add model

config.json

@@ -0,0 +1,37 @@
+{
+ "_name_or_path": "bigcode/santacoder",
+ "activation_function": "gelu_fast",
+ "architectures": [
+ "GPT2LMHeadCustomModel"
+ ],
+ "attention_head_type": "multiquery",
+ "attn_pdrop": 0.1,
+ "auto_map": {
+ "AutoConfig": "configuration_gpt2_mq.GPT2CustomConfig",
+ "AutoModelForCausalLM": "modeling_gpt2_mq.GPT2LMHeadCustomModel"
+ },
+ "bos_token_id": 50256,
+ "embd_pdrop": 0.1,
+ "eos_token_id": 50256,
+ "initializer_range": 0.02,
+ "layer_norm_epsilon": 1e-05,
+ "model_type": "gpt2",
+ "n_embd": 2048,
+ "n_head": 16,
+ "n_inner": 8192,
+ "n_layer": 24,
+ "n_positions": 2048,
+ "reorder_and_upcast_attn": false,
+ "resid_pdrop": 0.1,
+ "scale_attn_by_inverse_layer_idx": false,
+ "scale_attn_weights": true,
+ "summary_activation": null,
+ "summary_first_dropout": 0.1,
+ "summary_proj_to_labels": true,
+ "summary_type": "cls_index",
+ "summary_use_proj": true,
+ "torch_dtype": "float32",
+ "transformers_version": "4.26.0.dev0",
+ "use_cache": false,
+ "vocab_size": 49280
+}

configuration_gpt2_mq.py

@@ -0,0 +1,201 @@
+# coding=utf-8
+# Copyright 2018 The OpenAI Team Authors and Hugging Face Inc. team.
+# Copyright (c) 2018, 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.
+""" Custom GPT-2 configuration"""
+from collections import OrderedDict
+from typing import Any, List, Mapping, Optional
+from enum import Enum
+
+from transformers import PreTrainedTokenizer, TensorType, is_torch_available
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.onnx import OnnxConfigWithPast, PatchingSpec
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+ "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
+ "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
+ "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
+ "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
+ "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
+}
+
+MULTI_HEAD = "multihead"
+MULTI_QUERY = "multiquery"
+
+
+class GPT2CustomConfig(PretrainedConfig):
+ """
+ This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
+ instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the GPT-2
+ [gpt2](https://huggingface.co/gpt2) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50257):
+ Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`].
+ n_positions (`int`, *optional*, defaults to 1024):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ n_embd (`int`, *optional*, defaults to 768):
+ Dimensionality of the embeddings and hidden states.
+ n_layer (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ n_head (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ n_inner (`int`, *optional*, defaults to None):
+ Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
+ activation_function (`str`, *optional*, defaults to `"gelu"`):
+ Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
+ resid_pdrop (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ embd_pdrop (`int`, *optional*, defaults to 0.1):
+ The dropout ratio for the embeddings.
+ attn_pdrop (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention.
+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+ The epsilon to use in the layer normalization layers.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ summary_type (`string`, *optional*, defaults to `"cls_index"`):
+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
+ [`TFGPT2DoubleHeadsModel`].
+
+ Has to be one of the following options:
+
+ - `"last"`: Take the last token hidden state (like XLNet).
+ - `"first"`: Take the first token hidden state (like BERT).
+ - `"mean"`: Take the mean of all tokens hidden states.
+ - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
+ - `"attn"`: Not implemented now, use multi-head attention.
+ summary_use_proj (`bool`, *optional*, defaults to `True`):
+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
+ [`TFGPT2DoubleHeadsModel`].
+
+ Whether or not to add a projection after the vector extraction.
+ summary_activation (`str`, *optional*):
+ Argument used when doing sequence summary. Used in for the multiple choice head in
+ [`GPT2DoubleHeadsModel`].
+
+ Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
+ summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
+ [`TFGPT2DoubleHeadsModel`].
+
+ Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
+ summary_first_dropout (`float`, *optional*, defaults to 0.1):
+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
+ [`TFGPT2DoubleHeadsModel`].
+
+ The dropout ratio to be used after the projection and activation.
+ scale_attn_weights (`bool`, *optional*, defaults to `True`):
+ Scale attention weights by dividing by sqrt(head_dim)..
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
+ Whether to additionally scale attention weights by `1 / layer_idx + 1`.
+ reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
+ Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
+ dot-product/softmax to float() when training with mixed precision.
+
+ Example:
+
+ ```python
+ >>> from transformers import GPT2Config, GPT2Model
+
+ >>> # Initializing a GPT2 configuration
+ >>> configuration = GPT2Config()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = GPT2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "gpt2"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {
+ "hidden_size": "n_embd",
+ "max_position_embeddings": "n_positions",
+ "num_attention_heads": "n_head",
+ "num_hidden_layers": "n_layer",
+ }
+
+ def __init__(
+ self,
+ vocab_size=50257,
+ n_positions=1024,
+ n_embd=768,
+ n_layer=12,
+ n_head=12,
+ n_inner=None,
+ activation_function="gelu_new",
+ resid_pdrop=0.1,
+ embd_pdrop=0.1,
+ attn_pdrop=0.1,
+ layer_norm_epsilon=1e-5,
+ initializer_range=0.02,
+ summary_type="cls_index",
+ summary_use_proj=True,
+ summary_activation=None,
+ summary_proj_to_labels=True,
+ summary_first_dropout=0.1,
+ scale_attn_weights=True,
+ use_cache=True,
+ bos_token_id=50256,
+ eos_token_id=50256,
+ scale_attn_by_inverse_layer_idx=False,
+ reorder_and_upcast_attn=False,
+ attention_head_type=MULTI_HEAD,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.n_positions = n_positions
+ self.n_embd = n_embd
+ self.n_layer = n_layer
+ self.n_head = n_head
+ self.n_inner = n_inner
+ self.activation_function = activation_function
+ self.resid_pdrop = resid_pdrop
+ self.embd_pdrop = embd_pdrop
+ self.attn_pdrop = attn_pdrop
+ self.layer_norm_epsilon = layer_norm_epsilon
+ self.initializer_range = initializer_range
+ self.summary_type = summary_type
+ self.summary_use_proj = summary_use_proj
+ self.summary_activation = summary_activation
+ self.summary_first_dropout = summary_first_dropout
+ self.summary_proj_to_labels = summary_proj_to_labels
+ self.scale_attn_weights = scale_attn_weights
+ self.use_cache = use_cache
+ self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
+ self.reorder_and_upcast_attn = reorder_and_upcast_attn
+ self.attention_head_type = attention_head_type
+ # assert attention_head_type in [AttentionType.MULTI_HEAD, AttentionType.MULTI_QUERY]
+ assert attention_head_type in [MULTI_HEAD, MULTI_QUERY]
+
+ self.bos_token_id = bos_token_id
+ self.eos_token_id = eos_token_id
+
+ super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

modeling_gpt2_mq.py

@@ -0,0 +1,346 @@
+"""PyTorch OpenAI GPT-2 model modified with MultiQuery attention"""
+
+
+import math
+import os
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.cuda.amp import autocast
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPastAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ SequenceClassifierOutputWithPast,
+ TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel, SequenceSummary
+from transformers.pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
+
+from transformers.utils import (
+ ModelOutput,
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
+from transformers.models.gpt2.modeling_gpt2 import GPT2Model, GPT2Block, GPT2PreTrainedModel, GPT2LMHeadModel
+from .configuration_gpt2_mq import GPT2CustomConfig, MULTI_QUERY, MULTI_HEAD
+
+
+
+class GPT2MQAttention(nn.Module):
+ def __init__(self, config, is_cross_attention=False, layer_idx=None):
+ super().__init__()
+ assert config.attention_head_type == MULTI_QUERY
+
+ max_positions = config.max_position_embeddings
+ self.register_buffer(
+ "bias",
+ torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
+ 1, 1, max_positions, max_positions
+ ),
+ )
+ self.register_buffer("masked_bias", torch.tensor(-1e4))
+
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_heads
+ self.split_size = self.embed_dim
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {self.num_heads})."
