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update third-party huggingface compatible models for DenseMamba: https://arxiv.org/abs/2403.00818

added_tokens.json

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+{
+  "[PAD]": 32000
+}

config.json

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+{
+  "auto_map": {
+    "AutoConfig": "modeling_dense_gau_retnet.DenseGauRetNetConfig",
+    "AutoModelForCausalLM": "modeling_dense_gau_retnet.DenseGauRetNetForCausalLM",
+    "AutoModelForSequenceClassification": "modeling_dense_gau_retnet.DenseGauRetNetForSequenceClassification"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "query_key_dim": 768,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 2048,
+  "model_type": "DenseGauRetNet",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 16,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "layernorm_eps": 1e-5,
+  "retnorm": false,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.1",
+  "use_cache": false,
+  "vocab_size": 32001,
+  "v_factor": 2,
+  "intermediate_k_select_scale": 8,
+  "intermediate_v_select_scale": 32,
+  "dense_block_layers": 2,
+  "dropout": 0.1,
+  "deepnorm": false
+  }

configuration_dense_gau_retnet.py

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+# coding=utf-8
+# Copyright 2022 EleutherAI and the Huggingface Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" DenseGauRetNet model configuration"""
+
+from transformers.utils import logging
+from transformers.configuration_utils import PretrainedConfig
+
+logger = logging.get_logger(__name__)
+
+DenseGauRetNet_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class DenseGauRetNetConfig(PretrainedConfig):
+    model_type = "DenseGauRetNet"
+    _auto_class = "AutoConfig"
+
+    def __init__(
+            self,
+            hidden_act: str = "silu",
+            hidden_size: int = 1536,
+            query_key_dim: int = 768,
+            initializer_range: float = 0.02,
+            max_position_embeddings: int = 2048,
+            num_attention_heads: int = 2,
+            num_hidden_layers: int = 16,
+            rms_norm_eps: float = 1e-06,
+            layernorm_eps: float = 1e-5,
+            retnorm: bool = False,
+            vocab_size: int = 32001,
+            v_factor: int = 2,
+            intermediate_k_select_scale: int = 8,
+            intermediate_v_select_scale: int = 32,
+            dense_block_layers: int = 2,
+            dropout: float = 0.1,
+            use_cache: bool = False,
+            deepnorm: bool = False,
+            pad_token_id=0,
+            bos_token_id=1,
+            eos_token_id=2,
+            tie_word_embeddings=False,
+
+            **kwargs,
+    ):
+        self.hidden_act = hidden_act
+        self.hidden_size = hidden_size
+        self.query_key_dim = query_key_dim
+        self.initializer_range = initializer_range
+        self.max_position_embeddings = max_position_embeddings
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.rms_norm_eps = rms_norm_eps
+        self.layernorm_eps = layernorm_eps
+        self.retnorm = retnorm
+        self.vocab_size = vocab_size
+        self.v_factor = v_factor
+        self.intermediate_k_select_scale = intermediate_k_select_scale
+        self.intermediate_v_select_scale = intermediate_v_select_scale
+        self.dense_block_layers = dense_block_layers
+        self.dropout = dropout
+        self.use_cache = use_cache
+        self.deepnorm = deepnorm
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.29.1",
+  "use_cache": false
+}

