diff --git a/server/text_generation_server/models/custom_modeling/gpt_bigcode_modeling.py b/server/text_generation_server/models/custom_modeling/gpt_bigcode_modeling.py
new file mode 100644
index 00000000..69eae1c4
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+++ b/server/text_generation_server/models/custom_modeling/gpt_bigcode_modeling.py
@@ -0,0 +1,1283 @@
+# coding=utf-8
+# Copyright 2023 The Bigcode team and HuggingFace Inc. team.
+# 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 GPTBigCode model."""
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+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
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+)
+from transformers.models.gpt_bigcode.configuration_gpt_bigcode import (
+    GPTBigCodeConfig,
+    InferenceRunnerType,
+)
+
+
+try:
+    from flash_attn.bert_padding import pad_input, unpad_input
+    from flash_attn.flash_attn_interface import flash_attn_unpadded_func
+except ImportError:
+    flash_attn_unpadded_func = None
+    pad_input = None
+    unpad_input = None
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "bigcode/gpt_bigcode-santacoder"
+_CONFIG_FOR_DOC = "GPTBigCodeConfig"
+
+GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "bigcode/gpt_bigcode-santacoder",
+    # See all GPTBigCode models at https://huggingface.co/models?filter=gpt_bigcode
+]
+
+
+def upcast_masked_softmax(
+    x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor, scale: float, softmax_dtype: torch.dtype
+):
+    input_dtype = x.dtype
+    x = x.to(softmax_dtype) * scale
+    x = torch.where(mask, x, mask_value)
+    x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
+    return x
+
+
+@torch.jit.script
+def upcast_masked_softmax_fused(
+    x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor, scale: float, softmax_dtype: torch.dtype
+):
+    return upcast_masked_softmax(x, mask, mask_value, scale, softmax_dtype)
+
+
+def upcast_softmax(x: torch.Tensor, scale: float, softmax_dtype: torch.dtype):
+    input_dtype = x.dtype
+    x = x.to(softmax_dtype) * scale
+    x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
+    return x
+
+
+@torch.jit.script
+def upcast_softmax_fused(x: torch.Tensor, scale: float, softmax_dtype: torch.dtype):
+    return upcast_softmax(x, scale, softmax_dtype)
+
+
+def masked_softmax(x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor):
+    x = torch.where(mask, x, mask_value)
+    x = torch.nn.functional.softmax(x, dim=-1)
+    return x
+
+
+@torch.jit.script
+def masked_softmax_fused(x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor):
+    return masked_softmax(x, mask, mask_value)
+
+
+def softmax_function(
+    x: torch.Tensor,
+    mask: torch.Tensor,
+    mask_value: torch.Tensor,
+    scale: float,
+    softmax_dtype: torch.dtype,
+    upcast: bool = True,
+    fused_softmax: Optional[bool] = None,
+):
+    """
+    This selects the appropriate (fused) (upcast) (masked) softmax method. Because of the way jit works, each case
+    needs to be handled through a separate method. The fused kernels remove most of the overhead from masking, casting
+    and scaling, but only work well when the key length is a multiple of 8. For other key lengths, it is extremely
+    inefficient. TODO: Could have better fused kernels depending on scaling, dropout and head mask.
+     Is it doable without writing 32 functions?
+    """
+    if fused_softmax is None:
+        fused_softmax = x.size(-1) % 8 == 0
+    if upcast:
+        if mask is None:
+            return (upcast_softmax_fused if fused_softmax else upcast_softmax)(x, scale, softmax_dtype)
+        else:
+            return (upcast_masked_softmax_fused if fused_softmax else upcast_masked_softmax)(
+                x, mask, mask_value, scale, softmax_dtype
+            )
+    else:
+        if mask is None:
+            return torch.nn.functional.softmax(x, dim=-1)
+        else:
+            return (masked_softmax_fused if fused_softmax else masked_softmax)(x, mask, mask_value)
+
+
+class GPTBigCodeAttention(nn.Module):
+    def __init__(self, config, is_cross_attention=False, layer_idx=None):
+        super().__init__()
+        self.mask_value = None
+
+        self.multi_query = config.multi_query
+        self.seq_dim = -2 if self.multi_query else -1
+        self.flash_attention = config.flash_attention
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        self.kv_heads = 1 if self.multi_query else self.num_heads
+        self.kv_dim = self.kv_heads * self.head_dim
+        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
+        self.is_cross_attention = is_cross_attention
+
+        self.layer_idx = layer_idx
+        self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
+        self.scale_attention_softmax_in_fp32 = (
+            config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32
+        )
+        self.fused_softmax = config.fused_softmax
+
+        # KV caching and padding
+        self.pre_allocate_kv_cache = (
+            config.n_embd if config.pre_allocate_kv_cache is True else config.pre_allocate_kv_cache
+        )
+        pad_key_length = config.pre_allocate_kv_cache if config.pad_key_length is None else config.pad_key_length
+        self._tuple_cache_format = self.pre_allocate_kv_cache or pad_key_length or self.flash_attention
+
+        if self.is_cross_attention:
+            raise NotImplementedError("Cross-attention is not supported for gpt_bigcode.")
