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README.md

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+---
+library_name: transformers
+license: mit
+widget:
+  - text: MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG
+---
+
+## ESM-2 (TransformerEngine-optimized)
+
+This version of the ESM-2 model is optimized with NVIDIA's
+[TransformerEngine](https://github.com/NVIDIA/TransformerEngine) library. It is based on the
+[original ESM-2 model](https://huggingface.co/facebook/esm2_t48_15B_UR50D) from Facebook Research,
+and (within numerical precision) has identical weights and outputs.
+
+ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is
+suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed
+information on the model architecture and training data, please refer to the [accompanying
+paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in
+some demo notebooks
+([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb),
+[TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb))
+which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
+
+Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have
+somewhat better accuracy, but require much more memory and time to train:
+
+| Checkpoint name                                                          | Num layers | Num parameters |
+| ------------------------------------------------------------------------ | ---------- | -------------- |
+| [esm2_t48_15B_UR50D](https://huggingface.co/nvidia/esm2_t48_15B_UR50D)   | 48         | 15B            |
+| [esm2_t36_3B_UR50D](https://huggingface.co/nvidia/esm2_t36_3B_UR50D)     | 36         | 3B             |
+| [esm2_t33_650M_UR50D](https://huggingface.co/nvidia/esm2_t33_650M_UR50D) | 33         | 650M           |
+| [esm2_t30_150M_UR50D](https://huggingface.co/nvidia/esm2_t30_150M_UR50D) | 30         | 150M           |
+| [esm2_t12_35M_UR50D](https://huggingface.co/nvidia/esm2_t12_35M_UR50D)   | 12         | 35M            |
+| [esm2_t6_8M_UR50D](https://huggingface.co/nvidia/esm2_t6_8M_UR50D)       | 6          | 8M             |

config.json

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+{
+  "architectures": [
+    "NVEsmForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "attn_input_format": "bshd",
+  "auto_map": {
+    "AutoConfig": "esm_nv.NVEsmConfig",
+    "AutoModel": "esm_nv.NVEsmModel",
+    "AutoModelForMaskedLM": "esm_nv.NVEsmForMaskedLM"
+  },
+  "classifier_dropout": null,
+  "emb_layer_norm_before": false,
+  "encoder_activation": "gelu",
+  "esmfold_config": null,
+  "fuse_qkv_params": true,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 20480,
+  "is_folding_model": false,
+  "layer_norm_eps": 1e-05,
+  "mask_token_id": 32,
+  "max_position_embeddings": 1026,
+  "max_seq_length": null,
+  "micro_batch_size": null,
+  "model_type": "nv_esm",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "pad_token_id": 1,
+  "position_embedding_type": "rotary",
+  "qkv_weight_interleaved": true,
+  "token_dropout": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "use_cache": true,
+  "vocab_list": null,
+  "vocab_size": 33
+}

