End of training

.hydra/config.yaml

@@ -0,0 +1,20 @@
+model:
+  dataset_name: redwoodresearch/generated_stories
+  model_type: gpt_neox
+  pretrained_model_name: EleutherAI/pythia-1.4b-deduped
+  max_length: 1536
+  model_config_params:
+    sensor_loc_type: stories
+    sensor_token: null
+hparams:
+  learning_rate: 2.0e-05
+  weight_decay: 0.02
+  lr_scheduler_type: cosine
+  warmup_steps: 64
+  effective_batch_size: 2
+  num_train_epochs: 2
+per_device_train_batch_size: 2
+per_device_eval_batch_size: 1
+fp16: true
+dataset_len: 2
+push_to_hub: true

.hydra/hydra.yaml

@@ -0,0 +1,181 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    submitit_folder: ${hydra.sweep.dir}/.submitit/%j
+    timeout_min: 1440
+    cpus_per_task: null
+    gpus_per_node: null
+    tasks_per_node: 1
+    mem_gb: 16
+    nodes: 1
+    name: ${hydra.job.name}
+    stderr_to_stdout: false
+    _target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.SlurmLauncher
+    partition: null
+    qos: high
+    comment: null
+    constraint: null
+    exclude: ddpg.ist.berkeley.edu,dqn.ist.berkeley.edu
+    gres: gpu:A6000:1
+    cpus_per_gpu: null
+    gpus_per_task: null
+    mem_per_gpu: null
+    mem_per_cpu: null
+    account: null
+    signal_delay_s: 120
+    max_num_timeout: 0
+    additional_parameters: {}
+    array_parallelism: 256
+    setup: null
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: MULTIRUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=MULTIRUN
+    task: []
+  job:
+    name: train
+    chdir: null
+    override_dirname: ''
+    id: '746837'
+    num: 0
+    config_name: pythia_stories_local_test
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.1'
+    cwd: /nas/ucb/oliveradk/measurement-pred
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /nas/ucb/oliveradk/measurement-pred/conf
+      schema: file
+      provider: main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /nas/ucb/oliveradk/measurement-pred/multirun/2024-12-16/19-19-57/0
+    choices:
+      hparams: hparams
+      model: pythia_stories
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: slurm_chai
+      hydra/output: default
+  verbose: false

.hydra/overrides.yaml

@@ -0,0 +1 @@
+[]

README.md

@@ -0,0 +1,71 @@
+---
+license: apache-2.0
+base_model: EleutherAI/pythia-1.4b-deduped
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: pythia-1.4b-deduped-measurement_pred-generated_stories
+  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. -->
+
+# pythia-1.4b-deduped-measurement_pred-generated_stories
+
+This model is a fine-tuned version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6540
+- Accuracy: 0.625
+- Accuracy Sensor 0: 1.0
+- Auroc Sensor 0: 1.0
+- Accuracy Sensor 1: 0.5
+- Auroc Sensor 1: 1.0
+- Accuracy Sensor 2: 0.5
+- Auroc Sensor 2: 0.0
+- Accuracy Aggregated: 0.5
+- Auroc Aggregated: 1.0
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 2
+- eval_batch_size: 1
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 64
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy Sensor 0 | Auroc Sensor 0 | Accuracy Sensor 1 | Auroc Sensor 1 | Accuracy Sensor 2 | Auroc Sensor 2 | Accuracy Aggregated | Auroc Aggregated |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
+| No log        | 1.0   | 1    | 1.6540          | 0.625    | 1.0               | 1.0            | 0.5               | 1.0            | 0.5               | 0.0            | 0.5                 | 1.0              |
+| No log        | 2.0   | 2    | 1.6540          | 0.625    | 1.0               | 1.0            | 0.5               | 1.0            | 0.5               | 0.0            | 0.5                 | 1.0              |
+
+
+### Framework versions
+
+- Transformers 4.41.0
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,43 @@
+{
+  "_name_or_path": "EleutherAI/pythia-1.4b-deduped",
+  "aggregate_weight": 0.3,
+  "architectures": [
+    "GPTNeoXMeasurementPredictor"
+  ],
+  "attention_bias": true,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_gpt_neox_measurement_pred.GPTNeoXMeasurementPredictorConfig",
+    "AutoModelForSequenceClassification": "modeling_gpt_neox_measurement_pred.GPTNeoXMeasurementPredictor"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": 0.1,
+  "emb_dim": 2048,
+  "eos_token_id": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.0,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "gpt_neox_mp",
+  "n_sensors": 3,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 50277,
+  "rope_scaling": null,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 0.25,
+  "sensor_loc_type": "stories",
+  "sensor_token": null,
+  "sensor_token_id": 35991,
+  "sensors_weight": 0.7,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.0",
+  "use_aggregated": true,
+  "use_cache": false,
+  "use_parallel_residual": true,
+  "vocab_size": 50304
+}