+        self.c_attn = nn.Linear(self.embed_dim, self.embed_dim + 2 * self.kv_dim)
+
+        self.c_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+        self.attn_dropout = nn.Dropout(config.attn_pdrop)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+
+        if self.flash_attention:
+            if flash_attn_unpadded_func is None:
+                raise RuntimeError(
+                    "Flash Attention requires `flash_attn` and `einops`. "
+                    "To install, run `pip install flash-attn einops`."
+                )
+            if not self.multi_query:
+                # TODO: Flash Attention is implemented but not tested for MHA
+                raise ValueError("Flash Attention is not supported with multi-head attention.")
+
+    def _get_mask_value(self, device, dtype):
+        # torch.where expects a tensor. We use a cache to avoid recreating it every time.
+        if self.mask_value is None or self.mask_value.dtype != dtype or self.mask_value.device != device:
+            self.mask_value = torch.full([], torch.finfo(dtype).min, dtype=dtype, device=device)
+        return self.mask_value
+
+    def _attn(self, query, key, value, attention_mask, head_mask=None):
+        dtype = query.dtype
+        softmax_dtype = torch.float32 if self.attention_softmax_in_fp32 else dtype
+        upcast = dtype != softmax_dtype
+
+        unscale = self.layer_idx + 1 if self.scale_attention_softmax_in_fp32 and upcast else 1
+        scale_factor = unscale**-1
+        if self.scale_attn_weights:
+            scale_factor /= self.head_dim**0.5
+
+        # MQA models: (batch_size, query_length, num_heads * head_dim)
+        # MHA models: (batch_size, num_heads, query_length, head_dim)
+        query_shape = query.shape
+        batch_size = query_shape[0]
+        key_length = key.size(-2)
+
+        key = key.transpose(-1, -2)
+        if self.multi_query:
+            # (batch_size, query_length, num_heads, head_dim) x (batch_size, head_dim, key_length)
+            # -> (batch_size, query_length, num_heads, key_length)
+            query_length = query_shape[1]
+            attn_shape = (batch_size, query_length, self.num_heads, key_length)
+            attn_view = (batch_size, query_length * self.num_heads, key_length)
+            # No copy needed for MQA 2, or when layer_past is provided.
+            query = query.reshape(batch_size, query_length * self.num_heads, self.head_dim)
+        else:
+            # (batch_size, num_heads, query_length, head_dim) x (batch_size, num_heads, head_dim, key_length)
+            # -> (batch_size, num_heads, query_length, key_length)
+            query_length = query_shape[2]
+            attn_shape = (batch_size, self.num_heads, query_length, key_length)
+            attn_view = (batch_size * self.num_heads, query_length, key_length)
+            # Always copies
+            query = query.reshape(batch_size * self.num_heads, query_length, self.head_dim)
+            # No copy when layer_past is provided.
+            key = key.reshape(batch_size * self.num_heads, self.head_dim, key_length)
+
+        attn_weights = torch.empty(attn_view, device=query.device, dtype=query.dtype)
+        if query.device.type == "cpu":
+            # This is needed because of a bug in pytorch https://github.com/pytorch/pytorch/issues/80588.
+            # The bug was fixed in https://github.com/pytorch/pytorch/pull/96086,
+            # but the fix has not been released as of pytorch version 2.0.0.