modeling_dense_gau_retnet.py

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+# coding=utf-8
+# Copyright 2022 EleutherAI and the Huggingface Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" Pytorch DenseGAU RetNet model."""
+from typing import List, Optional, Tuple, Union
+import math
+import torch
+import torch.utils.checkpoint
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \
+    SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, \
+    replace_return_docstrings
+from transformers import top_k_top_p_filtering
+from transformers.generation.configuration_utils import GenerationConfig
+from .configuration_dense_gau_retnet import DenseGauRetNetConfig
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DenseGauRetNetConfig"
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+        input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm
+class DenseGauRetNetRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        return (self.weight * hidden_states).to(input_dtype)
+
+# added for retention
+# Copied from https://github.com/microsoft/torchscale/blob/main/torchscale/component/multiscale_retention.py
+def rotate_every_two(x):
+    x1 = x[:, :, :, ::2]
+    x2 = x[:, :, :, 1::2]
+    x = torch.stack((-x2, x1), dim=-1)
+    return x.flatten(-2)  # in einsum notation: rearrange(x, '... d j -> ... (d j)')\
+def theta_shift(x, sin, cos):
+    return (x * cos) + (rotate_every_two(x) * sin)
+
+
+#Parameter efficient HiddenProjection
+class HiddenProjection(nn.Module):
+    def __init__(self, input_dim, mid_reduction_ratio=16, final_reduction_ratio=4):
+        super(HiddenProjection, self).__init__()
+        self.fc1 = nn.Linear(input_dim, input_dim // mid_reduction_ratio, bias=False)
+        self.fc2 = nn.Linear(input_dim // mid_reduction_ratio, int(input_dim // final_reduction_ratio), bias=False)
+
+    def forward(self, x):
+        fc1_output = F.silu(self.fc1(x))
+        fc2_output = self.fc2(fc1_output)
+        return fc2_output
+
+# Copied and modified from transformers.models.bart.modeling_bart._expand_mask
+
+class MultiScaleGauRetention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: DenseGauRetNetConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.query_key_dim = config.query_key_dim
+        self.num_heads = config.num_attention_heads
+        self.factor = config.v_factor
+        self.head_dim = config.hidden_size * self.factor // self.num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.q_proj = nn.Linear(self.hidden_size, self.query_key_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.query_key_dim, bias=False)
+        self.key_dim = self.query_key_dim // self.num_heads
+        self.scaling = self.key_dim ** -0.5
+        self.expansion_dim = int(config.hidden_size * self.factor)
+        self.group_norm = DenseGauRetNetRMSNorm(self.expansion_dim // config.num_attention_heads, eps=config.rms_norm_eps)
+        self.to_hidden = nn.Sequential(
+            nn.Linear(config.hidden_size, self.expansion_dim * 2, bias=False),
+            nn.SiLU()
+        )
+        self.to_out = nn.Sequential(
+            nn.Linear(self.expansion_dim, config.hidden_size, bias=False),
+            nn.Dropout(0)
+        )
+        self.config = config
+        self.k_select = HiddenProjection(self.hidden_size, config.intermediate_k_select_scale, 2)
+        self.v_select = HiddenProjection(self.hidden_size, config.intermediate_v_select_scale, 0.5)
+        self.k_norm = DenseGauRetNetRMSNorm(self.query_key_dim, eps=config.rms_norm_eps)
+        self.v_norm = DenseGauRetNetRMSNorm(self.expansion_dim, eps=config.rms_norm_eps)
+        if config.deepnorm:
+            self.alpha = math.pow(2.0 * config.num_hidden_layers, 0.25)
+        else:
+            self.alpha = 1.0
+        self.dropout_module = torch.nn.Dropout(config.dropout)
+        self.reset_parameters()
+
+    #
+    def reset_parameters(self):
+        nn.init.xavier_uniform_(self.q_proj.weight, gain=2 ** -2.5)
+        nn.init.xavier_uniform_(self.k_proj.weight, gain=2 ** -2.5)
+        nn.init.xavier_uniform_(self.k_select.fc1.weight, gain=2 ** -2.5)
+        nn.init.xavier_uniform_(self.k_select.fc2.weight, gain=2 ** -2.5)
+        nn.init.xavier_uniform_(self.v_select.fc1.weight, gain=2 ** -2.5)
+        nn.init.xavier_uniform_(self.v_select.fc2.weight, gain=2 ** -2.5)
+        for module in self.to_out.modules():
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight, gain=2 ** -1)
+        for module in self.to_hidden.modules():
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5)
+
+    def forward(
+            self,
+            forward_impl: 'parallel',
+            hidden_states: torch.Tensor,
+            rel_pos,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: bool = False,
+            k_features=None,  # dense
+            v_features=None,  # dense
+            dense=False,
+            dense_layers=0,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, tgt_len, _ = hidden_states.size()
+        (sin, cos), inner_mask = rel_pos
+        x = hidden_states
+        q = F.silu(self.q_proj(x))
+        k = F.silu(self.k_proj(x))
+        v, gate = self.to_hidden(x).chunk(2, dim=-1)
+
+        k *= self.scaling
+        k_curr = k
+        v_curr = v
+
+        if dense:
+            k_gate = self.k_select(hidden_states.clone())
+            for i, k_past in enumerate(k_features):
+                k = k.clone() + F.silu(k_gate) * k_past
+            k = self.k_norm(k)
+
+            v_gate = self.v_select(hidden_states.clone())
+            for i, v_past in enumerate(v_features):
+                v = v.clone() + F.silu(v_gate) * v_past
+            v = self.v_norm(v)
+
+        q = q.view(bsz, tgt_len, self.num_heads, self.key_dim).transpose(1, 2)
+        k = k.view(bsz, tgt_len, self.num_heads, self.key_dim).transpose(1, 2)
+        qr = theta_shift(q, sin, cos)
+        kr = theta_shift(k, sin, cos)
+
+        if forward_impl == 'parallel':
+            output = self.parallel_forward(qr, kr, v, inner_mask)
+        elif forward_impl == 'recurrent':
+            output, past_key_value = self.recurrent_forward(qr, kr, v, inner_mask, past_key_value=past_key_value)
+
+        output = self.group_norm(output)
+        output = output.reshape(bsz, tgt_len, self.expansion_dim) * gate  # gate
+        output = self.to_out(output)
+        output = self.dropout_module(output)
+
+        return output, past_key_value, k_curr, v_curr
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    # retntion parallel forward
+    def recurrent_forward(
+            self,
+            qr, kr, v,
+            decay,
+            past_key_value,
+    ):
+        bsz = v.size(0)
+
+        v = v.view(bsz, self.num_heads, self.head_dim, 1)
+        kv = kr * v
+        if "prev_key_value" in past_key_value:
+            prev_kv = past_key_value["prev_key_value"]
+            prev_scale = past_key_value["scale"]
+            scale = prev_scale * decay + 1
+            kv = prev_kv * (prev_scale.sqrt() * decay / scale.sqrt()).view(self.num_heads, 1,
+                                                                           1) + kv / scale.sqrt().view(self.num_heads,
+                                                                                                       1, 1)
+        else:
+            scale = torch.ones_like(decay)
+
+        past_key_value["prev_key_value"] = kv
+        past_key_value["scale"] = scale
+
+        output = torch.sum(qr * kv, dim=3)
+        return output, past_key_value
+
+    def parallel_forward(self, qr, kr, v, mask):
+        bsz, tgt_len, embed_dim = v.size()
+
+        vr = v.view(bsz, tgt_len, self.num_heads, self.expansion_dim // self.num_heads).transpose(1, 2)
+
+        qk_mat = qr @ kr.transpose(-1, -2)  # bsz * m * tgt_len * tgt_len
+        qk_mat = qk_mat * mask
+        # invariant after normalization
+        qk_mat = qk_mat / qk_mat.detach().abs().sum(dim=-1, keepdim=True).clamp(min=1, max=5e4)
+
+        output = torch.matmul(qk_mat, vr)
+        output = output.transpose(1, 2)
+        return output
+
+
+class DenseGauRetNetDecoderLayer(nn.Module):
+    def __init__(self, config: DenseGauRetNetConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = MultiScaleGauRetention(config=config)
+        self.input_layernorm = DenseGauRetNetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        if config.deepnorm:
+            self.alpha = math.pow(2.0 * config.num_hidden_layers, 0.25)
+        else:
+            self.alpha = 1.0
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            rel_pos=None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: Optional[bool] = False,
+            k_features: Optional[List] = [],
+            v_features: Optional[List] = [],
+            dense=False,
+            dense_layers=0,
+            forward_impl: str = 'parallel',
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, past_key_value, k_curr, v_curr = self.self_attn(
+
+            hidden_states=hidden_states,
+            rel_pos=rel_pos,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            k_features=k_features,
+            v_features=v_features,
+            dense=dense,
+            dense_layers=dense_layers,
+            forward_impl=forward_impl,
+
+        )
+        hidden_states = residual + hidden_states
+
+        return hidden_states, past_key_value, k_curr, v_curr
+
+