configuration_gpt_neox_measurement_pred.py

@@ -0,0 +1,12 @@
+from transformers.models.gpt_neox import GPTNeoXConfig
+from .configuration_measurement_pred import MeasurementPredictorConfig
+
+
+class GPTNeoXMeasurementPredictorConfig(MeasurementPredictorConfig, GPTNeoXConfig):
+    model_type = "gpt_neox_mp"
+    def __init__(self, **kwargs):
+        kwargs["sensor_token_id"] = 35991
+        super().__init__(**kwargs)
+    
+    def get_emb_dim(self):
+        return self.hidden_size

configuration_measurement_pred.py

@@ -0,0 +1,26 @@
+from abc import abstractmethod
+from transformers import PretrainedConfig
+class MeasurementPredictorConfig(PretrainedConfig):
+    
+    def __init__(
+        self, 
+        sensor_token=" omit",
+        sensor_loc_type="locs_from_token",
+        n_sensors=3,
+        use_aggregated=True,
+        sensors_weight = 0.7,
+        aggregate_weight=0.3,
+        **kwargs
+    ):
+        self.sensor_token = sensor_token 
+        self.sensor_loc_type = sensor_loc_type
+        self.n_sensors = n_sensors
+        self.use_aggregated = use_aggregated
+        self.sensors_weight = sensors_weight
+        self.aggregate_weight = aggregate_weight
+        super().__init__(**kwargs)
+        self.emb_dim = self.get_emb_dim()
+    
+    @abstractmethod
+    def get_emb_dim(self):
+        raise NotImplementedError

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+    "sensor_probes.1.bias": "model-00001-of-00002.safetensors",
+    "sensor_probes.1.weight": "model-00001-of-00002.safetensors",
+    "sensor_probes.2.bias": "model-00001-of-00002.safetensors",
+    "sensor_probes.2.weight": "model-00001-of-00002.safetensors"
+  }
+}

modeling_gpt_neox_measurement_pred.py

@@ -0,0 +1,12 @@
+from transformers.models.gpt_neox import GPTNeoXPreTrainedModel, GPTNeoXModel
+
+from .modeling_measurement_pred import MeasurementPredictorMixin
+from .configuration_gpt_neox_measurement_pred import GPTNeoXMeasurementPredictorConfig
+
+class GPTNeoXMeasurementPredictor(GPTNeoXPreTrainedModel, MeasurementPredictorMixin):
+    config_class = GPTNeoXMeasurementPredictorConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.gpt_neox = GPTNeoXModel(config)
+        self.post_init()

modeling_measurement_pred.py

@@ -0,0 +1,104 @@
+from typing import Optional, Tuple, Union
+
+import torch
+from torch.nn import BCEWithLogitsLoss
+from transformers import PreTrainedModel, PreTrainedTokenizer
+from transformers.tokenization_utils_base import PreTrainedTokenizerBase
+from transformers.modeling_outputs import BaseModelOutputWithPast, SequenceClassifierOutputWithPast
+
+
+from .sensor_loc_reg import SENSOR_LOC_REGISTRY
+from .sensor_loc_finder import SensorLocFinder
+
+class MeasurementPredictorMixin(PreTrainedModel):
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.sensor_loc_type = config.sensor_loc_type
+        self.sensor_token = config.sensor_token
+        self.n_sensors = config.n_sensors
+        self.sensor_probes = torch.nn.ModuleList([
+            torch.nn.Linear(config.emb_dim, 1) for _ in range(config.n_sensors)
+        ])
+        self.use_aggregated = config.use_aggregated
+        if config.use_aggregated:
+            self.aggregate_probe = torch.nn.Linear(config.emb_dim, 1)
+        self.sensors_weight = config.sensors_weight
+        self.aggregate_weight = config.aggregate_weight
+
+        self.get_sensor_locs: SensorLocFinder = None 
+    
+    def init_sensor_loc_finder(self, tokenizer: PreTrainedTokenizerBase):
+        self.get_sensor_locs = SENSOR_LOC_REGISTRY[self.sensor_loc_type](
+            tokenizer, sensor_token=self.sensor_token, n_sensors=self.n_sensors
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        base_model_output: BaseModelOutputWithPast = self.base_model(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sensor_locs = self.get_sensor_locs(input_ids)
+        sensor_embs = base_model_output.last_hidden_state.gather(
+            1, sensor_locs.unsqueeze(-1).expand(-1, -1, self.config.emb_dim)
+        )
+        assert sensor_embs.shape == (input_ids.shape[0], self.n_sensors, self.config.emb_dim), f"{sensor_embs.shape} != {(input_ids.shape[0], self.n_sensors, self.config.emb_dim)}"
+        sensor_logits = torch.concat([self.sensor_probes[i](sensor_embs[:, i, :]) 
+                               for i in range(self.n_sensors)], dim=-1)
+        logits = sensor_logits
+
+        if self.use_aggregated:
+            last_emb = base_model_output.last_hidden_state[:, -1, :]
+            aggregate_logits = self.aggregate_probe(last_emb)
+            logits = torch.concat([logits, aggregate_logits], dim=-1)
+        
+        loss = None
+        if labels is not None:
+            loss_fct = BCEWithLogitsLoss()
+            sensor_loss = loss_fct(sensor_logits, labels[:, :self.n_sensors]) * self.sensors_weight
+            loss = sensor_loss
+            if self.use_aggregated: #TOOD: should be use aggregate
+                aggregate_loss = loss_fct(aggregate_logits, labels[:, -1:]) * self.aggregate_weight
+                loss += aggregate_loss
+
+        if not return_dict:
+            output = (logits, ) + base_model_output[1:]
+            return ((loss,) + output) if loss is not None else output 
+        
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=base_model_output.past_key_values,
+            hidden_states=base_model_output.hidden_states,
+            attentions=base_model_output.attentions,
+        )
+