+            attn_weights.zero_()
+            beta = 1
+        else:
+            beta = 0
+        attn_weights = torch.baddbmm(attn_weights, query, key, beta=beta, alpha=scale_factor).view(attn_shape)
+
+        attn_weights = softmax_function(
+            attn_weights,
+            attention_mask,
+            None if attention_mask is None else self._get_mask_value(attn_weights.device, softmax_dtype),
+            unscale,
+            softmax_dtype,
+            upcast,
+            self.fused_softmax,
+        )
+
+        attn_weights = self.attn_dropout(attn_weights)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            if self.multi_query:
+                head_mask = head_mask.transpose(1, 2)
+            attn_weights = attn_weights * head_mask
+
+        if self.multi_query:
+            attn_output = torch.bmm(attn_weights.view(attn_view), value).view(query_shape)
+        else:
+            attn_output = torch.matmul(attn_weights, value)
+
+        return attn_output, attn_weights
+
+    def _attn_flash(self, query, key, value, attention_mask, head_mask=None):
+        if head_mask is not None:
+            raise NotImplementedError("Head mask is not supported with flash attention.")
+
+        query_shape = query.shape
+        attn_shape = query_shape[0], self.num_heads, self.head_dim
+        query = query.view(attn_shape)
+        if self.multi_query:
+            key = key.unsqueeze(1).expand(attn_shape)
+            value = value.unsqueeze(1).expand(attn_shape)
+        else:
+            key = key.view(attn_shape)
+            value = value.view(attn_shape)
+
+        sequence_lengths, padding_index, _, max_sequence_length = attention_mask
+
+        # attn_output: (sum_seq_len, num_heads * head_dim)
+        attn_output = flash_attn_unpadded_func(
+            query,
+            key,
+            value,
+            sequence_lengths,
+            sequence_lengths,
+            max_sequence_length,
+            max_sequence_length,
+            self.dropout_p if self.training else 0.0,
+            softmax_scale=self.head_dim**-0.5 if self.scale_attn_weights else 1,
+            causal=True,
+        ).view(query_shape)
+
+        return attn_output, None
+
+    def _re_allocate_kv_cache(self, kv_cache, key_length, padded_key_length, allocate_key_length):
+        batch_size = kv_cache.size(-1)
+        assert not self.training
+        if self.multi_query:
+            allocated_kv_cache = torch.empty(
+                [batch_size, allocate_key_length, self.head_dim], dtype=kv_cache.dtype, device=kv_cache.device
+            )
+            allocated_kv_cache[:, :key_length].copy_(kv_cache)
+            padded_kv_cache = allocated_kv_cache[:, :padded_key_length]
+        else:
+            allocated_kv_cache = torch.empty(
+                [batch_size, self.num_heads, allocate_key_length, self.head_dim],
+                dtype=kv_cache.dtype,
+                device=kv_cache.device,
+            )
+            allocated_kv_cache[:, :, key_length].copy_(kv_cache)
+            padded_kv_cache = allocated_kv_cache[:, :, :padded_key_length]
+        return allocated_kv_cache, padded_kv_cache
+
+    def _merge_kv_caches(self, key_value, use_cache, layer_past, attention_mask):
+        flash_attention = self.flash_attention and layer_past is None
+
+        # Convert to standard KV cache format.
+        if flash_attention and use_cache:
+            _, padding_index, batch_size, max_sequence_length = attention_mask
+            current_kv_cache = pad_input(key_value, padding_index, batch_size, max_sequence_length)
+            if not self.multi_query:
+                current_kv_cache = current_kv_cache.view(
+                    batch_size, max_sequence_length, self.num_heads, 2 * self.head_dim
+                ).transpose(1, 2)
+        else:
+            current_kv_cache = key_value
+
+        # Calculate dimensions and recover layer_past
+        batch_size = current_kv_cache.size(0)
+        query_length = current_kv_cache.size(self.seq_dim)
+        if layer_past is None:
+            allocated_kv_cache, last_key_length = None, 0
+            last_kv_cache = None
+            key_length = query_length
+            allocated_key_length = key_length
+        else:
+            allocated_kv_cache, last_key_length = layer_past
+            last_kv_cache = (
+                allocated_kv_cache[:, :last_key_length]
+                if self.multi_query
+                else allocated_kv_cache[:, :, :last_key_length]
+            )
+            key_length = query_length + last_key_length
+            allocated_key_length = allocated_kv_cache.size(self.seq_dim)
+
+        padded_key_length = key_length if flash_attention else attention_mask.size(-1)
+        allocate_key_length = padded_key_length if use_cache else max(self.pre_allocate_kv_cache, padded_key_length)
+
+        # Re-allocate kv cache and copy last value
+        if allocate_key_length > allocated_key_length:
+            if self.multi_query:
+                allocated_kv_cache = torch.empty(
+                    [batch_size, allocate_key_length, 2 * self.head_dim],
+                    dtype=current_kv_cache.dtype,
+                    device=current_kv_cache.device,
+                )
+                if layer_past is not None:
+                    allocated_kv_cache[:, :last_key_length].copy_(last_kv_cache)
+                if allocate_key_length > key_length:
+                    # Nans in `value` can propagate through the matrix multiplication,
+                    # so we set the remaining values to zero. (`last_key_length:key_length` is set below.)