+DenseGauRetNet_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a Pytorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular Pytorch Module and refer to the Pytorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`DenseGauRetNetConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare DenseGauRetNet Model outputting raw hidden-states without any specific head on top.",
+    DenseGauRetNet_START_DOCSTRING,
+)
+class DenseGauRetNetPreTrainedModel(PreTrainedModel):
+    config_class = DenseGauRetNetConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DenseGauRetNetDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, DenseGauRetNetModel):
+            module.gradient_checkpointing = value
+
+
+DenseGauRetNet_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class RetNetRelPos(nn.Module):
+    def __init__(self, decoder_embed_dim, decoder_retention_heads, query_key_dim):
+        super().__init__()
+        angle = 1.0 / (10000 ** torch.linspace(0, 1, (query_key_dim // decoder_retention_heads) // 2))
+        angle = angle.unsqueeze(-1).repeat(1, 2).flatten()
+        decay = torch.log(1 - 2 ** (-5 - torch.arange(decoder_retention_heads, dtype=torch.float)))
+        self.register_buffer("angle", angle)
+        self.register_buffer("decay", decay)
+
+    def forward(self, slen, activate_recurrent=False):
+        if activate_recurrent:
+            sin = torch.sin(self.angle * (slen - 1))
+            cos = torch.cos(self.angle * (slen - 1))
+            retention_rel_pos = ((sin, cos), self.decay.exp())
+        else:
+            index = torch.arange(slen).to(self.decay)
+            sin = torch.sin(index[:, None] * self.angle[None, :])
+            cos = torch.cos(index[:, None] * self.angle[None, :])
+            mask = torch.tril(torch.ones(slen, slen).to(self.decay))
+            mask = torch.masked_fill(index[:, None] - index[None, :], ~mask.bool(), float("inf"))
+            mask = torch.exp(mask * self.decay[:, None, None])
+            mask = torch.nan_to_num(mask)
+            mask = mask / mask.sum(dim=-1, keepdim=True).sqrt()
+            retention_rel_pos = ((sin, cos), mask)
+
+        return retention_rel_pos
+
+
+@add_start_docstrings(
+    "The bare DenseGauRetNet Model outputting raw hidden-states without any specific head on top.",
+    DenseGauRetNet_START_DOCSTRING,
+)
+class DenseGauRetNetModel(DenseGauRetNetPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DenseGauRetNetDecoderLayer`]
+
+    Args:
+        config: DenseGauRetNetConfig
+    """
+
+    def __init__(self, config: DenseGauRetNetConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([DenseGauRetNetDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = DenseGauRetNetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+        self.retnet_rel_pos = RetNetRelPos(config.hidden_size, config.num_attention_heads,
+                                           config.query_key_dim)
+
+        if config.deepnorm:
+            init_scale = math.pow(8.0 * config.num_hidden_layers, 0.25)
+            for name, p in self.named_parameters():
+
+                if (
+                        "fc1" in name
+                        or "fc2" in name
+                        or "gate_proj" in name
+                        or "down_proj" in name
+                        or "up_proj" in name
+                        or "out_proj" in name
+                        or "v_proj" in name
+                        or "to_hidden" in name
+                        or "to_output" in name
+
+                ):
+                    p.data.div_(init_scale)
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def is_first_step(self, incremental_state):
+        if incremental_state is None:
+            return False
+        return incremental_state.get("is_first_step", False)
+
+    @add_start_docstrings_to_model_forward(DenseGauRetNet_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            forward_impl: Optional[str] = 'parallel',
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            sequence_offset=0,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layer
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = [] if use_cache else None
+
+        #
+        k_features = []
+        v_features = []
+        dense_layers = 0
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+            past_key_value = past_key_values[idx] if past_key_values is not None and len(
+                past_key_values) != 0 else {}
+
+            slen = input_ids.size(1)
+            if forward_impl == 'recurrent':
+                slen = sequence_offset
+            rel_pos = self.retnet_rel_pos(slen, forward_impl == 'recurrent',)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                hidden_states = layer_outputslayer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                dense = False
+                if idx >= 1:
+                    dense = True
+
+                layer_outputs, past_key_value, k_curr, v_curr = decoder_layer(
+                    hidden_states,
+                    rel_pos,
+                    forward_impl=forward_impl,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    k_features=k_features,
+                    v_features=v_features,
+                    dense=dense,
+                    dense_layers=dense_layers,
+                )
+                dense_layers += 1
+                k_features.append(k_curr)
+                v_features.append(v_curr)
+                if len(k_features) > self.config.dense_block_layers:
+                    k_features.pop(0)
+                if len(v_features) > self.config.dense_block_layers:
+                    v_features.pop(0)
+
+            hidden_states = layer_outputs  # used to be 3 ele,tmp  1
+
+
+            if use_cache:
+                next_decoder_cache.append(past_key_value)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class DenseGauRetNetForCausalLM(DenseGauRetNetPreTrainedModel):
+    _auto_class = "AutoModelForCausalLM"
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = DenseGauRetNetModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(DenseGauRetNet_INPUTS_DOCSTRING)
+    #@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[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,
+            forward_impl: str = 'parallel',
+            sequence_offset=0,
+
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            forward_impl=forward_impl,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            sequence_offset=sequence_offset,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+            self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
+
+    # infer mode
+
+    def sample_token(self, logit, do_sample=False, top_k=1, top_p=1.0, temperature=1.0):
+        if not do_sample:
+            return torch.argmax(logit, dim=-1, keepdim=True)
+        filtered = top_k_top_p_filtering(logit / temperature, top_k=top_k, top_p=top_p)
+        return torch.multinomial(torch.softmax(filtered, dim=-1), num_samples=1)
+
+    @torch.inference_mode()
+    def generate(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            parallel_compute_prompt=False,
+            generation_config: Optional[GenerationConfig] = None,
+            **kwargs,
+    ):
+        # breakpoint()
+        past_key_values = {}
+        for p_i in range(input_ids.shape[1] - 1):
+            outputs = self(input_ids[:, p_i:p_i + 1],
+                           forward_impl='recurrent',
+                           past_key_values=past_key_values,
+                           sequence_offset=p_i,
+                           return_dict=True,
+                           use_cache=True)
+            past_key_values = outputs.past_key_values
+        generated = input_ids[:, -1].unsqueeze(-1)  # [B, 1]
+        for i in range(generation_config.max_new_tokens):
+            outputs = self(generated[:,-1:],
+                           forward_impl='recurrent',
+                           past_key_values=past_key_values,
+                           use_cache=True,
+                           return_dict=True,
+                           sequence_offset=input_ids.shape[-1]+generated.shape[-1]-2  #1
+                           )
+            logit = outputs.logits[:, -1, :] # [batch_size, vocab_size]
+            past_key_values = outputs.past_key_values
+            token = self.sample_token(logit,
+                                      do_sample=generation_config.do_sample,
+                                      temperature=generation_config.temperature)
+
+            generated = torch.cat([generated, token], dim=-1)
+        return generated[:,1:]
+
+
+@add_start_docstrings(
+    """
+    The DenseGauRetNet Model transformer with a sequence classification head on top (linear layer).
+
+    [`DenseGauRetNetForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    DenseGauRetNet_START_DOCSTRING,
+)
+class DenseGauRetNetForSequenceClassification(DenseGauRetNetPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = DenseGauRetNetModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DenseGauRetNet_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[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,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78f8f3101653ca0e977ac9b9a2cbb1d3a0e04a6da567e88f1b0e3c811f1dfd59
+size 1493174903

special_tokens_map.json

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+{
+  "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": "[PAD]",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 2048,
+  "pad_token": null,
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}