reward-train-facebook-opt350m_v3

README.md

@@ -1,22 +1,20 @@
 ---
-license: mit
+license: other
 library_name: peft
 tags:
-- trl
-- reward-trainer
 - generated_from_trainer
-base_model: microsoft/deberta-v3-large
+base_model: facebook/opt-350m
 model-index:
-- name: microsoft/deberta-v3-large
+- name: facebook/opt-350m
   results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# microsoft/deberta-v3-large
+# facebook/opt-350m
 
-This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
+This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
 
 ## Model description
 
@@ -52,7 +50,7 @@ The following hyperparameters were used during training:
 ### Framework versions
 
 - PEFT 0.11.1
-- Transformers 4.41.2
-- Pytorch 2.1.2
-- Datasets 2.19.2
-- Tokenizers 0.19.1
+- Transformers 4.36.2
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.15.0

adapter_config.json

@@ -1,7 +1,7 @@
 {
   "alpha_pattern": {},
   "auto_mapping": null,
-  "base_model_name_or_path": "microsoft/deberta-v3-large",
+  "base_model_name_or_path": "facebook/opt-350m",
   "bias": "none",
   "fan_in_fan_out": false,
   "inference_mode": true,
@@ -24,8 +24,15 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "value_proj",
-    "query_proj"
+    "o_proj",
+    "v_proj",
+    "lm_head",
+    "up_proj",
+    "k_proj",
+    "embed_tokens",
+    "q_proj",
+    "down_proj",
+    "gate_proj"
   ],
   "task_type": "SEQ_CLS",
   "use_dora": false,

adapter_model.safetensors

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runs/Jun13_09-08-39_37181a7da965/events.out.tfevents.1718269720.37181a7da965.26.0

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special_tokens_map.json

@@ -1,12 +1,27 @@
 {
-  "bos_token": "[CLS]",
-  "cls_token": "[CLS]",
-  "eos_token": "[SEP]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
+  "bos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
   "unk_token": {
-    "content": "[UNK]",
+    "content": "</s>",
     "lstrip": false,
     "normalized": true,
     "rstrip": false,

tokenizer_config.json

@@ -1,58 +1,30 @@
 {
+  "add_bos_token": true,
+  "add_prefix_space": false,
   "added_tokens_decoder": {
-    "0": {
-      "content": "[PAD]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
     "1": {
-      "content": "[CLS]",
+      "content": "<pad>",
       "lstrip": false,
-      "normalized": false,
+      "normalized": true,
       "rstrip": false,
       "single_word": false,
       "special": true
     },
     "2": {
-      "content": "[SEP]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "3": {
-      "content": "[UNK]",
+      "content": "</s>",
       "lstrip": false,
       "normalized": true,
       "rstrip": false,
       "single_word": false,
       "special": true
-    },
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-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
     }
   },
-  "bos_token": "[CLS]",
+  "bos_token": "</s>",
   "clean_up_tokenization_spaces": true,
-  "cls_token": "[CLS]",
-  "do_lower_case": false,
-  "eos_token": "[SEP]",
-  "mask_token": "[MASK]",
+  "eos_token": "</s>",
+  "errors": "replace",
   "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "sp_model_kwargs": {},
-  "split_by_punct": false,
-  "tokenizer_class": "DebertaV2Tokenizer",
-  "unk_token": "[UNK]",
-  "vocab_type": "spm"
+  "pad_token": "<pad>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "</s>"
 }

training_args.bin

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'save_total_limit': None, 'save_safetensors': True, 'save_on_each_node': False, 'save_only_model': False, 'no_cuda': False, 'use_cpu': False, 'use_mps_device': False, 'seed': 42, 'data_seed': None, 'jit_mode_eval': False, 'use_ipex': False, 'bf16': False, 'fp16': False, 'fp16_opt_level': 'O1', 'half_precision_backend': 'auto', 'bf16_full_eval': False, 'fp16_full_eval': False, 'tf32': None, 'local_rank': 0, 'ddp_backend': None, 'tpu_num_cores': None, 'tpu_metrics_debug': False, 'debug': [], 'dataloader_drop_last': False, 'eval_steps': None, 'dataloader_num_workers': 0, 'past_index': -1, 'run_name': 'reward-train-facebook-opt350m', 'disable_tqdm': False, 'remove_unused_columns': False, 'label_names': None, 'load_best_model_at_end': False, 'metric_for_best_model': None, 'greater_is_better': None, 'ignore_data_skip': False, 'fsdp': [], 'fsdp_min_num_params': 0, 'fsdp_config': {'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, 'fsdp_transformer_layer_cls_to_wrap': None, 'deepspeed': None, 'label_smoothing_factor': 0.0, 'optim': 'adamw_torch', 'optim_args': None, 'adafactor': False, 'group_by_length': False, 'length_column_name': 'length', 'report_to': ['tensorboard', 'wandb'], 'ddp_find_unused_parameters': None, 'ddp_bucket_cap_mb': None, 'ddp_broadcast_buffers': None, 'dataloader_pin_memory': True, 'dataloader_persistent_workers': False, 'skip_memory_metrics': True, 'use_legacy_prediction_loop': False, 'push_to_hub': False, 'resume_from_checkpoint': None, 'hub_model_id': None, 'hub_strategy': 'every_save', 'hub_token': '<HUB_TOKEN>', 'hub_private_repo': False, 'hub_always_push': False, 'gradient_checkpointing': True, 'gradient_checkpointing_kwargs': None, 'include_inputs_for_metrics': False, 'fp16_backend': 'auto', 'push_to_hub_model_id': None, 'push_to_hub_organization': None, 'push_to_hub_token': '<PUSH_TO_HUB_TOKEN>', 'mp_parameters': '', 'auto_find_batch_size': False, 'full_determinism': False, 'torchdynamo': None, 'ray_scope': 'last', 'ddp_timeout': 1800, 'torch_compile': False, 'torch_compile_backend': None, 'torch_compile_mode': None, 'dispatch_batches': None, 'split_batches': False, 'include_tokens_per_second': False, 'include_num_input_tokens_seen': False, 'neftune_noise_alpha': None}
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wandb/run-20240613_091229-d45ws0q8/files/conda-environment.yaml

@@ -0,0 +1,1066 @@
+name: base
+channels:
+  - pytorch
+  - file:///tmp/conda
+  - rapidsai
+  - nvidia
+  - conda-forge
+  - defaults
+dependencies:
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+      - google-resumable-media==2.5.0
+      - gplearn==0.4.2
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wandb/run-20240613_091229-d45ws0q8/files/config.yaml

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+  - tensorboard
+  - wandb
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+  value: <PUSH_TO_HUB_TOKEN>
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+  value: ''
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wandb/run-20240613_091229-d45ws0q8/files/output.log

@@ -0,0 +1,50 @@
+You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...
+Could not estimate the number of tokens of the input, floating-point operations will not be computed
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.
+  warnings.warn("Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.")
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2636: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:180: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.

wandb/run-20240613_091229-d45ws0q8/files/requirements.txt

@@ -0,0 +1,847 @@
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wandb/run-20240613_091229-d45ws0q8/logs/debug.log

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