+                    allocated_kv_cache[:, key_length:, self.head_dim :].zero_()
+            else:
+                allocated_kv_cache = torch.empty(
+                    [batch_size, self.num_heads, allocate_key_length, 2 * self.head_dim],
+                    dtype=current_kv_cache.dtype,
+                    device=current_kv_cache.device,
+                )
+                if layer_past is not None:
+                    allocated_kv_cache[:, :, :last_key_length].copy_(last_kv_cache)
+                if allocate_key_length > key_length:
+                    allocated_kv_cache[:, :, key_length:, self.head_dim :].zero_()
+
+        # Copy the new values.
+        if allocate_key_length > allocated_key_length or layer_past is not None:
+            if self.multi_query:
+                allocated_kv_cache[:, last_key_length:key_length].copy_(current_kv_cache)
+                padded_kv_cache = allocated_kv_cache[:, :padded_key_length]
+            else:
+                allocated_kv_cache[:, :, last_key_length:key_length].copy_(current_kv_cache)
+                padded_kv_cache = allocated_kv_cache[:, :, :padded_key_length]
+            if not flash_attention:
+                # Use the merged KV cache.
+                # Not needed when layer_past is None but frees some memory.
+                key_value = padded_kv_cache
+
+        if use_cache:
+            if allocated_kv_cache is None:
+                allocated_kv_cache = current_kv_cache
+            present = allocated_kv_cache, key_length
+        else:
+            present = None
+        return key_value, present
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        layer_past: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+    ) -> Union[
+        Tuple[torch.Tensor, Optional[torch.Tensor]],
+        Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]],
+    ]:
+        flash_attention = self.flash_attention and layer_past is None
+        if self.multi_query or flash_attention:
+            query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=-1)
+        else:
+            # Note: We split as (self.num_heads, 3, self.head_dim) instead of (3, self.num_heads, self.head_dim),
+            # i.e., the memory layout is not the same as GPT2.
+            # This makes the concatenation with past_key_value more efficient.
+            query, key_value = (
+                self.c_attn(hidden_states)
+                .view(*hidden_states.shape[:2], self.num_heads, 3 * self.head_dim)
+                .transpose(1, 2)
+                .split((self.head_dim, 2 * self.head_dim), dim=3)
+            )
+
+        if self._tuple_cache_format:
+            # present =  (allocated_kv_cache, key_length)
+            key_value, present = self._merge_kv_caches(key_value, use_cache, layer_past, attention_mask)
+        else:
+            # present = key_value
+            if layer_past is not None:
+                key_value = torch.cat((layer_past, key_value), dim=-2)
+            present = key_value if use_cache else None
+
+        key, value = key_value.split((self.head_dim, self.head_dim), dim=-1)
+
+        attn_output, attn_weights = (self._attn_flash if flash_attention else self._attn)(
+            query, key, value, attention_mask, head_mask
+        )
+
+        if not self.multi_query:
+            attn_output = attn_output.transpose(1, 2).reshape(hidden_states.shape)
+        attn_output = self.c_proj(attn_output)
+        attn_output = self.resid_dropout(attn_output)
+
+        outputs = (attn_output, present)
+        if output_attentions:
+            if flash_attention:
+                raise ValueError("`output_attentions` is not supported with Flash Attention.")