+ )
+
+ self.scale_attn_weights = config.scale_attn_weights
+ if is_cross_attention:
+ raise NotImplementedError("Cross-attention not implemented for MQA")
+ self.is_cross_attention = is_cross_attention
+
+ # Layer-wise attention scaling, reordering, and upcasting
+ self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
+ self.layer_idx = layer_idx
+ self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
+
+ if self.is_cross_attention:
+ self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
+ self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
+ else:
+ # self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
+ self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
+ # Keys and values are shared across heads
+ self.kv_attn = Conv1D(2 * self.head_dim, self.embed_dim)
+ self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
+
+ self.attn_dropout = nn.Dropout(config.attn_pdrop)
+ self.resid_dropout = nn.Dropout(config.resid_pdrop)
+
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads)
+ index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
+
+ # Prune conv1d layers
+ self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
+ self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
+
+ # Update hyper params
+ self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads))
+ self.num_heads = self.num_heads - len(heads)
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def _attn(self, query, key, value, attention_mask=None, head_mask=None):
+ # query: (b, num_heads * sq, head_dim)
+ # key: (b, head_dim, sk)
+ # value: (b, sk, head_dim)
+ batch_size = query.size(0)
+ query_length = query.size(1) // self.num_heads
+ key_length = key.size(2)
+ # (b, num_heads * sq, head_dim) x (b, head_dim, sk) -> (b, num_heads * sq, sk)
+ attn_weights = torch.bmm(query, key)
+ # -> (b, num_heads, sq, sk)
+ attn_weights = attn_weights.view(batch_size, self.num_heads, query_length, key_length)
+
+ if self.scale_attn_weights:
+ attn_weights = attn_weights / torch.tensor(
+ value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
+ )
+
+ # Layer-wise attention scaling
+ if self.scale_attn_by_inverse_layer_idx:
+ attn_weights = attn_weights / float(self.layer_idx + 1)
+
+ if not self.is_cross_attention:
+ # if only "normal" attention layer implements causal mask
+ causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].to(torch.bool)
+ mask_value = torch.finfo(attn_weights.dtype).min
+ # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
+ # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
+ mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
+ attn_weights = torch.where(causal_mask, attn_weights, mask_value)
+
+ if attention_mask is not None:
+ # Apply the attention mask
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
+ attn_weights = attn_weights.type(value.dtype)
+ attn_weights = self.attn_dropout(attn_weights)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask
+
+ # (b, num_heads, sq, sk) -> (b, num_heads * sq, sk)
+ _attn_weights = attn_weights.view(batch_size, self.num_heads * query_length, key_length)
+ # (b, num_heads * sq, sk) x (b, sk, head_dim) -> (b, num_heads * sq, head_dim)
+ attn_output = torch.bmm(_attn_weights, value)
+ attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
+
+ return attn_output, attn_weights
+
+ def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None):
+ # Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
+ bsz, num_heads, q_seq_len, dk = query.size()
+ _, _, k_seq_len, _ = key.size()
+
+ # Preallocate attn_weights for `baddbmm`
+ attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)
+
+ # Compute Scale Factor
+ scale_factor = 1.0
+ if self.scale_attn_weights:
+ scale_factor /= float(value.size(-1)) ** 0.5
+
+ if self.scale_attn_by_inverse_layer_idx:
+ scale_factor /= float(self.layer_idx + 1)
+
+ # Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
+ with autocast(enabled=False):
+ q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len)
+ attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor)
+ attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
+
+ if not self.is_cross_attention:
+ # if only "normal" attention layer implements causal mask
+ query_length, key_length = query.size(-2), key.size(-2)
+ causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool()
+ mask_value = torch.finfo(attn_weights.dtype).min
+ # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
+ # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
+ mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
+ attn_weights = torch.where(causal_mask, attn_weights, mask_value)
+
+ if attention_mask is not None:
+ # Apply the attention mask
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
+ if attn_weights.dtype != torch.float32:
+ raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32")
+ attn_weights = attn_weights.type(value.dtype)
+ attn_weights = self.attn_dropout(attn_weights)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask
+
+ attn_output = torch.matmul(attn_weights, value)
+
+ return attn_output, attn_weights
+
+ def _split_heads(self, tensor, num_heads, attn_head_size):
+ """
+ Splits hidden_size dim into attn_head_size and num_heads
+ """
+ new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
+ tensor = tensor.view(new_shape)
+ return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
+
+ def _merge_heads(self, tensor, num_heads, attn_head_size):
+ """
+ Merges attn_head_size dim and num_attn_heads dim into hidden_size
+ """
+ tensor = tensor.permute(0, 2, 1, 3).contiguous()
+ new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
+ return tensor.view(new_shape)
+
+ def forward(
+ self,
+ hidden_states: Optional[Tuple[torch.FloatTensor]],
+ layer_past: Optional[Tuple[torch.Tensor]] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = False,
+ output_attentions: Optional[bool] = False,
+ ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
+ if encoder_hidden_states is not None:
+ raise NotImplementedError("Cross-attention not implemented for MQA")
+ if not hasattr(self, "q_attn"):
+ raise ValueError(
+ "If class is used as cross attention, the weights `q_attn` have to be defined. "
+ "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
+ )
+
+ query = self.q_attn(hidden_states)
+ key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
+ attention_mask = encoder_attention_mask
+ else:
+ query = self.q_attn(hidden_states)
+ key, value = self.kv_attn(hidden_states).split(self.head_dim, dim=2)
+
+
+ batch_size, seq_length = query.shape[:2]
+ # (query_length, batch, num_heads, head_dim)
+ # (batch, num_heads * query_length, head_dim)\
+
+ # (batch, query_length, hidden_size) -> (batch, num_heads, query_length, head_dim)
+ query = query.view(batch_size, seq_length, self.num_heads, self.head_dim).permute([0, 2, 1, 3])
+ # -> (batch, num_heads * query_length, head_dim)
+ query = query.reshape(batch_size, self.num_heads * seq_length, self.head_dim)
+
+ # (batch, query_length, hidden_size) -> (batch, query_length * num_heads, head_dim)
+ # query = query.view(
+ # batch_size, seq_length, self.num_heads, self.head_dim,
+ # ).reshape(
+ # batch_size, seq_length * self.num_heads, self.head_dim
+ # )
+ key = key.permute(0, 2, 1) # (batch_size, head_dim, seq_length)
+ # value (batch_size, seq_length, head_dim)
+
+ if layer_past is not None:
+ past_key, past_value = layer_past
+ # Concatenate on sequence dimension
+ key = torch.cat((past_key, key), dim=-1)
+ value = torch.cat((past_value, value), dim=-2)
+
+ if use_cache is True:
+ present = (key, value)
+ else:
+ present = None
+
+ if self.reorder_and_upcast_attn:
+ raise NotImplementedError("Reorder and upcast attention not implemented for MQA")
+ attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)
+ else:
+ attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
+
+ attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
+ attn_output = self.c_proj(attn_output)
+ attn_output = self.resid_dropout(attn_output)
+
+ outputs = (attn_output, present)
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs # a, present, (attentions)
+
+
+# inherit from gpt_modeling.py, and override `attn` module
+class GPT2CustomBlock(GPT2Block):
+
+ def __init__(self, config: GPT2CustomConfig, layer_idx=None):
+ super().__init__(config, layer_idx)
+ # Override attention module if using multiquery
+ if config.attention_head_type == MULTI_QUERY:
+ self.attn = GPT2MQAttention(config, layer_idx=layer_idx)
+ if config.add_cross_attention:
+ raise NotImplementedError("Cross-attention not implemented for MQA")
+
+
+# inherit from gpt_modeling.py and override `__init__` method
+class GPT2CustomModel(GPT2Model):
+ config_class = GPT2CustomConfig
+ 
+ def __init__(self, config):
+ GPT2PreTrainedModel.__init__(self, config)
+
+ self.embed_dim = config.hidden_size
+
+ self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
+ self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
+
+ self.drop = nn.Dropout(config.embd_pdrop)
+ self.h = nn.ModuleList([GPT2CustomBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
+ self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+
+class GPT2LMHeadCustomModel(GPT2LMHeadModel):
+ config_class = GPT2CustomConfig
+
+ def __init__(self, config):
+ GPT2PreTrainedModel.__init__(self, config)
+ self.transformer = GPT2CustomModel(config)
+ self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+
+ # Initialize weights and apply final processing
+ self.post_init()

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b979c0e0dd5dd83ba543576491cdd92197199ef001b5238c11165453fbb93778
+size 4600333857

special_tokens_map.json

@@ -0,0 +1,380 @@
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+<html class="">
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+</div>
+ </h1>
+ <div class="SVELTE_HYDRATER contents" data-props="{&quot;tagObjs&quot;:[{&quot;id&quot;:&quot;text-generation&quot;,&quot;label&quot;:&quot;Text Generation&quot;,&quot;subType&quot;:&quot;nlp&quot;,&quot;type&quot;:&quot;pipeline_tag&quot;},{&quot;id&quot;:&quot;pytorch&quot;,&quot;label&quot;:&quot;PyTorch&quot;,&quot;type&quot;:&quot;library&quot;},{&quot;id&quot;:&quot;transformers&quot;,&quot;label&quot;:&quot;Transformers&quot;,&quot;type&quot;:&quot;library&quot;},{&quot;id&quot;:&quot;dataset:bigcode/the-stack&quot;,&quot;label&quot;:&quot;bigcode/the-stack&quot;,&quot;type&quot;:&quot;dataset&quot;,&quot;disabled&quot;:false},{&quot;id&quot;:&quot;code&quot;,&quot;label&quot;:&quot;code&quot;,&quot;type&quot;:&quot;language&quot;},{&quot;id&quot;:&quot;arxiv:1911.02150&quot;,&quot;label&quot;:&quot;arxiv:1911.02150&quot;,&quot;type&quot;:&quot;arxiv&quot;},{&quot;id&quot;:&quot;arxiv:2207.14255&quot;,&quot;label&quot;:&quot;arxiv:2207.14255&quot;,&quot;type&quot;:&quot;arxiv&quot;},{&quot;id&quot;:&quot;arxiv:2301.03988&quot;,&quot;label&quot;:&quot;arxiv:2301.03988&quot;,&quot;type&quot;:&quot;arxiv&quot;},{&quot;id&quot;:&quot;gpt2&quot;,&quot;label&quot;:&quot;gpt2&quot;,&quot;type&quot;:&quot;other&quot;},{&quot;id&quot;:&quot;model-index&quot;,&quot;label&quot;:&quot;Eval Results&quot;,&quot;type&quot;:&quot;other&quot;},{&quot;id&quot;:&quot;has_space&quot;,&quot;label&quot;:&quot;Has a Space&quot;,&quot;type&quot;:&quot;other&quot;},{&quot;id&quot;:&quot;license:openrail&quot;,&quot;label&quot;:&quot;openrail&quot;,&quot;type&quot;:&quot;license&quot;}]}" data-target="ModelHeaderTags"><div class="flex flex-wrap mb-3 md:mb-4"><a class="tag 
+ tag-white" href="/models?pipeline_tag=text-generation"><div class="tag-ico tag-ico-indigo"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 18 18"><path d="M16.2607 8.08202L14.468 6.28928C14.3063 6.12804 14.0873 6.03749 13.859 6.03749C13.6307 6.03749 13.4117 6.12804 13.25 6.28928L5.6375 13.904V16.9125H8.64607L16.2607 9.30002C16.422 9.13836 16.5125 8.91935 16.5125 8.69102C16.5125 8.4627 16.422 8.24369 16.2607 8.08202V8.08202ZM8.1953 15.825H6.725V14.3547L11.858 9.22118L13.3288 10.6915L8.1953 15.825ZM14.0982 9.92262L12.6279 8.45232L13.8606 7.21964L15.3309 8.68994L14.0982 9.92262Z"></path><path d="M6.18125 9.84373H7.26875V6.03748H8.9V4.94998H4.55V6.03748H6.18125V9.84373Z"></path><path d="M4.55 11.475H2.375V2.775H11.075V4.95H12.1625V2.775C12.1625 2.48658 12.0479 2.20997 11.844 2.00602C11.64 1.80208 11.3634 1.6875 11.075 1.6875H2.375C2.08658 1.6875 1.80997 1.80208 1.60602 2.00602C1.40207 2.20997 1.2875 2.48658 1.2875 2.775V11.475C1.2875 11.7634 1.40207 12.04 1.60602 12.244C1.80997 12.4479 2.08658 12.5625 2.375 12.5625H4.55V11.475Z"></path></svg></div>
+ <span>Text Generation</span>
+ </a><a class="tag 
+ tag-white" href="/models?library=pytorch"><svg class="text-black inline-block ml-2 text-sm" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
+ <span>PyTorch</span>
+ </a><a class="tag 
+ tag-white" href="/models?library=transformers"><svg class="text-black inline-block ml-2 text-sm" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" preserveAspectRatio="xMidYMid meet" width="1em" height="1em" viewBox="0 0 90 90"><defs><mask id="a" x="31.46" y="42.5" width="26" height="25" maskUnits="userSpaceOnUse"><path d="M44.46,57.29c9.83,0,13-8.76,13-13.26,0-2.34-1.57-1.61-4.09-.36-2.33,1.15-5.46,2.74-8.91,2.74-7.18,0-13-6.88-13-2.38S34.63,57.29,44.46,57.29Z" fill="#fff"></path></mask></defs><path d="M44.71,77.5A34.75,34.75,0,1,0,10,42.75,34.75,34.75,0,0,0,44.71,77.5Z" fill="#ffd21e"></path><path d="M79.46,42.75A34.75,34.75,0,1,0,44.71,77.5,34.75,34.75,0,0,0,79.46,42.75ZM6,42.75A38.75,38.75,0,1,1,44.71,81.5,38.75,38.75,0,0,1,6,42.75Z" fill="#ff9d0b"></path><path d="M56,33.29c1.28.45,1.78,3.07,3.07,2.39a5,5,0,1,0-6.76-2.07C52.92,34.76,54.86,32.89,56,33.29Z" fill="#3a3b45"></path><path d="M32.45,33.29c-1.28.45-1.79,3.07-3.07,2.39a5,5,0,1,1,6.76-2.07C35.53,34.76,33.58,32.89,32.45,33.29Z" fill="#3a3b45"></path><path d="M44.46,57.29c9.83,0,13-8.76,13-13.26,0-2.34-1.57-1.61-4.09-.36-2.33,1.15-5.46,2.74-8.91,2.74-7.18,0-13-6.88-13-2.38S34.63,57.29,44.46,57.29Z" fill="#3a3b45"></path><g mask="url(#a)"><path d="M44.71,67.5a8.68,8.68,0,0,0,3-16.81l-.36-.13c-.84-.26-1.73,2.6-2.65,2.6S43,50.28,42.23,50.51a8.68,8.68,0,0,0,2.48,17Z" fill="#f94040"></path></g><path d="M68.21,38A3.25,3.25,0,1,0,65,34.75,3.25,3.25,0,0,0,68.21,38Z" fill="#ff9d0b"></path><path d="M21.71,38a3.25,3.25,0,1,0-3.25-3.25A3.25,3.25,0,0,0,21.71,38Z" fill="#ff9d0b"></path><path d="M15,49A5.22,5.22,0,0,0,11,50.87a5.92,5.92,0,0,0-1.33,3.75,7.28,7.28,0,0,0-1.94-.3A5.3,5.3,0,0,0,3.74,56a5.8,5.8,0,0,0-1.59,4.79A5.58,5.58,0,0,0,2.94,63a5.42,5.42,0,0,0-1.79,2.82,5.88,5.88,0,0,0,.79,4.74c-.08.12-.15.26-.22.39a5.2,5.2,0,0,0-.14,4.63c1,2.32,3.57,4.14,8.52,6.11,3.07,1.21,5.89,2,5.91,2a44.12,44.12,0,0,0,10.93,1.59c5.86,0,10.05-1.79,12.46-5.33C43.28,74.25,42.73,69,37.71,64a27.53,27.53,0,0,1-5-7.77c-.77-2.66-2.83-5.62-6.24-5.62h0a5.07,5.07,0,0,0-.86.07,5.68,5.68,0,0,0-3.73,2.38A13.22,13.22,0,0,0,19,50.28,7.41,7.41,0,0,0,15,49Zm0,4a3.6,3.6,0,0,1,1.82.65C19,55,23.09,62.08,24.6,64.83a2.43,2.43,0,0,0,2.14,1.31c1.55,0,2.75-1.53.15-3.48-3.92-2.93-2.55-7.72-.68-8a1,1,0,0,1,.24,0c1.7,0,2.45,2.93,2.45,2.93a31.55,31.55,0,0,0,6,9.29c3.77,3.77,4,6.8,1.22,10.84-1.88,2.75-5.47,3.58-9.16,3.58A41.51,41.51,0,0,1,17,79.81c-.11,0-13.45-3.8-11.76-7a1.39,1.39,0,0,1,1.34-.76c2.38,0,6.71,3.54,8.57,3.54A.77.77,0,0,0,16,75c.79-2.84-12.06-4-11-8.16a1.31,1.31,0,0,1,1.44-1c3.14,0,10.2,5.53,11.68,5.53a.28.28,0,0,0,.24-.11c.74-1.19.33-2-4.89-5.19S4.6,61,6.68,58.7a1.3,1.3,0,0,1,1-.38c3.17,0,10.66,6.82,10.66,6.82s2,2.1,3.25,2.1a.74.74,0,0,0,.68-.38c.86-1.46-8.06-8.22-8.56-11C13.37,54,14,53,15,53Z" fill="#ff9d0b"></path><path d="M36.1,77.69c2.75-4,2.55-7.07-1.22-10.84a31.55,31.55,0,0,1-6-9.29s-.82-3.21-2.69-2.91-3.24,5.08.68,8-.78,4.92-2.29,2.17S19,55,16.84,53.65s-3.63-.59-3.13,2.2,9.43,9.55,8.56,11-3.93-1.72-3.93-1.72S8.77,56.43,6.68,58.7,8.27,62.87,13.49,66s5.63,4,4.89,5.19S6.1,62.7,5,66.82,16.79,72.14,16,75,6.94,69.6,5.26,72.8s11.65,7,11.76,7C21.33,80.93,32.27,83.3,36.1,77.69Z" fill="#ffd21e"></path><path d="M74.9,49A5.21,5.21,0,0,1,79,50.87a5.92,5.92,0,0,1,1.33,3.75,7.35,7.35,0,0,1,2-.3A5.3,5.3,0,0,1,86.19,56a5.83,5.83,0,0,1,1.59,4.79A5.75,5.75,0,0,1,87,63a5.3,5.3,0,0,1,1.79,2.82A5.88,5.88,0,0,1,88,70.55c.08.12.16.26.23.39a5.26,5.26,0,0,1,.14,4.63c-1,2.32-3.58,4.14-8.52,6.11-3.08,1.21-5.89,2-5.92,2A44.12,44.12,0,0,1,63,85.27c-5.86,0-10-1.79-12.46-5.33C46.64,74.25,47.19,69,52.22,64a27.19,27.19,0,0,0,5-7.77c.78-2.66,2.83-5.62,6.24-5.62h0a4.91,4.91,0,0,1,.86.07,5.7,5.7,0,0,1,3.74,2.38,12.79,12.79,0,0,1,2.87-2.8A7.35,7.35,0,0,1,74.9,49Zm0,4a3.63,3.63,0,0,0-1.82.65C71,55,66.83,62.08,65.32,64.83a2.42,2.42,0,0,1-2.14,1.31c-1.54,0-2.75-1.53-.14-3.48,3.91-2.93,2.54-7.72.67-8a1,1,0,0,0-.24,0c-1.7,0-2.45,2.93-2.45,2.93a31.65,31.65,0,0,1-6,9.29c-3.78,3.77-4,6.8-1.22,10.84,1.87,2.75,5.47,3.58,9.15,3.58a41.61,41.61,0,0,0,9.93-1.46c.1,0,13.45-3.8,11.76-7a1.4,1.4,0,0,0-1.34-.76c-2.38,0-6.71,3.54-8.57,3.54a.76.76,0,0,1-.83-.61c-.8-2.84,12-4,11-8.16a1.31,1.31,0,0,0-1.44-1c-3.14,0-10.2,5.53-11.68,5.53a.25.25,0,0,1-.23-.11c-.74-1.19-.34-2,4.88-5.19S85.32,61,83.24,58.7a1.29,1.29,0,0,0-1-.38c-3.18,0-10.67,6.82-10.67,6.82s-2,2.1-3.24,2.1a.74.74,0,0,1-.68-.38c-.87-1.46,8-8.22,8.55-11C76.55,54,76,53,74.9,53Z" fill="#ff9d0b"></path><path d="M53.83,77.69c-2.75-4-2.56-7.07,1.22-10.84a31.65,31.65,0,0,0,6-9.29s.82-3.21,2.69-2.91,3.24,5.08-.67,8,.78,4.92,2.28,2.17S71,55,73.08,53.65s3.64-.59,3.13,2.2-9.42,9.55-8.55,11,3.92-1.72,3.92-1.72,9.58-8.71,11.66-6.44-1.58,4.17-6.8,7.33-5.63,4-4.89,5.19,12.27-8.52,13.35-4.4-11.76,5.32-11,8.16,9-5.38,10.74-2.18-11.65,7-11.76,7C68.6,80.93,57.65,83.3,53.83,77.69Z" fill="#ffd21e"></path></svg>
+ <span>Transformers</span>
+ </a><div class="relative inline-block mr-1 mb-1 md:mr-1.5 md:mb-1.5">
+ <button class="
+ " type="button">
+ 
+ <a class="tag mr-0 mb-0 md:mr-0 md:mb-0
+ tag-indigo" href="/models?dataset=dataset:bigcode/the-stack"><svg class="flex-none ml-2 -mr-1 opacity-40" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 25 25"><ellipse cx="12.5" cy="5" fill="currentColor" fill-opacity="0.25" rx="7.5" ry="2"></ellipse><path d="M12.5 15C16.6421 15 20 14.1046 20 13V20C20 21.1046 16.6421 22 12.5 22C8.35786 22 5 21.1046 5 20V13C5 14.1046 8.35786 15 12.5 15Z" fill="currentColor" opacity="0.5"></path><path d="M12.5 7C16.6421 7 20 6.10457 20 5V11.5C20 12.6046 16.6421 13.5 12.5 13.5C8.35786 13.5 5 12.6046 5 11.5V5C5 6.10457 8.35786 7 12.5 7Z" fill="currentColor" opacity="0.5"></path><path d="M5.23628 12C5.08204 12.1598 5 12.8273 5 13C5 14.1046 8.35786 15 12.5 15C16.6421 15 20 14.1046 20 13C20 12.8273 19.918 12.1598 19.7637 12C18.9311 12.8626 15.9947 13.5 12.5 13.5C9.0053 13.5 6.06886 12.8626 5.23628 12Z" fill="currentColor"></path></svg>
+ <span>bigcode/the-stack</span>
+ </a>
+ 
+ 
+ </button>
+ 
+ 
+ 
+ </div><a class="tag 
+ tag-green" href="/models?language=code"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="ml-2 text-green-600/80" preserveAspectRatio="xMidYMid meet" width="1em" height="1em" viewBox="0 0 10 10"><path fill-rule="evenodd" clip-rule="evenodd" d="M0.625 5C0.625 6.16032 1.08594 7.27312 1.90641 8.09359C2.72688 8.91406 3.83968 9.375 5 9.375C6.16032 9.375 7.27312 8.91406 8.09359 8.09359C8.91406 7.27312 9.375 6.16032 9.375 5C9.375 3.83968 8.91406 2.72688 8.09359 1.90641C7.27312 1.08594 6.16032 0.625 5 0.625C3.83968 0.625 2.72688 1.08594 1.90641 1.90641C1.08594 2.72688 0.625 3.83968 0.625 5ZM7.64365 7.48027C7.61734 7.50832 7.59054 7.53598 7.56326 7.56326C7.13828 7.98824 6.61864 8.2968 6.0539 8.46842C6.29802 8.11949 6.49498 7.64804 6.63475 7.09483C7.00845 7.18834 7.35014 7.3187 7.64365 7.48027ZM8.10076 6.87776C8.37677 6.42196 8.55005 5.90894 8.60556 5.37499H6.86808C6.85542 5.71597 6.82551 6.04557 6.77971 6.35841C7.25309 6.47355 7.68808 6.6414 8.062 6.85549C8.07497 6.86283 8.08789 6.87025 8.10076 6.87776ZM6.03795 6.22536C6.07708 5.95737 6.1044 5.67232 6.11705 5.37499H3.88295C3.89666 5.69742 3.92764 6.00542 3.9722 6.29287C4.37075 6.21726 4.79213 6.17749 5.224 6.17749C5.50054 6.17749 5.77294 6.19376 6.03795 6.22536ZM4.1261 7.02673C4.34894 7.84835 4.68681 8.375 5 8.375C5.32122 8.375 5.66839 7.82101 5.8908 6.963C5.67389 6.93928 5.45082 6.92699 5.224 6.92699C4.84316 6.92699 4.47332 6.96176 4.1261 7.02673ZM3.39783 7.21853C3.53498 7.71842 3.72038 8.14579 3.9461 8.46842C3.42141 8.30898 2.93566 8.03132 2.52857 7.65192C2.77253 7.48017 3.06711 7.33382 3.39783 7.21853ZM3.23916 6.48077C3.18263 6.13193 3.14625 5.76074 3.13192 5.37499H1.39444C1.4585 5.99112 1.67936 6.57938 2.03393 7.08403C2.3706 6.83531 2.78055 6.63162 3.23916 6.48077ZM1.39444 4.62499H3.13192C3.14615 4.24204 3.18211 3.87344 3.23794 3.52681C2.77814 3.37545 2.36731 3.17096 2.03024 2.92123C1.67783 3.42469 1.45828 4.011 1.39444 4.62499ZM2.5237 2.35262C2.76812 2.52552 3.06373 2.67281 3.39584 2.78875C3.53318 2.28573 3.71928 1.85578 3.9461 1.53158C3.41932 1.69166 2.93178 1.97089 2.5237 2.35262ZM3.97101 3.71489C3.92709 4.00012 3.89654 4.30547 3.88295 4.62499H6.11705C6.10453 4.33057 6.07761 4.04818 6.03909 3.78248C5.77372 3.81417 5.50093 3.83049 5.224 3.83049C4.79169 3.83049 4.3699 3.79065 3.97101 3.71489ZM5.8928 3.04476C5.67527 3.06863 5.45151 3.08099 5.224 3.08099C4.84241 3.08099 4.47186 3.04609 4.12405 2.98086C4.34686 2.1549 4.68584 1.625 5 1.625C5.32218 1.625 5.67048 2.18233 5.8928 3.04476ZM6.78083 3.6493C6.826 3.95984 6.85552 4.28682 6.86808 4.62499H8.60556C8.55029 4.09337 8.37827 3.58251 8.10436 3.1282C8.0903 3.1364 8.07618 3.14449 8.062 3.15249C7.68838 3.36641 7.25378 3.53417 6.78083 3.6493ZM7.64858 2.52499C7.35446 2.68754 7.0117 2.81868 6.63664 2.91268C6.49676 2.35623 6.29913 1.88209 6.0539 1.53158C6.61864 1.7032 7.13828 2.01176 7.56326 2.43674C7.59224 2.46572 7.62068 2.49514 7.64858 2.52499Z" fill="currentColor"></path></svg>
+ <span>code</span>
+ </a><div class="relative inline-block mr-1 mb-1 md:mr-1.5 md:mb-1.5">
+ <button class="
+ " type="button">
+ 
+ <a class="tag mr-0 mb-0 md:mr-0 md:mb-0
+ tag-purple" href="/models?other=arxiv:1911.02150">
+ <span>arxiv:1911.02150</span>
+ </a>
+ 
+ 
+ </button>
+ 
+ 
+ 
+ </div><div class="relative inline-block mr-1 mb-1 md:mr-1.5 md:mb-1.5">
+ <button class="
+ " type="button">
+ 
+ <a class="tag mr-0 mb-0 md:mr-0 md:mb-0
+ tag-purple" href="/models?other=arxiv:2207.14255">
+ <span>arxiv:2207.14255</span>
+ </a>
+ 
+ 
+ </button>
+ 
+ 
+ 
+ </div><div class="relative inline-block mr-1 mb-1 md:mr-1.5 md:mb-1.5">
+ <button class="
+ " type="button">
+ 
+ <a class="tag mr-0 mb-0 md:mr-0 md:mb-0
+ tag-purple" href="/models?other=arxiv:2301.03988">
+ <span>arxiv:2301.03988</span>
+ </a>
+ 
+ 
+ </button>
+ 
+ 
+ 
+ </div><a class="tag 
+ tag-purple" href="/models?other=gpt2">
+ <span>gpt2</span>
+ </a><a class="tag 
+ tag-purple" href="/models?other=model-index"><svg class="ml-2 text-orange-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M30 30h-8V4h8z" fill="currentColor"></path><path d="M20 30h-8V12h8z" fill="currentColor"></path><path d="M10 30H2V18h8z" fill="currentColor"></path></svg>
+ <span>Eval Results</span>
+ </a><div class="relative inline-block mr-1 mb-1 md:mr-1.5 md:mb-1.5 w-72">
+ <button class="
+ " type="button">
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+ </header>
+ <div class="SVELTE_HYDRATER contents" data-props="{&quot;commitLast&quot;:{&quot;date&quot;:&quot;2022-12-05T08:43:56.000Z&quot;,&quot;subject&quot;:&quot;Upload tokenizer&quot;,&quot;authors&quot;:[{&quot;_id&quot;:&quot;5e48005437cb5b49818287a5&quot;,&quot;avatar&quot;:&quot;https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1627890220261-5e48005437cb5b49818287a5.png?w=200&amp;h=200&amp;f=face&quot;,&quot;isHf&quot;:true,&quot;user&quot;:&quot;lvwerra&quot;}],&quot;commit&quot;:{&quot;id&quot;:&quot;ebb180e34c43d1a6a4004e7743fe6f422ee14f91&quot;,&quot;parentIds&quot;:[&quot;094f6ad6ef93b144cf51839083d85e36894d17b7&quot;]},&quot;title&quot;:&quot;Upload tokenizer&quot;},&quot;repo&quot;:{&quot;name&quot;:&quot;bigcode/santacoder&quot;,&quot;type&quot;:&quot;model&quot;}}" data-target="LastCommit"><div class="border border-b-0 dark:border-gray-800 px-3 py-2 flex items-baseline rounded-t-lg bg-gradient-to-t from-gray-100-to-white"><img class="w-4 h-4 rounded-full mt-0.5 mr-2.5 self-center" alt="lvwerra's picture" src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1627890220261-5e48005437cb5b49818287a5.png?w=200&amp;h=200&amp;f=face">
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+ </div>
+ </div>
+ <div class="mr-4 font-mono text-sm text-gray-500 truncate hover:prose-a:underline">Upload tokenizer</div>
+ <a class="text-sm border dark:border-gray-800 px-1.5 rounded bg-gray-50 dark:bg-gray-900 hover:underline" href="/bigcode/santacoder/commit/ebb180e34c43d1a6a4004e7743fe6f422ee14f91">ebb180e</a>
+ 
+ <time class="ml-auto hidden lg:block text-gray-500 dark:text-gray-400 truncate flex-none pl-2" datetime="2022-12-05T08:43:56" title="Mon, 05 Dec 2022 08:43:56 GMT">about 2 months ago</time></div></div>
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tokenizer.json

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+ <span>bigcode/the-stack</span>
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+ </div><a class="tag 
+ tag-green" href="/models?language=code"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="ml-2 text-green-600/80" preserveAspectRatio="xMidYMid meet" width="1em" height="1em" viewBox="0 0 10 10"><path fill-rule="evenodd" clip-rule="evenodd" d="M0.625 5C0.625 6.16032 1.08594 7.27312 1.90641 8.09359C2.72688 8.91406 3.83968 9.375 5 9.375C6.16032 9.375 7.27312 8.91406 8.09359 8.09359C8.91406 7.27312 9.375 6.16032 9.375 5C9.375 3.83968 8.91406 2.72688 8.09359 1.90641C7.27312 1.08594 6.16032 0.625 5 0.625C3.83968 0.625 2.72688 1.08594 1.90641 1.90641C1.08594 2.72688 0.625 3.83968 0.625 5ZM7.64365 7.48027C7.61734 7.50832 7.59054 7.53598 7.56326 7.56326C7.13828 7.98824 6.61864 8.2968 6.0539 8.46842C6.29802 8.11949 6.49498 7.64804 6.63475 7.09483C7.00845 7.18834 7.35014 7.3187 7.64365 7.48027ZM8.10076 6.87776C8.37677 6.42196 8.55005 5.90894 8.60556 5.37499H6.86808C6.85542 5.71597 6.82551 6.04557 6.77971 6.35841C7.25309 6.47355 7.68808 6.6414 8.062 6.85549C8.07497 6.86283 8.08789 6.87025 8.10076 6.87776ZM6.03795 6.22536C6.07708 5.95737 6.1044 5.67232 6.11705 5.37499H3.88295C3.89666 5.69742 3.92764 6.00542 3.9722 6.29287C4.37075 6.21726 4.79213 6.17749 5.224 6.17749C5.50054 6.17749 5.77294 6.19376 6.03795 6.22536ZM4.1261 7.02673C4.34894 7.84835 4.68681 8.375 5 8.375C5.32122 8.375 5.66839 7.82101 5.8908 6.963C5.67389 6.93928 5.45082 6.92699 5.224 6.92699C4.84316 6.92699 4.47332 6.96176 4.1261 7.02673ZM3.39783 7.21853C3.53498 7.71842 3.72038 8.14579 3.9461 8.46842C3.42141 8.30898 2.93566 8.03132 2.52857 7.65192C2.77253 7.48017 3.06711 7.33382 3.39783 7.21853ZM3.23916 6.48077C3.18263 6.13193 3.14625 5.76074 3.13192 5.37499H1.39444C1.4585 5.99112 1.67936 6.57938 2.03393 7.08403C2.3706 6.83531 2.78055 6.63162 3.23916 6.48077ZM1.39444 4.62499H3.13192C3.14615 4.24204 3.18211 3.87344 3.23794 3.52681C2.77814 3.37545 2.36731 3.17096 2.03024 2.92123C1.67783 3.42469 1.45828 4.011 1.39444 4.62499ZM2.5237 2.35262C2.76812 2.52552 3.06373 2.67281 3.39584 2.78875C3.53318 2.28573 3.71928 1.85578 3.9461 1.53158C3.41932 1.69166 2.93178 1.97089 2.5237 2.35262ZM3.97101 3.71489C3.92709 4.00012 3.89654 4.30547 3.88295 4.62499H6.11705C6.10453 4.33057 6.07761 4.04818 6.03909 3.78248C5.77372 3.81417 5.50093 3.83049 5.224 3.83049C4.79169 3.83049 4.3699 3.79065 3.97101 3.71489ZM5.8928 3.04476C5.67527 3.06863 5.45151 3.08099 5.224 3.08099C4.84241 3.08099 4.47186 3.04609 4.12405 2.98086C4.34686 2.1549 4.68584 1.625 5 1.625C5.32218 1.625 5.67048 2.18233 5.8928 3.04476ZM6.78083 3.6493C6.826 3.95984 6.85552 4.28682 6.86808 4.62499H8.60556C8.55029 4.09337 8.37827 3.58251 8.10436 3.1282C8.0903 3.1364 8.07618 3.14449 8.062 3.15249C7.68838 3.36641 7.25378 3.53417 6.78083 3.6493ZM7.64858 2.52499C7.35446 2.68754 7.0117 2.81868 6.63664 2.91268C6.49676 2.35623 6.29913 1.88209 6.0539 1.53158C6.61864 1.7032 7.13828 2.01176 7.56326 2.43674C7.59224 2.46572 7.62068 2.49514 7.64858 2.52499Z" fill="currentColor"></path></svg>
+ <span>code</span>
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+ tag-purple" href="/models?other=arxiv:1911.02150">
+ <span>arxiv:1911.02150</span>
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+ tag-purple" href="/models?other=arxiv:2207.14255">
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+ tag-purple" href="/models?other=arxiv:2301.03988">
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+ </div><a class="tag 
+ tag-purple" href="/models?other=gpt2">
+ <span>gpt2</span>
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+ tag-purple" href="/models?other=model-index"><svg class="ml-2 text-orange-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M30 30h-8V4h8z" fill="currentColor"></path><path d="M20 30h-8V12h8z" fill="currentColor"></path><path d="M10 30H2V18h8z" fill="currentColor"></path></svg>
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+ <span class="text-gray-400 !pr-0 -mr-1">License: </span>
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+ <div class="border-b border-gray-100"><div class="flex flex-col-reverse lg:flex-row lg:items-center lg:justify-between"><div class="flex items-center h-12 -mb-px overflow-x-auto overflow-y-hidden"><a class="tab-alternate " href="/bigcode/santacoder"><svg class="mr-1.5 text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg>
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+ Community
+ <div class="h-4 min-w-[1rem] px-1 rounded bg-black text-xs text-white shadow-sm items-center justify-center leading-none flex ml-1.5">13
+ </div>
+ 
+ </a>
+ </div>
+ 
+ <div class="SVELTE_HYDRATER contents" data-props="{&quot;model&quot;:{&quot;author&quot;:&quot;bigcode&quot;,&quot;cardData&quot;:{&quot;license&quot;:&quot;openrail&quot;,&quot;datasets&quot;:[&quot;bigcode/the-stack&quot;],&quot;language&quot;:[&quot;code&quot;],&quot;programming_language&quot;:[&quot;Java&quot;,&quot;JavaScript&quot;,&quot;Python&quot;],&quot;pipeline_tag&quot;:&quot;text-generation&quot;,&quot;inference&quot;:false,&quot;widget&quot;:[{&quot;text&quot;:&quot;def print_hello_world():&quot;,&quot;example_title&quot;:&quot;Hello world&quot;,&quot;group&quot;:&quot;Python&quot;}],&quot;model-index&quot;:[{&quot;name&quot;:&quot;SantaCoder&quot;,&quot;results&quot;:[{&quot;task&quot;:{&quot;type&quot;:&quot;text-generation&quot;},&quot;dataset&quot;:{&quot;type&quot;:&quot;nuprl/MultiPL-E&quot;,&quot;name&quot;:&quot;MultiPL HumanEval 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+ <div class="mr-4 font-mono text-sm text-gray-500 truncate hover:prose-a:underline">Upload tokenizer</div>
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+ 
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+ raw
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+ <div class="text-gray-400 flex items-center mr-4"><svg class="text-gray-300 text-sm mr-1.5 -translate-y-px" width="1em" height="1em" viewBox="0 0 22 28" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M15.3634 10.3639C15.8486 10.8491 15.8486 11.6357 15.3634 12.1209L10.9292 16.5551C10.6058 16.8785 10.0814 16.8785 9.7579 16.5551L7.03051 13.8277C6.54532 13.3425 6.54532 12.5558 7.03051 12.0707C7.51569 11.5855 8.30234 11.5855 8.78752 12.0707L9.7579 13.041C10.0814 13.3645 10.6058 13.3645 10.9292 13.041L13.6064 10.3639C14.0916 9.8787 14.8782 9.8787 15.3634 10.3639Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M10.6666 27.12C4.93329 25.28 0 19.2267 0 12.7867V6.52001C0 5.40001 0.693334 4.41334 1.73333 4.01334L9.73333 1.01334C10.3333 0.786673 11 0.786673 11.6 1.02667L19.6 4.02667C20.1083 4.21658 20.5465 4.55701 20.8562 5.00252C21.1659 5.44803 21.3324 5.97742 21.3333 6.52001V12.7867C21.3333 19.24 16.4 25.28 10.6666 27.12Z" fill="currentColor" fill-opacity="0.22"></path><path d="M10.0845 1.94967L10.0867 1.94881C10.4587 1.8083 10.8666 1.81036 11.2286 1.95515L11.2387 1.95919L11.2489 1.963L19.2489 4.963L19.25 4.96342C19.5677 5.08211 19.8416 5.29488 20.0351 5.57333C20.2285 5.85151 20.3326 6.18203 20.3333 6.52082C20.3333 6.52113 20.3333 6.52144 20.3333 6.52176L20.3333 12.7867C20.3333 18.6535 15.8922 24.2319 10.6666 26.0652C5.44153 24.2316 1 18.6409 1 12.7867V6.52001C1 5.82357 1.42893 5.20343 2.08883 4.94803L10.0845 1.94967Z" stroke="currentColor" stroke-opacity="0.30" stroke-width="2"></path></svg>
+
+ No virus
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+ <div class="dark:text-gray-300 sm:ml-auto">2.08 MB</div></div>
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tokenizer_config.json

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+ tag-green" href="/models?language=code"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="ml-2 text-green-600/80" preserveAspectRatio="xMidYMid meet" width="1em" height="1em" viewBox="0 0 10 10"><path fill-rule="evenodd" clip-rule="evenodd" d="M0.625 5C0.625 6.16032 1.08594 7.27312 1.90641 8.09359C2.72688 8.91406 3.83968 9.375 5 9.375C6.16032 9.375 7.27312 8.91406 8.09359 8.09359C8.91406 7.27312 9.375 6.16032 9.375 5C9.375 3.83968 8.91406 2.72688 8.09359 1.90641C7.27312 1.08594 6.16032 0.625 5 0.625C3.83968 0.625 2.72688 1.08594 1.90641 1.90641C1.08594 2.72688 0.625 3.83968 0.625 5ZM7.64365 7.48027C7.61734 7.50832 7.59054 7.53598 7.56326 7.56326C7.13828 7.98824 6.61864 8.2968 6.0539 8.46842C6.29802 8.11949 6.49498 7.64804 6.63475 7.09483C7.00845 7.18834 7.35014 7.3187 7.64365 7.48027ZM8.10076 6.87776C8.37677 6.42196 8.55005 5.90894 8.60556 5.37499H6.86808C6.85542 5.71597 6.82551 6.04557 6.77971 6.35841C7.25309 6.47355 7.68808 6.6414 8.062 6.85549C8.07497 6.86283 8.08789 6.87025 8.10076 6.87776ZM6.03795 6.22536C6.07708 5.95737 6.1044 5.67232 6.11705 5.37499H3.88295C3.89666 5.69742 3.92764 6.00542 3.9722 6.29287C4.37075 6.21726 4.79213 6.17749 5.224 6.17749C5.50054 6.17749 5.77294 6.19376 6.03795 6.22536ZM4.1261 7.02673C4.34894 7.84835 4.68681 8.375 5 8.375C5.32122 8.375 5.66839 7.82101 5.8908 6.963C5.67389 6.93928 5.45082 6.92699 5.224 6.92699C4.84316 6.92699 4.47332 6.96176 4.1261 7.02673ZM3.39783 7.21853C3.53498 7.71842 3.72038 8.14579 3.9461 8.46842C3.42141 8.30898 2.93566 8.03132 2.52857 7.65192C2.77253 7.48017 3.06711 7.33382 3.39783 7.21853ZM3.23916 6.48077C3.18263 6.13193 3.14625 5.76074 3.13192 5.37499H1.39444C1.4585 5.99112 1.67936 6.57938 2.03393 7.08403C2.3706 6.83531 2.78055 6.63162 3.23916 6.48077ZM1.39444 4.62499H3.13192C3.14615 4.24204 3.18211 3.87344 3.23794 3.52681C2.77814 3.37545 2.36731 3.17096 2.03024 2.92123C1.67783 3.42469 1.45828 4.011 1.39444 4.62499ZM2.5237 2.35262C2.76812 2.52552 3.06373 2.67281 3.39584 2.78875C3.53318 2.28573 3.71928 1.85578 3.9461 1.53158C3.41932 1.69166 2.93178 1.97089 2.5237 2.35262ZM3.97101 3.71489C3.92709 4.00012 3.89654 4.30547 3.88295 4.62499H6.11705C6.10453 4.33057 6.07761 4.04818 6.03909 3.78248C5.77372 3.81417 5.50093 3.83049 5.224 3.83049C4.79169 3.83049 4.3699 3.79065 3.97101 3.71489ZM5.8928 3.04476C5.67527 3.06863 5.45151 3.08099 5.224 3.08099C4.84241 3.08099 4.47186 3.04609 4.12405 2.98086C4.34686 2.1549 4.68584 1.625 5 1.625C5.32218 1.625 5.67048 2.18233 5.8928 3.04476ZM6.78083 3.6493C6.826 3.95984 6.85552 4.28682 6.86808 4.62499H8.60556C8.55029 4.09337 8.37827 3.58251 8.10436 3.1282C8.0903 3.1364 8.07618 3.14449 8.062 3.15249C7.68838 3.36641 7.25378 3.53417 6.78083 3.6493ZM7.64858 2.52499C7.35446 2.68754 7.0117 2.81868 6.63664 2.91268C6.49676 2.35623 6.29913 1.88209 6.0539 1.53158C6.61864 1.7032 7.13828 2.01176 7.56326 2.43674C7.59224 2.46572 7.62068 2.49514 7.64858 2.52499Z" fill="currentColor"></path></svg>
+ <span>code</span>
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+ tag-purple" href="/models?other=arxiv:1911.02150">
+ <span>arxiv:1911.02150</span>
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+ tag-purple" href="/models?other=arxiv:2207.14255">
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+ tag-purple" href="/models?other=arxiv:2301.03988">
