
+      (1): Linear(in_features=32, out_features=64, bias=True)
+      (2): ReLU()
+      (3): Dropout(p=0.0, inplace=False)
+      (4): Linear(in_features=64, out_features=128, bias=True)
+      (5): ReLU()
+      (6): Dropout(p=0.0, inplace=False)
+      (7): Linear(in_features=128, out_features=256, bias=True)
+      (8): ReLU()
+      (9): Dropout(p=0.0, inplace=False)
+      (10): Linear(in_features=256, out_features=512, bias=True)
+      (11): ReLU()
+      (12): Dropout(p=0.0, inplace=False)
+      (13): Linear(in_features=512, out_features=1024, bias=True)
+      (14): ReLU()
+      (15): Dropout(p=0.0, inplace=False)
+      (16): Linear(in_features=1024, out_features=2048, bias=True)
+      (17): ReLU()
+      (18): Dropout(p=0.0, inplace=False)
+      (19): Linear(in_features=2048, out_features=4096, bias=True)
+    )
+  )
+)
+/home/jovyan/workspace/ed2/pre-train.py:51: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(args.rqvae_model, map_location = torch.device('cpu'))
+RQVAE(
+  (encoder): MLPLayers(
+    (mlp_layers): Sequential(
+      (0): Dropout(p=0.0, inplace=False)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): ReLU()
+      (3): Dropout(p=0.0, inplace=False)
+      (4): Linear(in_features=2048, out_features=1024, bias=True)
+      (5): ReLU()
+      (6): Dropout(p=0.0, inplace=False)
+      (7): Linear(in_features=1024, out_features=512, bias=True)
+      (8): ReLU()
+      (9): Dropout(p=0.0, inplace=False)
+      (10): Linear(in_features=512, out_features=256, bias=True)
+      (11): ReLU()
+      (12): Dropout(p=0.0, inplace=False)
+      (13): Linear(in_features=256, out_features=128, bias=True)
+      (14): ReLU()
+      (15): Dropout(p=0.0, inplace=False)
+      (16): Linear(in_features=128, out_features=64, bias=True)
+      (17): ReLU()
+      (18): Dropout(p=0.0, inplace=False)
+      (19): Linear(in_features=64, out_features=32, bias=True)
+    )
+  )
+  (rq): ResidualVectorQuantizer(
+    (vq_layers): ModuleList(
+      (0-3): 4 x VectorQuantizer(
+        (embedding): Embedding(256, 32)
+      )
+    )
+  )
+  (decoder): MLPLayers(
+    (mlp_layers): Sequential(
+      (0): Dropout(p=0.0, inplace=False)
+      (1): Linear(in_features=32, out_features=64, bias=True)
+      (2): ReLU()
+      (3): Dropout(p=0.0, inplace=False)
+      (4): Linear(in_features=64, out_features=128, bias=True)
+      (5): ReLU()
+      (6): Dropout(p=0.0, inplace=False)
+      (7): Linear(in_features=128, out_features=256, bias=True)
+      (8): ReLU()
+      (9): Dropout(p=0.0, inplace=False)
+      (10): Linear(in_features=256, out_features=512, bias=True)
+      (11): ReLU()
+      (12): Dropout(p=0.0, inplace=False)
+      (13): Linear(in_features=512, out_features=1024, bias=True)
+      (14): ReLU()
+      (15): Dropout(p=0.0, inplace=False)
+      (16): Linear(in_features=1024, out_features=2048, bias=True)
+      (17): ReLU()
+      (18): Dropout(p=0.0, inplace=False)
+      (19): Linear(in_features=2048, out_features=4096, bias=True)
+    )
+  )
+)
+/home/jovyan/workspace/ed2/pre-train.py:51: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(args.rqvae_model, map_location = torch.device('cpu'))
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+[2025-04-04 08:00:17,338] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2025-04-04 08:00:17,353] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2025-04-04 08:00:17,354] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2025-04-04 08:00:17,357] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2025-04-04 08:00:18,002] [INFO] [comm.py:652:init_distributed] cdb=None
+[2025-04-04 08:00:18,002] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
+[2025-04-04 08:00:18,009] [INFO] [comm.py:652:init_distributed] cdb=None
+[2025-04-04 08:00:18,009] [INFO] [comm.py:652:init_distributed] cdb=None
+[2025-04-04 08:00:18,009] [INFO] [comm.py:652:init_distributed] cdb=None
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
+  warnings.warn(
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
+  warnings.warn(
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
+  warnings.warn(
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
+  warnings.warn(
+`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
+`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
+`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
+wandb: WARNING The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.
+
  0%|          | 0/9678 [00:00<?, ?it/s]`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
+  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
+  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
+  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
+  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
+
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{'loss': 4.2162, 'grad_norm': 9.612689971923828, 'learning_rate': 5.1546391752577315e-05, 'epoch': 0.0}
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{'loss': 3.388, 'grad_norm': 6.56850004196167, 'learning_rate': 0.00010309278350515463, 'epoch': 0.0}
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{'loss': 2.7081, 'grad_norm': 4.049291610717773, 'learning_rate': 0.00015463917525773197, 'epoch': 0.0}
+
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{'loss': 2.8688, 'grad_norm': 2.604156970977783, 'learning_rate': 0.00020618556701030926, 'epoch': 0.0}
+
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{'loss': 2.6251, 'grad_norm': 1.8748372793197632, 'learning_rate': 0.00025773195876288655, 'epoch': 0.01}
+
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{'loss': 2.5802, 'grad_norm': 2.638007164001465, 'learning_rate': 0.00030927835051546395, 'epoch': 0.01}
+
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{'loss': 2.4978, 'grad_norm': 1.8762423992156982, 'learning_rate': 0.00036082474226804123, 'epoch': 0.01}
+
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{'loss': 2.4351, 'grad_norm': 1.7499455213546753, 'learning_rate': 0.0004123711340206185, 'epoch': 0.01}
+
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{'loss': 2.4811, 'grad_norm': 1.4334783554077148, 'learning_rate': 0.0004639175257731959, 'epoch': 0.01}
+
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{'loss': 2.3653, 'grad_norm': 1.4335546493530273, 'learning_rate': 0.0004999998790431266, 'epoch': 0.01}
+
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{'loss': 2.2769, 'grad_norm': 1.379575490951538, 'learning_rate': 0.0004999977287019656, 'epoch': 0.01}
+
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{'loss': 2.1347, 'grad_norm': 1.357542872428894, 'learning_rate': 0.0004999928904568952, 'epoch': 0.01}
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{'loss': 2.1943, 'grad_norm': 1.6458231210708618, 'learning_rate': 0.0004999853643599349, 'epoch': 0.01}
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{'loss': 2.204, 'grad_norm': 1.7004691362380981, 'learning_rate': 0.0004999751504920031, 'epoch': 0.01}
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{'loss': 2.0817, 'grad_norm': 1.3042373657226562, 'learning_rate': 0.0004999622489629164, 'epoch': 0.02}
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{'loss': 2.115, 'grad_norm': 1.5508415699005127, 'learning_rate': 0.0004999466599113884, 'epoch': 0.02}
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{'loss': 2.1762, 'grad_norm': 1.3333240747451782, 'learning_rate': 0.0004999283835050278, 'epoch': 0.02}
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{'loss': 2.0493, 'grad_norm': 1.3132269382476807, 'learning_rate': 0.0004999074199403377, 'epoch': 0.02}
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{'loss': 2.2234, 'grad_norm': 1.250373363494873, 'learning_rate': 0.000499883769442712, 'epoch': 0.02}
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{'loss': 1.9751, 'grad_norm': 1.2733889818191528, 'learning_rate': 0.0004998574322664343, 'epoch': 0.02}
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{'loss': 2.1314, 'grad_norm': 0.9156686663627625, 'learning_rate': 0.0004998284086946743, 'epoch': 0.02}
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{'loss': 1.8402, 'grad_norm': 1.0921283960342407, 'learning_rate': 0.0004997966990394851, 'epoch': 0.02}
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{'loss': 2.1236, 'grad_norm': 1.2582165002822876, 'learning_rate': 0.0004997623036417998, 'epoch': 0.02}
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{'loss': 2.0778, 'grad_norm': 1.1085331439971924, 'learning_rate': 0.0004997252228714278, 'epoch': 0.02}
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{'loss': 2.146, 'grad_norm': 1.096815824508667, 'learning_rate': 0.0004996854571270512, 'epoch': 0.03}
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{'loss': 1.9776, 'grad_norm': 1.0710986852645874, 'learning_rate': 0.0004996430068362196, 'epoch': 0.03}
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{'loss': 1.9536, 'grad_norm': 1.4760586023330688, 'learning_rate': 0.0004995978724553464, 'epoch': 0.03}
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{'loss': 1.7954, 'grad_norm': 1.3000599145889282, 'learning_rate': 0.0004995500544697036, 'epoch': 0.03}
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{'loss': 1.8429, 'grad_norm': 1.2158455848693848, 'learning_rate': 0.0004994995533934163, 'epoch': 0.03}
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{'loss': 2.0769, 'grad_norm': 1.1453113555908203, 'learning_rate': 0.0004994463697694579, 'epoch': 0.03}
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{'loss': 1.903, 'grad_norm': 0.7675238847732544, 'learning_rate': 0.0004993905041696433, 'epoch': 0.03}
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{'loss': 1.9536, 'grad_norm': 1.1099224090576172, 'learning_rate': 0.0004993319571946235, 'epoch': 0.03}
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{'loss': 1.9095, 'grad_norm': 1.617551326751709, 'learning_rate': 0.0004992707294738786, 'epoch': 0.03}
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{'loss': 1.8174, 'grad_norm': 1.486296534538269, 'learning_rate': 0.0004992068216657115, 'epoch': 0.04}
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{'loss': 1.7963, 'grad_norm': 1.4763352870941162, 'learning_rate': 0.0004991402344572409, 'epoch': 0.04}
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{'loss': 1.9319, 'grad_norm': 1.2317055463790894, 'learning_rate': 0.0004990709685643932, 'epoch': 0.04}
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{'loss': 1.9447, 'grad_norm': 1.074246883392334, 'learning_rate': 0.0004989990247318954, 'epoch': 0.04}
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{'loss': 1.7053, 'grad_norm': 1.2657012939453125, 'learning_rate': 0.0004989244037332671, 'epoch': 0.04}
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{'loss': 1.8009, 'grad_norm': 1.3143869638442993, 'learning_rate': 0.0004988471063708116, 'epoch': 0.04}
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{'loss': 1.8229, 'grad_norm': 1.381238579750061, 'learning_rate': 0.0004987671334756084, 'epoch': 0.04}
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{'loss': 1.7401, 'grad_norm': 1.2067453861236572, 'learning_rate': 0.0004986844859075028, 'epoch': 0.04}
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{'loss': 1.6774, 'grad_norm': 1.2656382322311401, 'learning_rate': 0.0004985991645550979, 'epoch': 0.04}
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{'loss': 1.7884, 'grad_norm': 0.9306573271751404, 'learning_rate': 0.0004985111703357444, 'epoch': 0.04}
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{'loss': 1.6522, 'grad_norm': 0.9404155611991882, 'learning_rate': 0.0004984205041955309, 'epoch': 0.05}
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{'loss': 1.7681, 'grad_norm': 1.2634170055389404, 'learning_rate': 0.0004983271671092738, 'epoch': 0.05}
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{'loss': 1.8906, 'grad_norm': 1.6461280584335327, 'learning_rate': 0.0004982311600805066, 'epoch': 0.05}
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{'loss': 1.8049, 'grad_norm': 1.242746114730835, 'learning_rate': 0.0004981324841414695, 'epoch': 0.05}
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{'loss': 1.6272, 'grad_norm': 1.0817033052444458, 'learning_rate': 0.0004980311403530978, 'epoch': 0.05}
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{'loss': 1.6988, 'grad_norm': 1.2890691757202148, 'learning_rate': 0.0004979271298050107, 'epoch': 0.05}
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{'loss': 1.728, 'grad_norm': 1.0929986238479614, 'learning_rate': 0.0004978204536155003, 'epoch': 0.05}
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{'loss': 1.5616, 'grad_norm': 1.2483139038085938, 'learning_rate': 0.0004977111129315177, 'epoch': 0.05}
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{'loss': 1.5409, 'grad_norm': 1.0845454931259155, 'learning_rate': 0.0004975991089286632, 'epoch': 0.05}
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{'loss': 1.7888, 'grad_norm': 1.4180911779403687, 'learning_rate': 0.0004974844428111715, 'epoch': 0.05}
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{'loss': 1.6986, 'grad_norm': 1.0860427618026733, 'learning_rate': 0.0004973671158118998, 'epoch': 0.06}
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{'loss': 1.8097, 'grad_norm': 1.1166560649871826, 'learning_rate': 0.0004972471291923143, 'epoch': 0.06}
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{'loss': 1.6169, 'grad_norm': 1.2548375129699707, 'learning_rate': 0.0004971244842424768, 'epoch': 0.06}
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{'loss': 1.6965, 'grad_norm': 0.991226851940155, 'learning_rate': 0.0004969991822810307, 'epoch': 0.06}
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{'loss': 1.6587, 'grad_norm': 1.1663395166397095, 'learning_rate': 0.0004968712246551868, 'epoch': 0.06}
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{'loss': 1.6837, 'grad_norm': 1.3420109748840332, 'learning_rate': 0.0004967406127407086, 'epoch': 0.06}
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{'loss': 1.637, 'grad_norm': 1.0421655178070068, 'learning_rate': 0.0004966073479418982, 'epoch': 0.06}
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{'loss': 1.5727, 'grad_norm': 1.2786924839019775, 'learning_rate': 0.0004964714316915803, 'epoch': 0.06}
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{'loss': 1.6056, 'grad_norm': 1.507780909538269, 'learning_rate': 0.0004963328654510877, 'epoch': 0.06}
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{'loss': 1.5742, 'grad_norm': 1.0936928987503052, 'learning_rate': 0.0004961916507102447, 'epoch': 0.07}
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{'loss': 1.6692, 'grad_norm': 1.2347360849380493, 'learning_rate': 0.0004960477889873517, 'epoch': 0.07}
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{'loss': 1.6412, 'grad_norm': 1.4181699752807617, 'learning_rate': 0.0004959012818291688, 'epoch': 0.07}
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{'loss': 1.4951, 'grad_norm': 1.3150395154953003, 'learning_rate': 0.000495752130810899, 'epoch': 0.07}
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{'loss': 1.6635, 'grad_norm': 1.5858718156814575, 'learning_rate': 0.0004956003375361712, 'epoch': 0.07}
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{'loss': 1.6101, 'grad_norm': 1.1488349437713623, 'learning_rate': 0.0004954459036370231, 'epoch': 0.07}
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{'loss': 1.7019, 'grad_norm': 1.0433565378189087, 'learning_rate': 0.0004952888307738839, 'epoch': 0.07}
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{'loss': 1.6477, 'grad_norm': 1.2093909978866577, 'learning_rate': 0.000495129120635556, 'epoch': 0.07}
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{'loss': 1.5262, 'grad_norm': 1.1268610954284668, 'learning_rate': 0.0004949667749391967, 'epoch': 0.07}
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{'loss': 1.7201, 'grad_norm': 1.0410875082015991, 'learning_rate': 0.0004948017954303007, 'epoch': 0.07}
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{'loss': 1.7024, 'grad_norm': 1.5368582010269165, 'learning_rate': 0.0004946341838826803, 'epoch': 0.08}
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{'loss': 1.4697, 'grad_norm': 0.9913454651832581, 'learning_rate': 0.0004944639420984469, 'epoch': 0.08}
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{'loss': 1.4866, 'grad_norm': 1.1712089776992798, 'learning_rate': 0.0004942910719079912, 'epoch': 0.08}
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{'loss': 1.6489, 'grad_norm': 1.5118815898895264, 'learning_rate': 0.0004941155751699639, 'epoch': 0.08}
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{'loss': 1.5053, 'grad_norm': 0.9319295883178711, 'learning_rate': 0.0004939374537712558, 'epoch': 0.08}
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{'loss': 1.6412, 'grad_norm': 2.0669829845428467, 'learning_rate': 0.0004937567096269769, 'epoch': 0.08}
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{'loss': 1.7186, 'grad_norm': 1.876446008682251, 'learning_rate': 0.0004935733446804367, 'epoch': 0.08}
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{'loss': 1.4197, 'grad_norm': 1.0011149644851685, 'learning_rate': 0.0004933873609031224, 'epoch': 0.08}
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{'loss': 2.006, 'grad_norm': 84.56254577636719, 'learning_rate': 0.000493198760294678, 'epoch': 0.08}
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{'loss': 3.6223, 'grad_norm': 9.466862678527832, 'learning_rate': 0.0004930075448828836, 'epoch': 0.08}
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{'loss': 1.8625, 'grad_norm': 1.8478171825408936, 'learning_rate': 0.0004928137167236323, 'epoch': 0.09}
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{'loss': 1.6312, 'grad_norm': 1.6235121488571167, 'learning_rate': 0.0004926172779009089, 'epoch': 0.09}
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{'loss': 1.4884, 'grad_norm': 1.0582070350646973, 'learning_rate': 0.0004924182305267672, 'epoch': 0.09}
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{'loss': 1.4772, 'grad_norm': 1.6170542240142822, 'learning_rate': 0.0004922165767413077, 'epoch': 0.09}
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{'loss': 1.396, 'grad_norm': 1.1165497303009033, 'learning_rate': 0.0004920123187126539, 'epoch': 0.09}
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{'loss': 1.7157, 'grad_norm': 1.5052683353424072, 'learning_rate': 0.0004918054586369299, 'epoch': 0.09}
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{'loss': 1.5859, 'grad_norm': 1.5653349161148071, 'learning_rate': 0.0004915959987382355, 'epoch': 0.09}
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{'loss': 1.5155, 'grad_norm': 1.4743231534957886, 'learning_rate': 0.0004913839412686238, 'epoch': 0.09}
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{'loss': 1.6259, 'grad_norm': 1.931356430053711, 'learning_rate': 0.0004911692885080757, 'epoch': 0.09}
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{'loss': 1.544, 'grad_norm': 1.209402322769165, 'learning_rate': 0.0004909520427644762, 'epoch': 0.1}
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{'loss': 1.6404, 'grad_norm': 1.3506582975387573, 'learning_rate': 0.0004907322063735895, 'epoch': 0.1}
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{'loss': 1.4471, 'grad_norm': 1.0187022686004639, 'learning_rate': 0.0004905097816990331, 'epoch': 0.1}
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{'loss': 1.5233, 'grad_norm': 1.3293033838272095, 'learning_rate': 0.0004902847711322535, 'epoch': 0.1}
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{'loss': 1.4875, 'grad_norm': 1.0673298835754395, 'learning_rate': 0.0004900571770924999, 'epoch': 0.1}
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{'loss': 1.5143, 'grad_norm': 1.2059558629989624, 'learning_rate': 0.000489827002026798, 'epoch': 0.1}
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{'loss': 1.3904, 'grad_norm': 1.5264915227890015, 'learning_rate': 0.0004895942484099241, 'epoch': 0.1}
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{'loss': 1.5727, 'grad_norm': 1.055259108543396, 'learning_rate': 0.0004893589187443786, 'epoch': 0.1}
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{'loss': 1.3997, 'grad_norm': 1.3388723134994507, 'learning_rate': 0.0004891210155603585, 'epoch': 0.1}
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{'loss': 1.5489, 'grad_norm': 1.146324872970581, 'learning_rate': 0.0004888805414157304, 'epoch': 0.1}
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{'loss': 1.5821, 'grad_norm': 1.446535348892212, 'learning_rate': 0.0004886374988960036, 'epoch': 0.11}
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{'loss': 1.4677, 'grad_norm': 1.3649553060531616, 'learning_rate': 0.0004883918906143016, 'epoch': 0.11}
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{'loss': 1.4691, 'grad_norm': 1.2442352771759033, 'learning_rate': 0.00048814371921133417, 'epoch': 0.11}
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{'loss': 1.6451, 'grad_norm': 1.2473433017730713, 'learning_rate': 0.00048789298735536904, 'epoch': 0.11}
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{'loss': 1.6799, 'grad_norm': 1.7001152038574219, 'learning_rate': 0.0004876396977422033, 'epoch': 0.11}
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{'loss': 1.4816, 'grad_norm': 1.1802128553390503, 'learning_rate': 0.00048738385309513434, 'epoch': 0.11}
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{'loss': 1.681, 'grad_norm': 1.1580235958099365, 'learning_rate': 0.0004871254561649303, 'epoch': 0.11}
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{'loss': 1.507, 'grad_norm': 1.2179391384124756, 'learning_rate': 0.000486864509729801, 'epoch': 0.11}
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{'loss': 1.276, 'grad_norm': 1.1689331531524658, 'learning_rate': 0.00048660101659536763, 'epoch': 0.11}
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{'loss': 1.5065, 'grad_norm': 1.1789075136184692, 'learning_rate': 0.0004863349795946329, 'epoch': 0.11}
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{'loss': 1.286, 'grad_norm': 0.98433917760849, 'learning_rate': 0.00048606640158795034, 'epoch': 0.12}
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{'loss': 1.4637, 'grad_norm': 0.9510318636894226, 'learning_rate': 0.0004857952854629938, 'epoch': 0.12}
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{'loss': 1.4925, 'grad_norm': 1.0436633825302124, 'learning_rate': 0.0004855216341347259, 'epoch': 0.12}
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{'loss': 1.3586, 'grad_norm': 1.4930212497711182, 'learning_rate': 0.0004852454505453674, 'epoch': 0.12}
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{'loss': 1.5212, 'grad_norm': 0.8224968910217285, 'learning_rate': 0.00048496673766436517, 'epoch': 0.12}
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{'loss': 1.4901, 'grad_norm': 1.2815650701522827, 'learning_rate': 0.00048468549848835996, 'epoch': 0.12}
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{'loss': 1.5946, 'grad_norm': 1.2067404985427856, 'learning_rate': 0.000484401736041155, 'epoch': 0.12}
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{'loss': 1.5052, 'grad_norm': 1.530190348625183, 'learning_rate': 0.0004841154533736827, 'epoch': 0.12}
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{'loss': 1.4186, 'grad_norm': 1.2579395771026611, 'learning_rate': 0.0004838266535639722, 'epoch': 0.12}
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{'loss': 1.3459, 'grad_norm': 1.5573033094406128, 'learning_rate': 0.00048353533971711625, 'epoch': 0.13}
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{'loss': 1.4907, 'grad_norm': 1.2861477136611938, 'learning_rate': 0.0004832415149652378, 'epoch': 0.13}
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{'loss': 1.3579, 'grad_norm': 0.8996832370758057, 'learning_rate': 0.00048294518246745643, 'epoch': 0.13}
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{'loss': 1.3771, 'grad_norm': 1.4587196111679077, 'learning_rate': 0.0004826463454098542, 'epoch': 0.13}
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{'loss': 1.3366, 'grad_norm': 1.1128901243209839, 'learning_rate': 0.00048234500700544144, 'epoch': 0.13}
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{'loss': 1.3485, 'grad_norm': 1.0845853090286255, 'learning_rate': 0.0004820411704941223, 'epoch': 0.13}
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{'loss': 1.4484, 'grad_norm': 1.1594990491867065, 'learning_rate': 0.00048173483914265976, 'epoch': 0.13}
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{'loss': 1.4147, 'grad_norm': 1.2621511220932007, 'learning_rate': 0.0004814260162446408, 'epoch': 0.13}
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{'loss': 1.3724, 'grad_norm': 1.6021978855133057, 'learning_rate': 0.00048111470512044065, 'epoch': 0.13}
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{'loss': 1.2895, 'grad_norm': 1.341562271118164, 'learning_rate': 0.0004804846316087254, 'epoch': 0.14}
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{'loss': 1.3963, 'grad_norm': 1.1911447048187256, 'learning_rate': 0.00048016587599557996, 'epoch': 0.14}
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{'loss': 1.3621, 'grad_norm': 1.0620297193527222, 'learning_rate': 0.0004798446457049201, 'epoch': 0.14}
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{'loss': 1.3658, 'grad_norm': 1.1608030796051025, 'learning_rate': 0.0004795209441905217, 'epoch': 0.14}
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{'loss': 1.3683, 'grad_norm': 1.5855592489242554, 'learning_rate': 0.0004791947749327308, 'epoch': 0.14}
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{'loss': 1.3186, 'grad_norm': 1.5676512718200684, 'learning_rate': 0.0004788661414384255, 'epoch': 0.14}
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{'loss': 1.3231, 'grad_norm': 1.149056315422058, 'learning_rate': 0.0004785350472409792, 'epoch': 0.14}
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{'loss': 1.2976, 'grad_norm': 0.9153057336807251, 'learning_rate': 0.00047820149590022153, 'epoch': 0.14}
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{'loss': 1.4205, 'grad_norm': 1.2601430416107178, 'learning_rate': 0.00047786549100240107, 'epoch': 0.14}
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{'loss': 1.3192, 'grad_norm': 1.2112687826156616, 'learning_rate': 0.0004775270361601461, 'epoch': 0.14}
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{'loss': 1.355, 'grad_norm': 1.1192986965179443, 'learning_rate': 0.00047718613501242604, 'epoch': 0.15}
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{'loss': 1.3638, 'grad_norm': 1.2099722623825073, 'learning_rate': 0.00047684279122451223, 'epoch': 0.15}
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{'loss': 1.331, 'grad_norm': 1.124934434890747, 'learning_rate': 0.00047649700848793886, 'epoch': 0.15}
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{'loss': 1.5308, 'grad_norm': 1.2407630681991577, 'learning_rate': 0.00047614879052046254, 'epoch': 0.15}
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{'loss': 1.3365, 'grad_norm': 1.2991951704025269, 'learning_rate': 0.00047579814106602316, 'epoch': 0.15}
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{'loss': 1.3209, 'grad_norm': 1.3195995092391968, 'learning_rate': 0.0004754450638947032, 'epoch': 0.15}
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{'loss': 1.4705, 'grad_norm': 1.4838430881500244, 'learning_rate': 0.000475089562802687, 'epoch': 0.15}
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{'loss': 1.3316, 'grad_norm': 1.126265287399292, 'learning_rate': 0.0004747316416122205, 'epoch': 0.15}
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{'loss': 1.4045, 'grad_norm': 1.0109881162643433, 'learning_rate': 0.00047437130417156973, 'epoch': 0.15}
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{'loss': 1.3058, 'grad_norm': 0.8355922698974609, 'learning_rate': 0.00047400855435497945, 'epoch': 0.15}
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{'loss': 1.2732, 'grad_norm': 1.406782865524292, 'learning_rate': 0.00047364339606263185, 'epoch': 0.16}
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{'loss': 1.3472, 'grad_norm': 0.9180629849433899, 'learning_rate': 0.00047327583322060406, 'epoch': 0.16}
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{'loss': 1.4165, 'grad_norm': 1.1642565727233887, 'learning_rate': 0.00047290586978082654, 'epoch': 0.16}
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{'loss': 1.5038, 'grad_norm': 1.6167621612548828, 'learning_rate': 0.00047253350972104003, 'epoch': 0.16}
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{'loss': 1.3744, 'grad_norm': 1.1963645219802856, 'learning_rate': 0.00047215875704475314, 'epoch': 0.16}
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{'loss': 1.3314, 'grad_norm': 1.246496319770813, 'learning_rate': 0.00047178161578119925, 'epoch': 0.16}
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{'loss': 1.4149, 'grad_norm': 1.1050803661346436, 'learning_rate': 0.00047140208998529303, 'epoch': 0.16}
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{'loss': 1.3817, 'grad_norm': 1.379414439201355, 'learning_rate': 0.00047102018373758716, 'epoch': 0.16}
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{'loss': 1.2865, 'grad_norm': 1.0111711025238037, 'learning_rate': 0.00047063590114422804, 'epoch': 0.16}
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{'loss': 1.317, 'grad_norm': 1.5682810544967651, 'learning_rate': 0.00047024924633691197, 'epoch': 0.17}
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{'loss': 1.3659, 'grad_norm': 0.9005609154701233, 'learning_rate': 0.0004698602234728406, 'epoch': 0.17}
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{'loss': 1.3373, 'grad_norm': 1.00556218624115, 'learning_rate': 0.00046946883673467624, 'epoch': 0.17}
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{'loss': 1.4858, 'grad_norm': 1.387304425239563, 'learning_rate': 0.00046907509033049683, 'epoch': 0.17}
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{'loss': 1.4221, 'grad_norm': 1.2545139789581299, 'learning_rate': 0.0004686789884937509, 'epoch': 0.17}
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{'loss': 1.3609, 'grad_norm': 1.3092625141143799, 'learning_rate': 0.00046828053548321187, 'epoch': 0.17}
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{'loss': 1.3406, 'grad_norm': 1.0507373809814453, 'learning_rate': 0.0004678797355829322, 'epoch': 0.17}
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{'loss': 1.3098, 'grad_norm': 1.0271055698394775, 'learning_rate': 0.0004674765931021976, 'epoch': 0.17}
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{'loss': 1.2113, 'grad_norm': 1.1777933835983276, 'learning_rate': 0.0004670711123754804, 'epoch': 0.17}
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{'loss': 1.3137, 'grad_norm': 1.1888736486434937, 'learning_rate': 0.00046666329776239306, 'epoch': 0.17}
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{'loss': 1.23, 'grad_norm': 1.4923595190048218, 'learning_rate': 0.00046625315364764156, 'epoch': 0.18}
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{'loss': 1.3182, 'grad_norm': 1.3654290437698364, 'learning_rate': 0.00046584068444097766, 'epoch': 0.18}
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{'loss': 1.2854, 'grad_norm': 1.5486831665039062, 'learning_rate': 0.0004654258945771521, 'epoch': 0.18}
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{'loss': 1.3328, 'grad_norm': 1.3204468488693237, 'learning_rate': 0.0004650087885158667, 'epoch': 0.18}
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{'loss': 1.3741, 'grad_norm': 1.4773411750793457, 'learning_rate': 0.000464589370741726, 'epoch': 0.18}
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{'loss': 1.26, 'grad_norm': 1.20607328414917, 'learning_rate': 0.00046416764576418993, 'epoch': 0.18}
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{'loss': 1.2398, 'grad_norm': 1.2402129173278809, 'learning_rate': 0.00046374361811752436, 'epoch': 0.18}
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{'loss': 1.4529, 'grad_norm': 1.1439170837402344, 'learning_rate': 0.00046331729236075305, 'epoch': 0.18}
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{'loss': 1.4535, 'grad_norm': 1.268829107284546, 'learning_rate': 0.0004628886730776084, 'epoch': 0.18}
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{'loss': 1.3249, 'grad_norm': 1.37249755859375, 'learning_rate': 0.0004624577648764819, 'epoch': 0.18}
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{'loss': 1.2866, 'grad_norm': 1.2565507888793945, 'learning_rate': 0.0004620245723903751, 'epoch': 0.19}
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{'loss': 1.1712, 'grad_norm': 1.1188476085662842, 'learning_rate': 0.0004615891002768494, 'epoch': 0.19}
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{'loss': 1.1788, 'grad_norm': 1.3613345623016357, 'learning_rate': 0.00046115135321797617, 'epoch': 0.19}
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{'loss': 1.2391, 'grad_norm': 1.3465733528137207, 'learning_rate': 0.00046071133592028626, 'epoch': 0.19}
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{'loss': 1.2213, 'grad_norm': 1.2718288898468018, 'learning_rate': 0.00046026905311471946, 'epoch': 0.19}
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{'loss': 1.2036, 'grad_norm': 1.372878909111023, 'learning_rate': 0.0004598245095565738, 'epoch': 0.19}
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{'loss': 1.192, 'grad_norm': 1.198723316192627, 'learning_rate': 0.00045937771002545403, 'epoch': 0.19}
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{'loss': 1.2999, 'grad_norm': 0.9199055433273315, 'learning_rate': 0.00045892865932522077, 'epoch': 0.19}
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{'loss': 1.3587, 'grad_norm': 1.3330020904541016, 'learning_rate': 0.0004584773622839383, 'epoch': 0.19}
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{'loss': 1.4654, 'grad_norm': 1.228247046470642, 'learning_rate': 0.0004580238237538232, 'epoch': 0.2}
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{'loss': 1.1583, 'grad_norm': 1.6616053581237793, 'learning_rate': 0.0004575680486111915, 'epoch': 0.2}
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{'loss': 1.2121, 'grad_norm': 1.0149476528167725, 'learning_rate': 0.000457110041756407, 'epoch': 0.2}
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{'loss': 1.2938, 'grad_norm': 1.6001296043395996, 'learning_rate': 0.00045664980811382813, 'epoch': 0.2}
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{'loss': 1.1885, 'grad_norm': 1.263951301574707, 'learning_rate': 0.00045618735263175495, 'epoch': 0.2}
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{'loss': 1.2981, 'grad_norm': 1.4672034978866577, 'learning_rate': 0.00045572268028237637, 'epoch': 0.2}
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{'loss': 1.117, 'grad_norm': 1.1751412153244019, 'learning_rate': 0.0004552557960617161, 'epoch': 0.2}
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{'loss': 1.1909, 'grad_norm': 1.131339192390442, 'learning_rate': 0.0004547867049895796, 'epoch': 0.2}
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{'loss': 1.1328, 'grad_norm': 0.991729736328125, 'learning_rate': 0.0004543154121094996, 'epoch': 0.2}
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{'loss': 0.9973, 'grad_norm': 0.9073534607887268, 'learning_rate': 0.0004538419224886819, 'epoch': 0.2}
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{'loss': 1.2367, 'grad_norm': 1.2480231523513794, 'learning_rate': 0.00045336624121795124, 'epoch': 0.21}
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{'loss': 1.1738, 'grad_norm': 1.28248131275177, 'learning_rate': 0.0004528883734116963, 'epoch': 0.21}
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{'loss': 1.3788, 'grad_norm': 1.386885166168213, 'learning_rate': 0.00045240832420781474, 'epoch': 0.21}
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{'loss': 1.3046, 'grad_norm': 1.1158126592636108, 'learning_rate': 0.0004519260987676579, 'epoch': 0.21}
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{'loss': 1.251, 'grad_norm': 1.4696872234344482, 'learning_rate': 0.0004326031337062333, 'epoch': 0.25}
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{'loss': 1.2215, 'grad_norm': 1.3883171081542969, 'learning_rate': 0.00043204226176660107, 'epoch': 0.25}
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{'loss': 1.1014, 'grad_norm': 1.1045054197311401, 'learning_rate': 0.0004314794325607584, 'epoch': 0.25}
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{'loss': 1.0845, 'grad_norm': 1.273431658744812, 'learning_rate': 0.00043091465214008354, 'epoch': 0.25}
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{'loss': 1.1162, 'grad_norm': 1.0618531703948975, 'learning_rate': 0.0004303479265769337, 'epoch': 0.25}
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{'loss': 1.0385, 'grad_norm': 1.3230130672454834, 'learning_rate': 0.0004297792619645797, 'epoch': 0.25}
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{'loss': 1.1548, 'grad_norm': 1.3020095825195312, 'learning_rate': 0.0004292086644171403, 'epoch': 0.25}
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{'loss': 1.3534, 'grad_norm': 1.3359605073928833, 'learning_rate': 0.0004286361400695169, 'epoch': 0.25}
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{'loss': 1.2432, 'grad_norm': 0.9598666429519653, 'learning_rate': 0.00042806169507732706, 'epoch': 0.26}
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{'loss': 1.2512, 'grad_norm': 0.9668743014335632, 'learning_rate': 0.00042748533561683865, 'epoch': 0.26}
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{'loss': 1.136, 'grad_norm': 1.0706520080566406, 'learning_rate': 0.0004269070678849034, 'epoch': 0.26}
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{'loss': 1.1211, 'grad_norm': 1.0470647811889648, 'learning_rate': 0.00042632689809889027, 'epoch': 0.26}
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{'loss': 1.0704, 'grad_norm': 1.1314566135406494, 'learning_rate': 0.0004257448324966183, 'epoch': 0.26}
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{'loss': 1.1727, 'grad_norm': 1.085749626159668, 'learning_rate': 0.00042516087733629004, 'epoch': 0.26}
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{'loss': 1.2864, 'grad_norm': 1.2759054899215698, 'learning_rate': 0.00042457503889642396, 'epoch': 0.26}
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{'loss': 1.0734, 'grad_norm': 1.2468546628952026, 'learning_rate': 0.0004239873234757871, 'epoch': 0.26}
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{'loss': 1.1816, 'grad_norm': 1.2701605558395386, 'learning_rate': 0.00042339773739332706, 'epoch': 0.26}
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{'loss': 1.0726, 'grad_norm': 1.0027897357940674, 'learning_rate': 0.00042280628698810447, 'epoch': 0.26}
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{'loss': 1.278, 'grad_norm': 1.4462809562683105, 'learning_rate': 0.0004222129786192245, 'epoch': 0.27}
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{'loss': 1.1479, 'grad_norm': 1.6216635704040527, 'learning_rate': 0.00042102081352672594, 'epoch': 0.27}
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{'loss': 1.1709, 'grad_norm': 4.33831787109375, 'learning_rate': 0.0004204219696209248, 'epoch': 0.27}
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{'loss': 1.081, 'grad_norm': 1.4484752416610718, 'learning_rate': 0.0004192187912831393, 'epoch': 0.27}
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{'loss': 1.134, 'grad_norm': 1.5165774822235107, 'learning_rate': 0.0004180083353971871, 'epoch': 0.27}
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{'loss': 1.2379, 'grad_norm': 1.2738356590270996, 'learning_rate': 0.0004161791201268247, 'epoch': 0.28}
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{'loss': 1.2347, 'grad_norm': 1.127266764640808, 'learning_rate': 0.000415565799522563, 'epoch': 0.28}
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{'loss': 1.2155, 'grad_norm': 1.4078705310821533, 'learning_rate': 0.0004149506988022894, 'epoch': 0.28}
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{'loss': 1.1583, 'grad_norm': 0.9871230125427246, 'learning_rate': 0.0004143338245793906, 'epoch': 0.28}
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{'loss': 1.171, 'grad_norm': 0.8731614947319031, 'learning_rate': 0.0004137151834863213, 'epoch': 0.28}
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{'loss': 1.2112, 'grad_norm': 1.0814101696014404, 'learning_rate': 0.0004130947821745332, 'epoch': 0.28}
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{'loss': 1.0269, 'grad_norm': 1.695854902267456, 'learning_rate': 0.0004124726273144034, 'epoch': 0.28}
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{'loss': 1.1974, 'grad_norm': 0.9004189372062683, 'learning_rate': 0.00041184872559516244, 'epoch': 0.28}
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{'loss': 1.1068, 'grad_norm': 1.034590482711792, 'learning_rate': 0.0004112230837248229, 'epoch': 0.28}
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{'loss': 1.0922, 'grad_norm': 1.4124444723129272, 'learning_rate': 0.0004105957084301069, 'epoch': 0.29}
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{'loss': 1.2392, 'grad_norm': 1.4991061687469482, 'learning_rate': 0.00040996660645637345, 'epoch': 0.29}
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{'loss': 1.0433, 'grad_norm': 1.316277027130127, 'learning_rate': 0.00040933578456754687, 'epoch': 0.29}
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{'loss': 0.9623, 'grad_norm': 1.6624524593353271, 'learning_rate': 0.00040870324954604323, 'epoch': 0.29}
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{'loss': 1.108, 'grad_norm': 1.2658953666687012, 'learning_rate': 0.00040806900819269766, 'epoch': 0.29}
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{'loss': 1.137, 'grad_norm': 1.3295769691467285, 'learning_rate': 0.00040743306732669133, 'epoch': 0.29}
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{'loss': 1.1281, 'grad_norm': 1.6526551246643066, 'learning_rate': 0.0004067954337854783, 'epoch': 0.29}
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{'loss': 1.0086, 'grad_norm': 1.1587738990783691, 'learning_rate': 0.0004061561144247115, 'epoch': 0.29}
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{'loss': 1.0723, 'grad_norm': 1.454256534576416, 'learning_rate': 0.0004055151161181694, 'epoch': 0.29}
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{'loss': 1.2486, 'grad_norm': 1.476521611213684, 'learning_rate': 0.00040487244575768193, 'epoch': 0.29}
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{'loss': 1.1071, 'grad_norm': 0.9193058609962463, 'learning_rate': 0.00040422811025305676, 'epoch': 0.3}
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{'loss': 1.1724, 'grad_norm': 1.8984912633895874, 'learning_rate': 0.0004035821165320045, 'epoch': 0.3}
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{'loss': 1.174, 'grad_norm': 1.7232333421707153, 'learning_rate': 0.00040293447154006435, 'epoch': 0.3}
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{'loss': 1.2448, 'grad_norm': 0.9074342846870422, 'learning_rate': 0.0004022851822405297, 'epoch': 0.3}
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{'loss': 1.1207, 'grad_norm': 1.2896414995193481, 'learning_rate': 0.0004016342556143727, 'epoch': 0.3}
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{'loss': 1.0868, 'grad_norm': 1.264952301979065, 'learning_rate': 0.0004009816986601702, 'epoch': 0.3}
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{'loss': 1.1881, 'grad_norm': 1.1345736980438232, 'learning_rate': 0.00040032751839402727, 'epoch': 0.3}
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{'loss': 1.1796, 'grad_norm': 1.0581121444702148, 'learning_rate': 0.00039967172184950263, 'epoch': 0.3}
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{'loss': 1.108, 'grad_norm': 1.1877319812774658, 'learning_rate': 0.0003990143160775327, 'epoch': 0.3}
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{'loss': 0.9986, 'grad_norm': 1.0478935241699219, 'learning_rate': 0.0003983553081463559, 'epoch': 0.3}
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{'loss': 1.0221, 'grad_norm': 1.1299808025360107, 'learning_rate': 0.0003976947051414366, 'epoch': 0.31}
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{'loss': 0.9024, 'grad_norm': 1.0618709325790405, 'learning_rate': 0.00039703251416538883, 'epoch': 0.31}
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{'loss': 1.2754, 'grad_norm': 1.099435806274414, 'learning_rate': 0.0003963687423379003, 'epoch': 0.31}
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{'loss': 1.1154, 'grad_norm': 1.1851657629013062, 'learning_rate': 0.00039570339679565527, 'epoch': 0.31}
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{'loss': 0.9979, 'grad_norm': 1.1812468767166138, 'learning_rate': 0.00039503648469225826, 'epoch': 0.31}
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{'loss': 1.0625, 'grad_norm': 1.1941215991973877, 'learning_rate': 0.00039436801319815717, 'epoch': 0.31}
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{'loss': 1.189, 'grad_norm': 0.9969115853309631, 'learning_rate': 0.00039369798950056567, 'epoch': 0.31}
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{'loss': 1.107, 'grad_norm': 1.1364282369613647, 'learning_rate': 0.00039302642080338664, 'epoch': 0.31}
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{'loss': 1.238, 'grad_norm': 1.234078288078308, 'learning_rate': 0.0003923533143271341, 'epoch': 0.31}
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{'loss': 1.1151, 'grad_norm': 1.0862330198287964, 'learning_rate': 0.0003916786773088559, 'epoch': 0.32}
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{'loss': 1.1031, 'grad_norm': 0.7923426032066345, 'learning_rate': 0.0003910025170020558, 'epoch': 0.32}
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{'loss': 1.0546, 'grad_norm': 1.6818230152130127, 'learning_rate': 0.0003903248406766158, 'epoch': 0.32}
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{'loss': 1.1412, 'grad_norm': 1.0749332904815674, 'learning_rate': 0.00038964565561871723, 'epoch': 0.32}
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{'loss': 1.0668, 'grad_norm': 1.6896083354949951, 'learning_rate': 0.00038896496913076327, 'epoch': 0.32}
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{'loss': 1.2632, 'grad_norm': 1.24112069606781, 'learning_rate': 0.0003882827885312998, 'epoch': 0.32}
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{'loss': 1.0969, 'grad_norm': 1.2017937898635864, 'learning_rate': 0.00038759912115493724, 'epoch': 0.32}
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{'loss': 0.9202, 'grad_norm': 1.20210599899292, 'learning_rate': 0.00038691397435227097, 'epoch': 0.32}
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{'loss': 1.1583, 'grad_norm': 1.408257246017456, 'learning_rate': 0.0003862273554898032, 'epoch': 0.32}
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{'loss': 1.0382, 'grad_norm': 1.5274341106414795, 'learning_rate': 0.000385539271949863, 'epoch': 0.32}
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{'loss': 1.1232, 'grad_norm': 1.1086093187332153, 'learning_rate': 0.00038484973113052736, 'epoch': 0.33}
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{'loss': 1.1019, 'grad_norm': 1.35590660572052, 'learning_rate': 0.0003841587404455413, 'epoch': 0.33}
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{'loss': 1.1557, 'grad_norm': 1.0598667860031128, 'learning_rate': 0.000383466307324239, 'epoch': 0.33}
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{'loss': 1.0634, 'grad_norm': 1.1116646528244019, 'learning_rate': 0.00038277243921146253, 'epoch': 0.33}
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{'loss': 1.0067, 'grad_norm': 1.4738123416900635, 'learning_rate': 0.0003820771435674829, 'epoch': 0.33}
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{'loss': 1.0798, 'grad_norm': 1.3569648265838623, 'learning_rate': 0.00038138042786791987, 'epoch': 0.33}
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{'loss': 1.0349, 'grad_norm': 1.073232650756836, 'learning_rate': 0.00038068229960366054, 'epoch': 0.33}
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{'loss': 1.1281, 'grad_norm': 1.4980833530426025, 'learning_rate': 0.0003799827662807801, 'epoch': 0.33}
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{'loss': 1.1056, 'grad_norm': 1.4308544397354126, 'learning_rate': 0.0003792818354204601, 'epoch': 0.33}
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{'loss': 1.0928, 'grad_norm': 1.5736769437789917, 'learning_rate': 0.0003785795145589085, 'epoch': 0.33}
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{'loss': 1.2857, 'grad_norm': 1.5231635570526123, 'learning_rate': 0.0003778758112472776, 'epoch': 0.34}
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{'loss': 1.1738, 'grad_norm': 1.0374634265899658, 'learning_rate': 0.00037717073305158376, 'epoch': 0.34}
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{'loss': 0.9849, 'grad_norm': 1.1238751411437988, 'learning_rate': 0.0003764642875526256, 'epoch': 0.34}
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{'loss': 1.059, 'grad_norm': 1.039632797241211, 'learning_rate': 0.0003757564823459025, 'epoch': 0.34}
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{'loss': 0.9745, 'grad_norm': 1.1179864406585693, 'learning_rate': 0.0003750473250415334, 'epoch': 0.34}
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{'loss': 1.1077, 'grad_norm': 1.1467711925506592, 'learning_rate': 0.0003743368232641741, 'epoch': 0.34}
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{'loss': 1.0454, 'grad_norm': 1.0984638929367065, 'learning_rate': 0.00037362498465293604, 'epoch': 0.34}
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{'loss': 1.073, 'grad_norm': 1.0687668323516846, 'learning_rate': 0.000372911816861304, 'epoch': 0.34}
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{'loss': 1.1094, 'grad_norm': 1.6367201805114746, 'learning_rate': 0.00037219732755705353, 'epoch': 0.34}
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{'loss': 1.1536, 'grad_norm': 1.2905350923538208, 'learning_rate': 0.0003714815244221689, 'epoch': 0.35}
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{'loss': 1.1906, 'grad_norm': 0.9039587378501892, 'learning_rate': 0.00037076441515276003, 'epoch': 0.35}
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{'loss': 1.1448, 'grad_norm': 1.3063287734985352, 'learning_rate': 0.0003700460074589804, 'epoch': 0.35}
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{'loss': 1.2537, 'grad_norm': 1.5665302276611328, 'learning_rate': 0.00036932630906494346, 'epoch': 0.35}
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{'loss': 1.1135, 'grad_norm': 1.0483524799346924, 'learning_rate': 0.0003686053277086401, 'epoch': 0.35}
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{'loss': 1.0816, 'grad_norm': 1.2478828430175781, 'learning_rate': 0.0003678830711418551, 'epoch': 0.35}
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{'loss': 1.1217, 'grad_norm': 1.14175283908844, 'learning_rate': 0.00036715954713008406, 'epoch': 0.35}
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{'loss': 1.1127, 'grad_norm': 1.0000718832015991, 'learning_rate': 0.0003664347634524497, 'epoch': 0.35}
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{'loss': 1.0619, 'grad_norm': 1.5337084531784058, 'learning_rate': 0.00036570872790161834, 'epoch': 0.35}
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{'loss': 1.1094, 'grad_norm': 0.878587543964386, 'learning_rate': 0.00036498144828371604, 'epoch': 0.35}
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{'loss': 0.9874, 'grad_norm': 1.3913145065307617, 'learning_rate': 0.0003642529324182449, 'epoch': 0.36}
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{'loss': 1.0177, 'grad_norm': 1.388934850692749, 'learning_rate': 0.0003635231881379985, 'epoch': 0.36}
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{'loss': 1.0533, 'grad_norm': 1.2259199619293213, 'learning_rate': 0.00036279222328897826, 'epoch': 0.36}
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{'loss': 1.1231, 'grad_norm': 1.561023235321045, 'learning_rate': 0.0003620600457303085, 'epoch': 0.36}
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{'loss': 1.0971, 'grad_norm': 1.6612300872802734, 'learning_rate': 0.0003613266633341528, 'epoch': 0.36}
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{'loss': 1.024, 'grad_norm': 1.3272353410720825, 'learning_rate': 0.00036059208398562793, 'epoch': 0.36}
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{'loss': 1.0369, 'grad_norm': 1.2441381216049194, 'learning_rate': 0.00035985631558272074, 'epoch': 0.36}
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{'loss': 0.9626, 'grad_norm': 1.1805377006530762, 'learning_rate': 0.00035911936603620225, 'epoch': 0.36}
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{'loss': 1.1415, 'grad_norm': 1.3905142545700073, 'learning_rate': 0.00035838124326954254, 'epoch': 0.36}
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{'loss': 1.0101, 'grad_norm': 1.2646347284317017, 'learning_rate': 0.0003576419552188261, 'epoch': 0.36}
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{'loss': 0.9862, 'grad_norm': 1.1667832136154175, 'learning_rate': 0.000356901509832666, 'epoch': 0.37}
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{'loss': 0.9644, 'grad_norm': 0.8571506142616272, 'learning_rate': 0.00035615991507211887, 'epoch': 0.37}
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{'loss': 0.8918, 'grad_norm': 0.9799628853797913, 'learning_rate': 0.00035541717891059886, 'epoch': 0.37}
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{'loss': 0.935, 'grad_norm': 1.0068583488464355, 'learning_rate': 0.0003546733093337924, 'epoch': 0.37}
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{'loss': 1.0752, 'grad_norm': 0.8947041034698486, 'learning_rate': 0.0003539283143395719, 'epoch': 0.37}
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{'loss': 0.9534, 'grad_norm': 1.0797079801559448, 'learning_rate': 0.00035318220193790976, 'epoch': 0.37}
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{'loss': 0.9862, 'grad_norm': 1.0746815204620361, 'learning_rate': 0.0003524349801507929, 'epoch': 0.37}
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{'loss': 1.0606, 'grad_norm': 1.0419459342956543, 'learning_rate': 0.0003516866570121356, 'epoch': 0.37}
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{'loss': 0.97, 'grad_norm': 1.1848483085632324, 'learning_rate': 0.0003509372405676937, 'epoch': 0.37}
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{'loss': 1.0748, 'grad_norm': 1.6243517398834229, 'learning_rate': 0.0003494351600031677, 'epoch': 0.38}
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{'loss': 1.0639, 'grad_norm': 1.4265719652175903, 'learning_rate': 0.00034868251203302314, 'epoch': 0.38}
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{'loss': 0.9635, 'grad_norm': 1.0866336822509766, 'learning_rate': 0.00034792880305679967, 'epoch': 0.38}
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{'loss': 1.0258, 'grad_norm': 0.9332922697067261, 'learning_rate': 0.0003464182345120877, 'epoch': 0.38}
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{'loss': 1.0168, 'grad_norm': 0.9739323854446411, 'learning_rate': 0.000345661391184799, 'epoch': 0.38}
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{'loss': 1.0996, 'grad_norm': 0.912225067615509, 'learning_rate': 0.00034490351933365673, 'epoch': 0.38}
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{'loss': 0.9759, 'grad_norm': 0.853449285030365, 'learning_rate': 0.0003441446271070816, 'epoch': 0.38}
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{'loss': 1.1549, 'grad_norm': 0.9426379799842834, 'learning_rate': 0.0003426238141760826, 'epoch': 0.39}
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{'loss': 1.0742, 'grad_norm': 1.3207985162734985, 'learning_rate': 0.00034186190982300323, 'epoch': 0.39}
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{'loss': 1.0011, 'grad_norm': 0.9599164128303528, 'learning_rate': 0.00034109901779700474, 'epoch': 0.39}
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{'loss': 0.9508, 'grad_norm': 1.0271940231323242, 'learning_rate': 0.0003403351463004831, 'epoch': 0.39}
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{'loss': 1.134, 'grad_norm': 1.680550456047058, 'learning_rate': 0.0003395703035463659, 'epoch': 0.39}
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{'loss': 1.1811, 'grad_norm': 1.4388446807861328, 'learning_rate': 0.00033880449775802306, 'epoch': 0.39}
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{'loss': 0.8677, 'grad_norm': 1.1560338735580444, 'learning_rate': 0.00033803773716917896, 'epoch': 0.39}
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{'loss': 1.0965, 'grad_norm': 1.414915680885315, 'learning_rate': 0.00033727003002382346, 'epoch': 0.39}
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{'loss': 1.0057, 'grad_norm': 1.1397249698638916, 'learning_rate': 0.0003365013845761239, 'epoch': 0.39}
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{'loss': 1.1104, 'grad_norm': 1.470551609992981, 'learning_rate': 0.00033573180909033567, 'epoch': 0.39}
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{'loss': 1.0244, 'grad_norm': 0.9758129119873047, 'learning_rate': 0.0003349613118407138, 'epoch': 0.4}
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{'loss': 1.0993, 'grad_norm': 0.9483941793441772, 'learning_rate': 0.0003341899011114238, 'epoch': 0.4}
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{'loss': 0.9506, 'grad_norm': 1.0213124752044678, 'learning_rate': 0.00033341758519645273, 'epoch': 0.4}
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{'loss': 0.993, 'grad_norm': 1.0135273933410645, 'learning_rate': 0.0003326443723995199, 'epoch': 0.4}
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{'loss': 0.9969, 'grad_norm': 0.9621232748031616, 'learning_rate': 0.0003318702710339875, 'epoch': 0.4}
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{'loss': 1.0203, 'grad_norm': 1.1387019157409668, 'learning_rate': 0.0003310952894227718, 'epoch': 0.4}
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{'loss': 0.9874, 'grad_norm': 1.172455072402954, 'learning_rate': 0.00032798672730875603, 'epoch': 0.41}
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{'loss': 1.074, 'grad_norm': 1.0053542852401733, 'learning_rate': 0.00032564647235493833, 'epoch': 0.41}
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{'loss': 1.0043, 'grad_norm': 1.0846545696258545, 'learning_rate': 0.0003248647495200886, 'epoch': 0.41}
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{'loss': 1.0302, 'grad_norm': 1.0012749433517456, 'learning_rate': 0.00032408222176091427, 'epoch': 0.41}
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{'loss': 0.9389, 'grad_norm': 1.074317455291748, 'learning_rate': 0.0003232988974909296, 'epoch': 0.41}
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{'loss': 0.9604, 'grad_norm': 1.0520236492156982, 'learning_rate': 0.000254713257368626, 'epoch': 0.5}
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{'loss': 1.0497, 'grad_norm': 1.0495281219482422, 'learning_rate': 0.00024159919043155556, 'epoch': 0.52}
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{'loss': 0.8901, 'grad_norm': 0.7148188352584839, 'learning_rate': 0.00023914178887159327, 'epoch': 0.52}
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{'loss': 1.0241, 'grad_norm': 1.2880741357803345, 'learning_rate': 0.0002383228767653474, 'epoch': 0.52}
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{'loss': 0.8021, 'grad_norm': 1.2413357496261597, 'learning_rate': 0.00022605918789984707, 'epoch': 0.54}
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{'loss': 0.7335, 'grad_norm': 1.096956729888916, 'learning_rate': 0.00022524334000081664, 'epoch': 0.54}
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{'loss': 0.9085, 'grad_norm': 1.4210362434387207, 'learning_rate': 0.00021223192885603571, 'epoch': 0.55}
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{'loss': 0.8978, 'grad_norm': 0.9129871726036072, 'learning_rate': 0.00021142179629394283, 'epoch': 0.55}
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{'loss': 0.9259, 'grad_norm': 1.0471965074539185, 'learning_rate': 0.00020818550089911234, 'epoch': 0.56}
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{'loss': 0.9819, 'grad_norm': 0.7237703800201416, 'learning_rate': 0.0002001274471797252, 'epoch': 0.57}
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{'loss': 0.7477, 'grad_norm': 1.2666767835617065, 'learning_rate': 0.0001993244482597829, 'epoch': 0.57}
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{'loss': 0.7239, 'grad_norm': 1.1628350019454956, 'learning_rate': 0.00019852199418884527, 'epoch': 0.57}
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{'loss': 0.8326, 'grad_norm': 1.1754180192947388, 'learning_rate': 0.00019772009359466828, 'epoch': 0.57}
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{'loss': 0.779, 'grad_norm': 0.9641863703727722, 'learning_rate': 0.00019691875509905718, 'epoch': 0.57}
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{'loss': 0.8895, 'grad_norm': 1.4042216539382935, 'learning_rate': 0.00019611798731777397, 'epoch': 0.57}
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{'loss': 0.8636, 'grad_norm': 0.8444200158119202, 'learning_rate': 0.00019531779886044424, 'epoch': 0.57}
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{'loss': 0.7449, 'grad_norm': 0.6162629127502441, 'learning_rate': 0.00019132584732880383, 'epoch': 0.58}
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{'loss': 0.8205, 'grad_norm': 0.8406280279159546, 'learning_rate': 0.00018893817792101935, 'epoch': 0.58}
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{'loss': 0.733, 'grad_norm': 1.5820422172546387, 'learning_rate': 0.00018814358975418984, 'epoch': 0.58}
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{'loss': 0.8889, 'grad_norm': 1.0317678451538086, 'learning_rate': 0.00018734966664975023, 'epoch': 0.58}
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{'loss': 0.897, 'grad_norm': 1.1359983682632446, 'learning_rate': 0.00018655641714373413, 'epoch': 0.59}
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{'loss': 0.7574, 'grad_norm': 1.0122281312942505, 'learning_rate': 0.00018576384976493282, 'epoch': 0.59}
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{'loss': 0.7553, 'grad_norm': 0.9288797378540039, 'learning_rate': 0.00018497197303480348, 'epoch': 0.59}
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{'loss': 0.74, 'grad_norm': 0.8775319457054138, 'learning_rate': 0.00018418079546737783, 'epoch': 0.59}
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{'loss': 0.8761, 'grad_norm': 1.075302243232727, 'learning_rate': 0.0001833903255691702, 'epoch': 0.59}
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{'loss': 0.7531, 'grad_norm': 0.8293854594230652, 'learning_rate': 0.00011411650365749454, 'epoch': 0.69}
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{'loss': 0.6403, 'grad_norm': 0.865106463432312, 'learning_rate': 0.00011342915153251332, 'epoch': 0.69}
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{'loss': 0.7709, 'grad_norm': 1.0484962463378906, 'learning_rate': 0.0001127432677781425, 'epoch': 0.69}
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{'loss': 0.6684, 'grad_norm': 0.9692133069038391, 'learning_rate': 0.00011137593486306727, 'epoch': 0.69}
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{'loss': 0.8146, 'grad_norm': 1.2010059356689453, 'learning_rate': 0.00011069450040353448, 'epoch': 0.69}
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{'loss': 0.7884, 'grad_norm': 1.0554862022399902, 'learning_rate': 5.276258198957051e-05, 'epoch': 0.79}
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{'loss': 0.7309, 'grad_norm': 1.1452159881591797, 'learning_rate': 5.225994417785726e-05, 'epoch': 0.79}
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{'loss': 0.6804, 'grad_norm': 0.7298494577407837, 'learning_rate': 5.175943241053582e-05, 'epoch': 0.79}
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{'loss': 0.6306, 'grad_norm': 1.0335783958435059, 'learning_rate': 5.1261052068965306e-05, 'epoch': 0.79}
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{'loss': 0.6508, 'grad_norm': 1.478288173675537, 'learning_rate': 1.940660884577894e-08, 'epoch': 1.0}
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{'loss': 0.5287, 'grad_norm': 1.7626447677612305, 'learning_rate': 1.053661447877885e-08, 'epoch': 1.0}
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{'loss': 0.6035, 'grad_norm': 1.4299039840698242, 'learning_rate': 4.354435154813042e-09, 'epoch': 1.0}
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{'loss': 0.6028, 'grad_norm': 0.8753514885902405, 'learning_rate': 8.601373429339976e-10, 'epoch': 1.0}
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+
                                                     
{'eval_loss': 2.729660987854004, 'eval_runtime': 5409.1379, 'eval_samples_per_second': 9.159, 'eval_steps_per_second': 0.286, 'epoch': 1.0}
+
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                                                     /opt/saturncloud/envs/saturn/lib/python3.12/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  partition = torch.load(path, map_location=map_location)
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  partition = torch.load(path, map_location=map_location)
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  partition = torch.load(path, map_location=map_location)
+/opt/saturncloud/envs/saturn/lib/python3.12/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  partition = torch.load(path, map_location=map_location)
+
                                                      
{'train_runtime': 82593.2232, 'train_samples_per_second': 7.499, 'train_steps_per_second': 0.117, 'train_loss': 0.9993063533914609, 'epoch': 1.0}
+
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+rqllama pre-train finished.