+            if self.multi_query:
+                # Transpose to return weights in the usual format (batch_size, num_heads, query_length, key_length)
+                attn_weights = attn_weights.transpose(1, 2)
+            outputs += (attn_weights,)
+
+        return outputs  # a, present, (attentions)
+
+
+class GPTBigCodeMLP(nn.Module):
+    def __init__(self, intermediate_size, config):
+        super().__init__()
+        embed_dim = config.hidden_size
+        self.c_fc = nn.Linear(embed_dim, intermediate_size)
+        self.c_proj = nn.Linear(intermediate_size, embed_dim)
+        self.act = ACT2FN[config.activation_function]
+        self.dropout = nn.Dropout(config.resid_pdrop)
+
+    # Copied from transformers.models.gpt2.modeling_gpt2.GPT2MLP.forward
+    def forward(self, hidden_states: Optional[Tuple[torch.Tensor]]) -> torch.Tensor:
+        hidden_states = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.c_proj(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class GPTBigCodeBlock(nn.Module):
+    def __init__(self, config, layer_idx=None):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
+
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = GPTBigCodeAttention(config, layer_idx=layer_idx)
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+
+        if config.add_cross_attention:
+            raise NotImplementedError("Cross-attention is not supported for gpt_bigcode.")
+
+        self.mlp = GPTBigCodeMLP(self.inner_dim, config)
+
+    def forward(
+        self,
+        hidden_states: Optional[Tuple[torch.Tensor]],
+        layer_past: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+    ) -> Union[
+        Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+    ]:
+        if encoder_hidden_states is not None or encoder_attention_mask is not None:
+            raise NotImplementedError("Cross-attention is not supported for gpt_bigcode.")
+
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_outputs = self.attn(
+            hidden_states,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
+        outputs = attn_outputs[1:]
+        # residual connection
+        hidden_states = attn_output + residual
+
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        # residual connection
+        hidden_states = residual + feed_forward_hidden_states
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs
+        else:
+            outputs = (hidden_states,) + outputs[1:]
+
+        return outputs  # hidden_states, present, (attentions, cross_attentions)
+
+
+class GPTBigCodePreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = GPTBigCodeConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["GPTBigCodeBlock"]
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, (GPTBigCodeMLP, GPTBigCodeAttention)):
+            # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+            #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+            #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+            #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
+            #
+            # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+            module.c_proj.weight.data.normal_(
+                mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
+            )
+            module.c_proj._is_hf_initialized = True
+        elif isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    # Copied from transformers.models.gpt2.modeling_gpt2.GPT2PreTrainedModel._set_gradient_checkpointing with GPT2->GPTBigCode
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, GPTBigCodeModel):
+            module.gradient_checkpointing = value
+
+
+GPT_BIGCODE_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 ([`GPTBigCodeConfig`]): 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.
+"""
+
+GPT_BIGCODE_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.Tensor` of shape `(batch_size, input_ids_length)`):
+            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
+            `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
+            sequence tokens in the vocabulary.
+
+            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
+            `input_ids`.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        past_key_values (`Tuple[torch.Tensor]` of length `config.n_layers`):
+            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
+            `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
+            their past given to this model should not be passed as `input_ids` as they have already been computed.
+        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**.
+
+            If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for
+            `past_key_values`. In other words, the `attention_mask` always has to have the length:
+            `len(past_key_values) + len(input_ids)`
+
+            [What are attention masks?](../glossary#attention-mask)
+        token_type_ids (`torch.Tensor` of shape `(batch_size, input_ids_length)`, *optional*):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+            1]`:
+
+            - 0 corresponds to a *sentence A* token,
+            - 1 corresponds to a *sentence B* token.
+
+            [What are token type IDs?](../glossary#token-type-ids)
+        position_ids (`torch.Tensor` 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.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (`torch.Tensor` 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.
+
+            If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
+            `past_key_values`).
+        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.
+"""
+
+
+@add_start_docstrings(
+    "The bare GPT_BIGCODE Model transformer outputting raw hidden-states without any specific head on top.",
+    GPT_BIGCODE_START_DOCSTRING,
+)
+class GPTBigCodeModel(GPTBigCodePreTrainedModel):
+    _keys_to_ignore_on_load_missing = ["attn.masked_bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.multi_query = config.multi_query
+        self.embed_dim = config.hidden_size
+
+        if config.add_cross_attention:
+            raise NotImplementedError("Cross-attention is not supported for gpt_bigcode.")