sensor_loc_finder.py

@@ -0,0 +1,17 @@
+from abc import ABC, abstractmethod
+import torch
+from transformers import PreTrainedTokenizerBase
+
+
+class SensorLocFinder(ABC):
+
+    @abstractmethod
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, **kwargs):
+        pass
+
+    @abstractmethod
+    def find_sensor_locs(self, input_ids: torch.Tensor) -> torch.Tensor:
+        pass
+
+    def __call__(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.find_sensor_locs(input_ids)

sensor_loc_reg.py

@@ -0,0 +1,10 @@
+from enum import Enum
+
+from .sensor_loc_stories import StoriesSensorLocFinder
+from .sensor_locs_from_token import SensorLocFinderFromToken
+
+
+SENSOR_LOC_REGISTRY = {
+    "stories": StoriesSensorLocFinder,
+    "locs_from_token": SensorLocFinderFromToken
+}

sensor_loc_stories.py

@@ -0,0 +1,46 @@
+import torch
+from transformers import PreTrainedTokenizerBase
+
+from .sensor_loc_finder import SensorLocFinder
+
+
+class StoriesSensorLocFinder(SensorLocFinder):
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, **kwargs):
+        self.questions_section_toks = tokenizer.encode("## Questions")
+        self.question_mark_tok = tokenizer.encode("?")[0]
+        self.other_question_mark_tok = tokenizer.encode(")?")[0]
+        assert len(self.questions_section_toks) == 2
+
+    def find_sensor_locs(self, input_ids: torch.Tensor) -> torch.Tensor:
+        device = input_ids.device
+        question_mark_locs = self._is_sensor_loc(input_ids)
+        total_locs = torch.cumsum(question_mark_locs, dim=-1)
+        total_overall = total_locs[:, -1]
+        assert (
+            total_overall == 3
+        ).all(), "can handle different cases, but assuming this is easiest"
+        eqs = total_locs[:, :, None] == torch.arange(1, 4)[None, None].to(device)
+        locs = torch.where(
+            eqs.any(dim=-2),
+            torch.argmax(eqs.to(torch.uint8), dim=-2),
+            input_ids.shape[-1] - 3,
+        ).clamp(max=input_ids.shape[-1] - 3)
+        return locs
+
+    
+    def _is_sensor_loc(self, input_ids: torch.Tensor):
+        questions_section_toks = self.questions_section_toks
+        question_mark_tok = self.question_mark_tok
+        other_question_mark_tok = self.other_question_mark_tok
+        eq_question_item = (input_ids[:, :-1] == questions_section_toks[0]) & (
+            input_ids[:, 1:] == questions_section_toks[1]
+        )
+        assert (eq_question_item.sum(dim=-1, dtype=torch.int) == 1).all(), "could relax"
+
+        summed = torch.cumsum(
+            torch.cat([eq_question_item, eq_question_item[:, -1:]], dim=-1), dim=-1
+        )
+        return (summed > 0) & (
+            (input_ids == question_mark_tok) | (input_ids == other_question_mark_tok)
+        )

sensor_locs_from_token.py

@@ -0,0 +1,16 @@
+import torch
+from transformers import PreTrainedTokenizerBase
+
+from .sensor_loc_finder import SensorLocFinder
+
+
+class SensorLocFinderFromToken(SensorLocFinder):
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, sensor_token: str, n_sensors: int):
+        self.sensor_token_id = tokenizer.encode(sensor_token)[0]
+        self.n_sensors = n_sensors
+
+    def find_sensor_locs(self, input_ids: torch.Tensor) -> torch.Tensor:
+        flat_sensor_token_idxs = (input_ids == self.sensor_token_id).nonzero(as_tuple=True)[1]
+        sensor_token_idxs = flat_sensor_token_idxs.view(-1, self.n_sensors)
+        return sensor_token_idxs

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,224 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|padding|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50254": {
+      "content": "                        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50255": {
+      "content": "                       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50256": {
+      "content": "                      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50257": {
+      "content": "                     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50258": {
+      "content": "                    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
+      "content": "                   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50260": {
+      "content": "                  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50261": {
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+      "rstrip": false,
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+    "50270": {
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+      "normalized": true,
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50275": {
+      "content": "   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50276": {
+      "content": "  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50277": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "padding_side": "left",
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "truncation_side": "left",
+  "unk_token": "<|endoftext|>"
+}

train.log

@@ -0,0 +1 @@
+[2024-12-16 19:20:07,423][accelerate.utils.other][WARNING] - Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccc5f117ab4a216525346209c056ed0fc4ab9b532db00f6b6e118e8d1834990e
+size 5112