esm_nv.py

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+# coding=utf-8
+# noqa: license-check
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2025 NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""TransformerEngine-optimized ESM model.
+
+Adapted from `modeling_esm.py` in huggingface/transformers.
+"""
+
+from typing import Optional, Tuple, Union
+
+# TODO: put import guard around transformer_engine here, with an informative error message around
+# installation and the nvidia docker container.
+import torch
+import transformer_engine.pytorch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformer_engine.pytorch.attention.rope import RotaryPositionEmbedding
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.esm.configuration_esm import EsmConfig
+from transformers.models.esm.modeling_esm import EsmEmbeddings, EsmPooler
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class NVEsmConfig(EsmConfig):
+    """NVEsmConfig is a configuration for the NVEsm model."""
+
+    model_type: str = "nv_esm"
+
+    def __init__(
+        self,
+        qkv_weight_interleaved: bool = True,
+        encoder_activation: str = "gelu",
+        attn_input_format: str = "bshd",
+        fuse_qkv_params: bool = True,
+        micro_batch_size: Optional[int] = None,
+        max_seq_length: Optional[int] = None,
+        **kwargs,
+    ):
+        """Initialize the NVEsmConfig with additional TE-related config options.
+
+        Args:
+            qkv_weight_interleaved: Whether to interleave the qkv weights. If set to `False`, the
+                QKV weight is interpreted as a concatenation of query, key, and value weights along
+                the `0th` dimension. The default interpretation is that the individual `q`, `k`, and
+                `v` weights for each attention head are interleaved. This parameter is set to `False`
+                when using :attr:`fuse_qkv_params=False`.
+            encoder_activation: The activation function to use in the encoder.
+            attn_input_format: The input format to use for the attention. This controls
+                whether the dimensions of the intermediate hidden states is 'batch first'
+                ('bshd') or 'sequence first' ('sbhd'). `s` stands for the sequence length,
+                `b` batch size, `h` the number of heads, `d` head size. Note that these
+                formats are very closely related to the `qkv_format` in the
+                `MultiHeadAttention` and `DotProductAttention` modules.
+            fuse_qkv_params: Whether to fuse the qkv parameters. If set to `True`,
+                `TransformerLayer` module exposes a single fused parameter for query-key-value.
+                This enables optimizations such as QKV fusion without concatentations/splits and
+                also enables the argument `fuse_wgrad_accumulation`.
+            micro_batch_size: The micro batch size to use for the attention. This is needed for
+                JIT Warmup, a technique where jit fused functions are warmed up before training to
+                ensure same kernels are used for forward propogation and activation recompute phase.
+            max_seq_length: The maximum sequence length to use for the attention. This is needed for
+                JIT Warmup, a technique where jit fused functions are warmed up before training to
+                ensure same kernels are used for forward propogation and activation recompute phase.
+            **kwargs: Additional config options to pass to EsmConfig.
+        """
+        super().__init__(**kwargs)
+        # Additional TE-related config options.
+        self.qkv_weight_interleaved = qkv_weight_interleaved
+        self.encoder_activation = encoder_activation
+        self.attn_input_format = attn_input_format
+        self.fuse_qkv_params = fuse_qkv_params
+        self.micro_batch_size = micro_batch_size
+        self.max_seq_length = max_seq_length
+
+
+class NVEsmEncoder(nn.Module):
+    """NVEsmEncoder is a TransformerEngine-optimized ESM encoder."""
+
+    def __init__(self, config: NVEsmConfig):
+        """Initialize a NVEsmEncoder.
+
+        Args:
+            config (NVEsmConfig): The configuration of the model.
+        """
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList(
+            [
+                transformer_engine.pytorch.TransformerLayer(
+                    hidden_size=config.hidden_size,
+                    ffn_hidden_size=config.intermediate_size,
+                    num_attention_heads=config.num_attention_heads,
+                    layernorm_epsilon=config.layer_norm_eps,
+                    hidden_dropout=config.hidden_dropout_prob,
+                    attention_dropout=config.attention_probs_dropout_prob,
+                    qkv_weight_interleaved=config.qkv_weight_interleaved,
+                    layer_number=i + 1,
+                    layer_type="encoder",
+                    self_attn_mask_type="padding",
+                    activation=config.encoder_activation,
+                    attn_input_format=config.attn_input_format,
+                    seq_length=config.max_seq_length,
+                    micro_batch_size=config.micro_batch_size,
+                    num_gqa_groups=config.num_attention_heads,
+                    fuse_qkv_params=config.fuse_qkv_params,
+                    params_dtype=config.torch_dtype,
+                    window_size=(-1, -1),
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.emb_layer_norm_after = transformer_engine.pytorch.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        if config.position_embedding_type == "rotary":
+            self.rotary_embeddings = RotaryPositionEmbedding(config.hidden_size // config.num_attention_heads)
+            self.te_rope_emb = self.rotary_embeddings(max_seq_len=config.max_position_embeddings)
+        else:
+            self.te_rope_emb = None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: bool = False,
+    ):
+        """Forward pass of the NVEsmEncoder.
+
+        Args:
+            hidden_states (torch.Tensor): The hidden states.
+            attention_mask (torch.Tensor): The attention mask.
+            output_hidden_states (bool): Whether to output the hidden states.
+        """
+        all_hidden_states = () if output_hidden_states else None
+
+        if self.te_rope_emb is not None:
+            te_rope_emb = self.te_rope_emb.to(hidden_states.device, non_blocking=True)
+            te_rope_emb = te_rope_emb[: hidden_states.shape[1]]
+        else:
+            te_rope_emb = None
+
+        for layer_module in self.layers:
+            if output_hidden_states:
+                all_hidden_states = (*all_hidden_states, hidden_states)
+
+            hidden_states = layer_module(
+                hidden_states,
+                attention_mask,
+                rotary_pos_emb=te_rope_emb,
+            )
+
+        hidden_states = self.emb_layer_norm_after(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = (*all_hidden_states, hidden_states)
+
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+        )
+
+
+class NVEsmPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and pretrained model loading."""
+
+    config_class = NVEsmConfig
+    base_model_prefix = "esm"
+    supports_gradient_checkpointing = False
+    _no_split_modules = (
+        "TransformerLayer",
+        "EsmEmbeddings",
+    )
+
+    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights.
+
+        Args:
+            module (nn.Module): The module to initialize the weights for.
+        """
+        if isinstance(
+            module, (nn.Linear, transformer_engine.pytorch.Linear, transformer_engine.pytorch.LayerNormLinear)
+        ):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        if isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        if isinstance(module, (nn.LayerNorm, transformer_engine.pytorch.LayerNorm)):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, transformer_engine.pytorch.LayerNormLinear):
+            module.layer_norm_weight.data.fill_(1.0)
+            if module.layer_norm_bias is not None:
+                module.layer_norm_bias.data.zero_()
+
+
+class NVEsmModel(NVEsmPreTrainedModel):
+    """The ESM Encoder-only protein language model.
+
+    This model uses NVDIA's TransformerEngine to optimize attention layer training and inference.
+    """
+
+    def __init__(self, config: NVEsmConfig, add_pooling_layer: bool = True):
+        """Initialize a NVEsmModel.
+
+        Args:
+            config (NVEsmConfig): The configuration of the model.
+            add_pooling_layer (bool): Whether to add a pooling layer.
+        """
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = EsmEmbeddings(config)
+        self.encoder = NVEsmEncoder(config)
+        self.pooler = EsmPooler(config) if add_pooling_layer else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        """Get the input embeddings of the model."""
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value: torch.Tensor):
+        """Set the input embeddings of the model.
+
+        Args:
+            value (torch.Tensor): The input embeddings.
+        """
+        self.embeddings.word_embeddings = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        """Forward pass of the NVEsmModel.
+
+        Args:
+            input_ids (torch.Tensor): The input ids.
+            attention_mask (torch.Tensor): The attention mask.
+            position_ids (torch.Tensor): The position ids.
+            head_mask (torch.Tensor): The head mask.
+            inputs_embeds (torch.Tensor): The input embeddings.
+            output_hidden_states (bool): Whether to output the hidden states.
+
+        Returns:
+            BaseModelOutputWithPooling: The output of the model.
+        """
+        r"""
+        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the
+            cross-attention if the model is configured as a decoder.
+        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on the padding token indices of the encoder input.
+            This mask is used in the cross-attention if the model is configured as a decoder. Mask
+            values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            Note that this mask is inverted when it is passed to TransformerEngine, which expects a
+            boolean mask where 1s are masked and 0s are not masked.
+        """
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length)), device=device)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+        # TE expects a boolean attention mask, where 1s are masked and 0s are not masked
+        extended_attention_mask = extended_attention_mask < -1
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+        )
+
+
+class NVEsmForMaskedLM(NVEsmPreTrainedModel):
+    """NVEsmForMaskedLM is a TransformerEngine-optimized ESM model for masked language modeling."""
+
+    _tied_weights_keys = ("lm_head.decoder.weight",)
+
+    def __init__(self, config: NVEsmConfig):
+        """Initialize a NVEsmForMaskedLM.
+
+        Args:
+            config (NVEsmConfig): The configuration of the model.
+        """
+        super().__init__(config)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.esm = NVEsmModel(config, add_pooling_layer=False)
+        self.lm_head = NVEsmLMHead(config)
+
+        self.init_weights()
+        self.post_init()
+
+    def get_output_embeddings(self):
+        """Get the output embeddings of the model."""
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        """Set the output embeddings of the model."""
+        self.lm_head.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        """Forward pass of the NVEsmForMaskedLM.
+
+        Args:
+            input_ids (torch.LongTensor): The input ids.
+            attention_mask (torch.Tensor): The attention mask.
+            position_ids (torch.LongTensor): The position ids.
+            inputs_embeds (torch.FloatTensor): The input embeddings.
+            labels (torch.LongTensor): The labels.
+            output_hidden_states (bool): Whether to output the hidden states.
+
+        Returns:
+            MaskedLMOutput: The output of the model.
+        """
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        kwargs (`Dict[str, any]`, *optional*, defaults to `{}`):
+            Used to hide legacy arguments that have been deprecated.
+        """
+        outputs = self.esm(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(prediction_scores.device)
+            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+        )
+
+    def predict_contacts(self, tokens: torch.Tensor, attention_mask: torch.Tensor):
+        """Predict the contacts of the model.
+
+        Args:
+            tokens (torch.Tensor): The tokens.
+            attention_mask (torch.Tensor): The attention mask.
+
+        Returns:
+            torch.Tensor: The predicted contacts.
+        """
+        return self.esm.predict_contacts(tokens, attention_mask=attention_mask)
+
+
+class NVEsmLMHead(nn.Module):
+    """ESM Head for masked language modeling using TransformerEngine."""
+
+    def __init__(self, config: NVEsmConfig):
+        """Initialize a NVEsmLMHead.
+
+        Args:
+            config (NVEsmConfig): The configuration of the model.
+        """
+        super().__init__()
+        self.dense = transformer_engine.pytorch.Linear(config.hidden_size, config.hidden_size)
+
+        self.decoder = transformer_engine.pytorch.LayerNormLinear(
+            config.hidden_size,
+            config.vocab_size,
+            bias=True,
+            eps=config.layer_norm_eps,
+        )
+
+    def forward(self, features, **kwargs):
+        """Forward pass of the NVEsmLMHead.
+
+        Args:
+            features (torch.Tensor): The features.
+            **kwargs: Additional arguments.
+        """
+        x = self.dense(features)
+        x = torch.nn.functional.gelu(x)
+        x = self.decoder(x)
+        return x

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+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "<cls>",
+  "eos_token": "<eos>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,53 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<cls>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<eos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<cls>",
+  "eos_token": "<eos>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "tokenizer_class": "EsmTokenizer",
+  "unk_token": "<unk>"
+}

vocab.txt

@@ -0,0 +1,33 @@
+<cls>
+<pad>
+<eos>
+<unk>
+L
+A
+G
+V
+S
+E
+R
+T
+I
+D
+P
+K
+Q
+N
+F
+Y
+M
+H
+W
+C
+X
+B
+U
+Z
+O
+.
+-
+<null_1>
+<mask>