+
+        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([GPTBigCodeBlock(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)
+
+        self.pad_key_length = config.pre_allocate_kv_cache if config.pad_key_length is None else config.pad_key_length
+        self._tuple_cache_format = config.pre_allocate_kv_cache or self.pad_key_length or config.flash_attention
+        self.inference_runner_type = InferenceRunnerType(config.inference_runner)
+
+        self.flash_attention = config.flash_attention
+
+        if self.inference_runner_type == InferenceRunnerType.NO_RUNNER:
+            self.inference_runner = None
+        else:
+            from .inference_runner import GPTBigCodeInferenceRunner
+
+            self.inference_runner = GPTBigCodeInferenceRunner(config, self)
+
+        max_positions = config.max_position_embeddings
+        # Causal mask
+        self.register_buffer(
+            "bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)), persistent=False
+        )
+
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+
+    def _get_causal_mask(self, padding_mask, query_length, key_length):
+        # Self-attention mask.
+        attention_mask = self.bias[None, key_length - query_length : key_length, :key_length]
+
+        if padding_mask is not None:
+            attention_mask = attention_mask * padding_mask.unsqueeze(1).to(
+                dtype=torch.bool, device=attention_mask.device
+            )
+
+        # MQA models: (batch_size, query_length, n_heads, key_length)
+        # MHA models: (batch_size, n_heads, query_length, key_length)
+        return attention_mask.unsqueeze(2 if self.multi_query else 1)
+
+    def _get_position_ids(self, position_ids, padding_mask, query_length, key_length, device):
+        if position_ids is not None:
+            position_ids = position_ids.to(device)
+        elif padding_mask is not None and padding_mask.ndim == 2:
+            # create position_ids on the fly for batch generation
+            position_ids = padding_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(padding_mask == 0, 1)
+            if key_length > query_length:
+                position_ids = position_ids[:, key_length - query_length : key_length :]
+        else:
+            position_ids = torch.arange(key_length - query_length, key_length, dtype=torch.long, device=device)
+        return position_ids.view(-1, query_length)
+
+    @add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPastAndCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Union[List[torch.Tensor], int]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
+        if self.inference_runner is not None and past_key_values is not None:
+            if self.config.validate_runner_input:
+                assert input_ids is not None
+                assert past_key_values is not None
+                assert attention_mask is not None
+                assert token_type_ids is None
+                assert position_ids is not None
+                assert head_mask is None
+                assert inputs_embeds is None
+                assert encoder_hidden_states is None
+                assert encoder_attention_mask is None
+                use_cache = use_cache if use_cache is not None else self.config.use_cache
+                assert use_cache is True
+                output_attentions = (
+                    output_attentions if output_attentions is not None else self.config.output_attentions
+                )
+                assert output_attentions is False
+                output_hidden_states = (
+                    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+                )
+                assert output_hidden_states is False
+                return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+                assert return_dict is True
+            return self.inference_runner.forward(input_ids, attention_mask, position_ids, past_key_values)
+
+        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 = self.config.use_cache if use_cache is None else use_cache
+        return_dict = self.config.use_return_dict if return_dict is None else return_dict
+
+        if input_ids is not None:
+            if inputs_embeds is not None:
+                raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+            input_shape = input_ids.shape
+            input_ids = input_ids.view(-1, input_shape[-1])
+            batch_size, query_length = input_ids.shape
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.shape[:-1]
+            inputs_embeds = inputs_embeds.view(-1, input_shape[-2:])
+            batch_size, query_length = inputs_embeds.shape[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if batch_size <= 0:
+            raise ValueError("batch_size has to be defined and > 0")
+
+        flash_attention = self.flash_attention and past_key_values is None
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = tuple([None] * len(self.h))
+        elif self._tuple_cache_format:
+            past_length = past_key_values[0][1]
+        else:
+            past_length = past_key_values[0].size(-2)
+        key_length = past_length + query_length
+
+        position_ids = self._get_position_ids(position_ids, attention_mask, query_length, key_length, input_ids.device)
+
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids.view(-1, query_length)
+
+        if not flash_attention:
+            # Self-attention mask (padding + causal).