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+ </div><a class="tag 
+ tag-purple" href="/models?other=gpt2">
+ <span>gpt2</span>
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+ tag-purple" href="/models?other=model-index"><svg class="ml-2 text-orange-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M30 30h-8V4h8z" fill="currentColor"></path><path d="M20 30h-8V12h8z" fill="currentColor"></path><path d="M10 30H2V18h8z" fill="currentColor"></path></svg>
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+ <span class="text-gray-400 !pr-0 -mr-1">License: </span>
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+ <div class="border-b border-gray-100"><div class="flex flex-col-reverse lg:flex-row lg:items-center lg:justify-between"><div class="flex items-center h-12 -mb-px overflow-x-auto overflow-y-hidden"><a class="tab-alternate " href="/bigcode/santacoder"><svg class="mr-1.5 text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg>
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+ Community
+ <div class="h-4 min-w-[1rem] px-1 rounded bg-black text-xs text-white shadow-sm items-center justify-center leading-none flex ml-1.5">13
+ </div>
+ 
+ </a>
+ </div>
+ 
+ <div class="SVELTE_HYDRATER contents" data-props="{&quot;model&quot;:{&quot;author&quot;:&quot;bigcode&quot;,&quot;cardData&quot;:{&quot;license&quot;:&quot;openrail&quot;,&quot;datasets&quot;:[&quot;bigcode/the-stack&quot;],&quot;language&quot;:[&quot;code&quot;],&quot;programming_language&quot;:[&quot;Java&quot;,&quot;JavaScript&quot;,&quot;Python&quot;],&quot;pipeline_tag&quot;:&quot;text-generation&quot;,&quot;inference&quot;:false,&quot;widget&quot;:[{&quot;text&quot;:&quot;def print_hello_world():&quot;,&quot;example_title&quot;:&quot;Hello world&quot;,&quot;group&quot;:&quot;Python&quot;}],&quot;model-index&quot;:[{&quot;name&quot;:&quot;SantaCoder&quot;,&quot;results&quot;:[{&quot;task&quot;:{&quot;type&quot;:&quot;text-generation&quot;},&quot;dataset&quot;:{&quot;type&quot;:&quot;nuprl/MultiPL-E&quot;,&quot;name&quot;:&quot;MultiPL HumanEval 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+ 
+ </div>
+ <div class="mr-4 font-mono text-sm text-gray-500 truncate hover:prose-a:underline">Add max length to tokenizer</div>
+ <a class="text-sm border dark:border-gray-800 px-1.5 rounded bg-gray-50 dark:bg-gray-900 hover:underline" href="/bigcode/santacoder/commit/aaeed5256bf8ce467234f9b8b85cfadbe9c76508">aaeed52</a>
+ 
+ <time class="ml-auto hidden lg:block text-gray-500 dark:text-gray-400 truncate flex-none pl-2" datetime="2023-01-06T21:26:53" title="Fri, 06 Jan 2023 21:26:53 GMT">18 days ago</time></div></div>
+ <div class="flex flex-wrap items-center px-3 py-1.5 border dark:border-gray-800 text-sm text-gray-800 dark:bg-gray-900">
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+ history
+ </a><a class="flex items-center hover:underline my-1 mr-4 " href="/bigcode/santacoder/blame/main/tokenizer_config.json"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M16 2a14 14 0 1 0 14 14A14 14 0 0 0 16 2zm0 26a12 12 0 1 1 12-12a12 12 0 0 1-12 12z" fill="currentColor"></path><path d="M11.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z" fill="currentColor"></path><path d="M20.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z" fill="currentColor"></path></svg>
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+ contribute
+ </a><a class="flex items-center hover:underline my-1 mr-4 " href="/bigcode/santacoder/delete/main/tokenizer_config.json"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M12 12h2v12h-2z" fill="currentColor"></path><path d="M18 12h2v12h-2z" fill="currentColor"></path><path d="M4 6v2h2v20a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2V8h2V6zm4 22V8h16v20z" fill="currentColor"></path><path d="M12 2h8v2h-8z" fill="currentColor"></path></svg>
+ delete
+ </a>
+ 
+ 
+ <div class="dark:text-gray-300 sm:ml-auto">363 Bytes</div></div>
+
+ <div class="border border-t-0 rounded-b-lg dark:bg-gray-925 dark:border-gray-800 leading-tight"><div class="py-3"><div class="SVELTE_HYDRATER contents" data-props="{&quot;lines&quot;:[&quot;{&quot;,&quot; &lt;span class=\\&quot;hljs-attr\\&quot;&gt;&amp;quot;name_or_path&amp;quot;&lt;/span&gt;: &lt;span class=\\&quot;hljs-string\\&quot;&gt;&amp;quot;bigcode/digit-bytelevel-bpe-jss-v1.1-49152&amp;quot;&lt;/span&gt;,&quot;,&quot; &lt;span class=\\&quot;hljs-attr\\&quot;&gt;&amp;quot;special_tokens_map_file&amp;quot;&lt;/span&gt;: &lt;span class=\\&quot;hljs-string\\&quot;&gt;&amp;quot;/Users/leandro/.cache/huggingface/hub/models--bigcode--digit-bytelevel-bpe-jss-v1.1-49152/snapshots/fa09b77949689a484afafc5f89534e6b6ba2c151/special_tokens_map.json&amp;quot;&lt;/span&gt;,&quot;,&quot; &lt;span class=\\&quot;hljs-attr\\&quot;&gt;&amp;quot;tokenizer_class&amp;quot;&lt;/span&gt;: &lt;span class=\\&quot;hljs-string\\&quot;&gt;&amp;quot;PreTrainedTokenizerFast&amp;quot;&lt;/span&gt;,&quot;,&quot; &lt;span class=\\&quot;hljs-attr\\&quot;&gt;&amp;quot;vocab_size&amp;quot;&lt;/span&gt;: &lt;span class=\\&quot;hljs-number\\&quot;&gt;49152&lt;/span&gt;,&quot;,&quot; &lt;span class=\\&quot;hljs-attr\\&quot;&gt;&amp;quot;model_max_length&amp;quot;&lt;/span&gt;: &lt;span class=\\&quot;hljs-number\\&quot;&gt;2048&lt;/span&gt;&quot;,&quot;}&quot;,&quot;&quot;]}" data-target="BlobContent">
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+<div class="relative text-sm"><div class="overflow-x-auto"><table class="border-collapse font-mono"><tbody><tr class="" id="L1">
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+ <td class="px-3 overflow-visible whitespace-pre"> <span class="hljs-attr">&quot;tokenizer_class&quot;</span>: <span class="hljs-string">&quot;PreTrainedTokenizerFast&quot;</span>,</td>
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+ <td class="text-right select-none pl-5 pr-3 cursor-pointer text-gray-300 hover:text-black blob-line-num" line-num="5"></td>
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