+            attention_mask = self._get_causal_mask(attention_mask, query_length, key_length)
+            if self.pad_key_length:
+                pad = -key_length % 8
+                if pad > 0:
+                    attention_mask = torch.nn.functional.pad(attention_mask, (0, pad), mode="constant", value=False)
+
+        if encoder_hidden_states is not None or encoder_attention_mask is not None:
+            raise NotImplementedError("Cross-attention is not supported for gpt_bigcode.")
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # head_mask has shape n_layer x batch x n_heads x N x N
+        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = inputs_embeds + position_embeds
+
+        if token_type_ids is not None:
+            token_type_embeds = self.wte(token_type_ids)
+            hidden_states = hidden_states + token_type_embeds
+
+        hidden_states = self.drop(hidden_states)
+
+        # TODO: Unpad earlier (input ids), support unpadded input?
+        if flash_attention:
+            hidden_states, padding_index, sequence_lengths, max_sequence_length = unpad_input(
+                hidden_states, attention_mask
+            )
+            # Pass the required parameters through the attention_mask argument
+            attention_mask = (sequence_lengths, padding_index, batch_size, max_sequence_length)
+
+        presents = [] if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+        all_hidden_states = () if output_hidden_states else None
+        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, use_cache, output_attentions)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    None,
+                    attention_mask,
+                    head_mask[i],
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    attention_mask=attention_mask,
+                    head_mask=head_mask[i],
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = outputs[0]
+            if use_cache:
+                presents.append(outputs[1])
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
+
+        hidden_states = self.ln_f(hidden_states)
+
+        if flash_attention:
+            hidden_states = pad_input(hidden_states, padding_index, batch_size, query_length)
+
+        hidden_states = hidden_states.view(input_shape + (hidden_states.size(-1),))
+
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
+                if v is not None
+            )
+
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The GPT_BIGCODE Model transformer with a language modeling head on top (linear layer with weights tied to the input
+    embeddings).
+    """,
+    GPT_BIGCODE_START_DOCSTRING,
+)
+class GPTBigCodeForCausalLM(GPTBigCodePreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = GPTBigCodeModel(config)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.predict_last_token = config.predict_last_token
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -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(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": kwargs.get("position_ids", None),
+                "attention_mask": kwargs.get("attention_mask", None),
+                "token_type_ids": token_type_ids,
+            }
+        )
+        return model_inputs
+
+    @add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=CausalLMOutputWithCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
+        r"""
+        labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+
+        if self.predict_last_token and not self.training:
+            # We only care about the last token.
+            hidden_states = hidden_states[:, -1:]
+
+        lm_logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous().to(shift_logits.device)
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+            cross_attentions=transformer_outputs.cross_attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+    ) -> Tuple[Tuple[torch.Tensor]]:
+        """
+        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
+        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
+        beam_idx at every generation step.
+        """
+        return tuple(layer_past.index_select(0, beam_idx.to(layer_past.device)) for layer_past in past_key_values)
+
+
+@add_start_docstrings(
+    """
+    The GPTBigCode Model transformer with a sequence classification head on top (linear layer).
+
+    [`GPTBigCodeForSequenceClassification`] uses the last token in order to do the classification, as other causal
+    models (e.g. GPT-1) 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).
+    """,
+    GPT_BIGCODE_START_DOCSTRING,
+)
+class GPTBigCodeForSequenceClassification(GPTBigCodePreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = GPTBigCodeModel(config)
+        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = 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.Tensor` 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.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size, sequence_length = input_ids.shape[:2]
+        else:
+            batch_size, sequence_length = inputs_embeds.shape[:2]
+
+        assert (
+            self.config.pad_token_id is not None or batch_size == 1
+        ), "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
+                logger.warning(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+                )
+
+        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,
+        )
+
+
+@add_start_docstrings(
+    """
+    GPT_BIGCODE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
+    for Named-Entity-Recognition (NER) tasks.
+    """,
+    GPT_BIGCODE_START_DOCSTRING,
+)
+class GPTBigCodeForTokenClassification(GPTBigCodePreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.transformer = GPTBigCodeModel(config)
+        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
+            classifier_dropout = config.classifier_dropout
+        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = transformer_outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1).to(logits.device))
+
+        if not return_dict:
+            output = (logits,) + transformer_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )