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README.md

@@ -1,3 +1,233 @@
 ---
+datasets:
+  - tiiuae/falcon-refinedweb
+language:
+  - en
+inference: true
+widget:
+  - text: "Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"
+    example_title: "Abu Dhabi Trip"
+  - text: "What's the Everett interpretation of quantum mechanics?"
+    example_title: "Q/A: Quantum & Answers"
+  - text: "Give me a list of the top 10 dive sites you would recommend around the world."
+    example_title: "Diving Top 10"
+  - text: "Can you tell me more about deep-water soloing?"
+    example_title: "Extreme sports"
+  - text: "Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?"
+    example_title: "Twitter Helper"
+  - text: "What are the responsabilities of a Chief Llama Officer?"
+    example_title: "Trendy Jobs"
 license: apache-2.0
 ---
+
+# ✨ Falcon-7B-Instruct
+
+**Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
+
+*Paper coming soon 😊.*
+
+🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
+
+## Why use Falcon-7B-Instruct?
+
+* **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).**
+* **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 
+
+💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). 
+
+🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother!
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model = "tiiuae/falcon-7b-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+sequences = pipeline(
+   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
+    max_length=200,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+
+```
+
+💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
+
+For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). 
+
+You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct.
+
+
+# Model Card for Falcon-7B-Instruct
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
+- **Model type:** Causal decoder-only;
+- **Language(s) (NLP):** English and French;
+- **License:** Apache 2.0;
+- **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
+
+### Model Source
+
+- **Paper:** *coming soon*.
+
+## Uses
+
+### Direct Use
+
+Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets.
+
+### Out-of-Scope Use
+
+Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. 
+
+## Bias, Risks, and Limitations
+
+Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
+
+### Recommendations
+
+We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use.
+
+## How to Get Started with the Model
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model = "tiiuae/falcon-7b-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+sequences = pipeline(
+   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
+    max_length=200,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+
+```
+
+## Training Details
+
+### Training Data
+
+Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets.
+
+| **Data source**    | **Fraction** | **Tokens** | **Description**                       |
+|--------------------|--------------|------------|-----------------------------------|
+| [Bai ze](https://github.com/project-baize/baize-chatbot) | 65%          | 164M     | chat                 |
+| [GPT4All](https://github.com/nomic-ai/gpt4all)              | 25%           | 62M       | instruct                                  |
+| [GPTeacher](https://github.com/teknium1/GPTeacher)      | 5%           | 11M        | instruct |
+| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5%          | 13M     | massive web crawl                 |
+
+
+The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
+
+
+## Evaluation
+
+*Paper coming soon.*
+
+See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
+
+Note that this model variant is not optimized for NLP benchmarks. 
+
+
+## Technical Specifications 
+
+For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
+
+### Model Architecture and Objective
+
+Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
+
+The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
+
+* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
+* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
+* **Decoder-block:** parallel attention/MLP with a single layer norm.
+
+| **Hyperparameter** | **Value** | **Comment**                            |
+|--------------------|-----------|----------------------------------------|
+| Layers             | 32        |                                        |
+| `d_model`          | 4544      | Increased to compensate for multiquery                                       |
+| `head_dim`         | 64        | Reduced to optimise for FlashAttention |
+| Vocabulary         | 65024     |                                        |
+| Sequence length    | 2048      |                                        |
+
+### Compute Infrastructure
+
+#### Hardware
+
+Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances. 
+
+#### Software
+
+Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
+
+
+## Citation
+
+*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite: 
+```
+@article{falcon40b,
+  title={{Falcon-40B}: an open large language model with state-of-the-art performance},
+  author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
+  year={2023}
+}
+```
+
+To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
+
+```
+@article{refinedweb,
+  title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
+  author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
+  journal={arXiv preprint arXiv:2306.01116},
+  eprint={2306.01116},
+  eprinttype = {arXiv},
+  url={https://arxiv.org/abs/2306.01116},
+  year={2023}
+}
+```
+
+
+## License
+
+Falcon-7B-Instruct is made available under the Apache 2.0 license.
+
+## Contact
+falconllm@tii.ae

config.json

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+{
+  "alibi": false,
+  "apply_residual_connection_post_layernorm": false,
+  "architectures": [
+    "FalconForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_falcon.FalconConfig",
+    "AutoModel": "modeling_falcon.FalconModel",
+    "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",
+    "AutoModelForQuestionAnswering": "modeling_falcon.FalconForQuestionAnswering",
+    "AutoModelForCausalLM": "modeling_falcon.FalconForCausalLM"
+  },
+  "bias": false,
+  "bos_token_id": 11,
+  "eos_token_id": 11,
+  "hidden_dropout": 0.0,
+  "hidden_size": 4544,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "falcon",
+  "multi_query": true,
+  "new_decoder_architecture": false,
+  "num_attention_heads": 71,
+  "num_hidden_layers": 32,
+  "parallel_attn": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.27.4",
+  "use_cache": true,
+  "vocab_size": 65024
+}

configuration_falcon.py

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+# coding=utf-8
+# Copyright 2023 the Falcon authors and HuggingFace Inc. team.  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.
+""" Falcon configuration"""
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
+    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
+}
+
+
+class FalconConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
+    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
+    [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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 65024):
+            Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`FalconModel`]
+        hidden_size (`int`, *optional*, defaults to 4544):
+            Dimension of the hidden representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 71):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether the model should return the last key/values attentions (not used by all models). Only relevant if
+            `config.is_decoder=True`.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+            The epsilon used by the layer normalization layers.
+        hidden_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for MLP layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for attention layers.
+        num_kv_heads (`int`, *optional*):
+            Number of key-value heads to use per attention layer. If unset, defaults to the same value as
+            `num_attention_heads`.
+        alibi (`bool`, *optional*, defaults to `False`):
+            Whether to use ALiBi positional biases during self-attention.
+        new_decoder_architecture (`bool`, *optional*, defaults to `False`):
+            Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
+            arguments are ignored, as the new decoder always uses parallel attention.
+        multi_query (`bool`, *optional*, defaults to `True`):
+            Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
+        parallel_attn (`bool`, *optional*, defaults to `True`):
+            Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
+            instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
+        bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias on Linear layers.
+        bos_token_id (`int`, *optional*, defaults to 11):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 11):
+            The id of the "end-of-sequence" token.
+
+    Example:
+
+    ```python
+    >>> from transformers import FalconModel, FalconConfig
+
+    >>> # Initializing a small (2-layer) Falcon configuration
+    >>> configuration = FalconConfig(num_hidden_layers=2)
+
+    >>> # Initializing a model from the small configuration
+    >>> model = FalconModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "falcon"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=65024,
+        hidden_size=4544,
+        num_hidden_layers=32,
+        num_attention_heads=71,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        num_kv_heads=None,
+        alibi=False,
+        new_decoder_architecture=False,
+        multi_query=True,
+        parallel_attn=True,
+        bias=False,
+        bos_token_id=11,
+        eos_token_id=11,
+        **kwargs,
+    ):
+        logger.warning_once(
+            "\nWARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. "
+            "For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model "
+            "without the trust_remote_code=True argument.\n"
+        )
+        self.vocab_size = vocab_size
+        # Backward compatibility with n_embed kwarg
+        n_embed = kwargs.pop("n_embed", None)
+        self.hidden_size = hidden_size if n_embed is None else n_embed
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads
+        self.alibi = alibi
+        self.new_decoder_architecture = new_decoder_architecture
+        self.multi_query = multi_query  # Ignored when new_decoder_architecture is True
+        self.parallel_attn = parallel_attn
+        self.bias = bias
+
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+    @property
+    def head_dim(self):
+        return self.hidden_size // self.num_attention_heads
+
+    @property
+    def rotary(self):
+        return not self.alibi

gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

handler.py

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+import torch
+
+from typing import Any, Dict
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and tokenizer from path
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True
+        )
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        # preprocess
+        inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(**inputs, **parameters)
+        else:
+            outputs = self.model.generate(**inputs)
+
+        # postprocess the prediction
+        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return [{"generated_text": prediction}]

modeling_falcon.py

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+# coding=utf-8
+# Copyright 2023 the Falcon authors and HuggingFace Inc. team.  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.
+"""PyTorch Falcon model."""
+
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
+from torch.nn import functional as F
+
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    QuestionAnsweringModelOutput,
+    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 .configuration_falcon import FalconConfig
+
+
+logger = logging.get_logger(__name__)
+
+FALCON_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "tiiuae/falcon-40b",
+    "tiiuae/falcon-40b-instruct",
+    "tiiuae/falcon-7b",
+    "tiiuae/falcon-7b-instruct",
+    "tiiuae/falcon-rw-7b",
+    "tiiuae/falcon-rw-1b",
+]
+_CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b"
+_CONFIG_FOR_DOC = "FalconConfig"
+
+
+# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
+# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
+class FalconLinear(nn.Linear):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        hidden_states = input @ self.weight.T
+        if self.bias is None:
+            return hidden_states
+        return hidden_states + self.bias
+
+
+# rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
+def rotate_half(x):
+    x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+class FalconRotaryEmbedding(nn.Module):
+    """Implementation of RotaryEmbedding from GPT-NeoX.
+    This implementation is designed to operate on queries and keys that are compatible with `[batch_size,
+    n_heads_per_partition, seq_len, head_dim]` (e.g. MinGPTAttention format).
+    """
+
+    def __init__(self, head_dim: int, base=10000):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.head_dim = head_dim
+        self.seq_len_cached = -1
+        self.cos_cached: torch.Tensor | None = None
+        self.sin_cached: torch.Tensor | None = None
+
+    def cos_sin(self, seq_len: int, past_key_values_length: int, device="cpu", dtype=torch.bfloat16) -> torch.Tensor:
+        total_length = seq_len + past_key_values_length
+        if total_length > self.seq_len_cached:
+            self.seq_len_cached = total_length
+            t = torch.arange(total_length, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1).to(device)
+
+            if dtype in [torch.float16, torch.bfloat16]:
+                emb = emb.float()
+
+            self.cos_cached = emb.cos()[None, :, :]
+            self.sin_cached = emb.sin()[None, :, :]
+
+            self.cos_cached = self.cos_cached.type(dtype)
+            self.sin_cached = self.sin_cached.type(dtype)
+
+        return (
+            self.cos_cached[:, past_key_values_length : seq_len + past_key_values_length],
+            self.sin_cached[:, past_key_values_length : seq_len + past_key_values_length],
+        )
+
+    def forward(self, query, key, past_key_values_length=0):
+        batch, seq_len, head_dim = query.shape
+        cos, sin = self.cos_sin(seq_len, past_key_values_length, query.device, query.dtype)
+        return (query * cos) + (rotate_half(query) * sin), (key * cos) + (rotate_half(key) * sin)
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
+) -> torch.BoolTensor:
+    """
+    Make causal mask used for self-attention. This mask does not take the existing attention mask into account - it
+    just blocks tokens from attending forwards in the sequence. The output shape will be `[batch_size, 1,
+    target_length, target_length+past_key_values_length]`.
+    """
+    batch_size, target_length = input_ids_shape
+
+    mask = torch.triu(torch.ones((target_length, target_length), dtype=torch.bool, device=device), diagonal=1)
+    # If past_key_values_length is 0 this is an empty tensor and the concatenation is a no-op.
+    # This code style is an unfortunate consequence of getting your TF engineer to port models; doing it this
+    # way avoids a data-dependent conditional, which will help me when I have to port this to XLA later.
+    past_mask = torch.zeros((target_length, past_key_values_length), dtype=torch.bool, device=device)
+    mask = torch.cat([past_mask, mask], dim=-1)
+    expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
+    return expanded_mask
+
+
+def _expand_mask(mask: torch.Tensor, past_key_values_length: int) -> torch.BoolTensor:
+    """
+    Expands attention_mask from `[batch_size, seq_length]` to `[batch_size, 1, seq_length, seq_length + past_length]`.
+    """
+    batch_size, total_length = mask.shape
+    seq_length = total_length - past_key_values_length if past_key_values_length is not None else total_length
+
+    expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
+    return expanded_mask.expand(batch_size, 1, seq_length, total_length)
+
+
+def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
+    batch_size, seq_length = attention_mask.shape
+    closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
+    base = torch.tensor(
+        2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
+    )
+    powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != num_heads:
+        extra_base = torch.tensor(
+            2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
+        )
+        num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
+        extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
+        slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
+
+    # Note: alibi will added to the attention bias that will be applied to the query, key product of attention
+    # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
+    # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
+    # => the query_length dimension will then be broadcasted correctly
+    # This is more or less identical to T5's relative position bias:
+    # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
+    arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
+    alibi = slopes[..., None].bfloat16() * arange_tensor
+    return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
+
+
+# Copied from transformers.models.bloom.modeling_bloom.dropout_add
+def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
+    """
+    Dropout add function
+
+    Args:
+        x (`torch.tensor`, *required*):
+            input tensor
+        residual (`torch.tensor`, *required*):
+            residual tensor
+        prob (`float`, *required*):
+            dropout probability
+        training (`bool`, *required*):
+            training mode
+    """
+    out = F.dropout(x, p=prob, training=training)
+    out = residual + out
+    return out
+
+
+class FalconAttention(nn.Module):
+    def __init__(self, config: FalconConfig):
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.split_size = self.hidden_size
+        self.hidden_dropout = config.hidden_dropout
+
+        if self.head_dim * self.num_heads != self.hidden_size:
+            raise ValueError(
+                f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
+                f" {self.num_heads})."
+            )
+
+        self.maybe_rotary = FalconRotaryEmbedding(config.head_dim) if config.rotary else lambda q, k, t: (q, k)
+
+        # Layer-wise attention scaling
+        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
+        self.beta = self.inv_norm_factor
+        if config.new_decoder_architecture:
+            qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim
+        elif config.multi_query:
+            qkv_out_dim = self.hidden_size + 2 * self.head_dim
+        else:
+            qkv_out_dim = 3 * self.hidden_size
+        self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias)
+        self.new_decoder_architecture = config.new_decoder_architecture
+        self.multi_query = config.multi_query
+        self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias)
+        self.attention_dropout = nn.Dropout(config.attention_dropout)
+        self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1
+
+    def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv`
+
+        Args:
+            fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
+
+        Returns:
+            query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
+            value: [batch_size, seq_length, num_heads, head_dim]
+        """
+        if self.new_decoder_architecture:
+            batch, seq_len, _ = fused_qkv.shape
+            qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim)
+            query = qkv[:, :, :, :-2]
+            key = qkv[:, :, :, [-2]]
+            value = qkv[:, :, :, [-1]]
+            key = torch.broadcast_to(key, query.shape)
+            value = torch.broadcast_to(value, query.shape)
+
+            query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
+            return query, key, value
+        elif not self.multi_query:
+            batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
+            fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
+            return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
+        else:
+            batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
+            fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
+            return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
+
+    # Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
+    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Merge heads together over the last dimenstion
+
+        Args:
+            x (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
+
+        Returns:
+            torch.tensor: [batch_size, seq_length, num_heads * head_dim]
+        """
+        # What we want to achieve is:
+        # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
+        batch_size_and_num_heads, seq_length, _ = x.shape
+        batch_size = batch_size_and_num_heads // self.num_heads
+
+        # First view to decompose the batch size
+        # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
+        x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
+
+        # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
+        x = x.permute(0, 2, 1, 3)
+
+        # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
+        return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        alibi: Optional[torch.Tensor],
+        attention_mask: torch.Tensor,
+        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        use_cache: bool = False,
+        output_attentions: bool = False,
+    ):
+        fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
+        num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
+        # 3 x [batch_size, seq_length, num_heads, head_dim]
+        (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
+
+        batch_size, query_length, _, _ = query_layer.shape
+
+        query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, query_length, self.head_dim)
+        key_layer = key_layer.transpose(1, 2).reshape(
+            batch_size * num_kv_heads,
+            query_length,
+            self.head_dim,
+        )
+        value_layer = value_layer.transpose(1, 2).reshape(batch_size * num_kv_heads, query_length, self.head_dim)
+
+        past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
+        query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            # concatenate along seq_length dimension:
+            #  - key: [batch_size * self.num_heads, kv_length, head_dim]
+            #  - value: [batch_size * self.num_heads, kv_length, head_dim]
+            key_layer = torch.cat((past_key, key_layer), dim=1)
+            value_layer = torch.cat((past_value, value_layer), dim=1)
+
+        _, kv_length, _ = key_layer.shape
+        if use_cache:
+            present = (key_layer, value_layer)
+        else:
+            present = None
+
+        attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
+
+        query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
+        key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
+        value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
+
+        if alibi is None:
+            if output_attentions:
+                # F.scaled_dot_product_attention doesn't return the attention weights, so we have
+                # to do it by hand if we want them
+                attention_scores = query_layer_ @ key_layer_.transpose(-1, -2)
+                attention_scores /= math.sqrt(self.head_dim)
+
+                attention_scores = F.softmax(
+                    attention_scores + attention_mask_float, dim=-1, dtype=hidden_states.dtype
+                )
+                attn_output = attention_scores @ value_layer_
+            else:
+                attn_output = F.scaled_dot_product_attention(
+                    query_layer_, key_layer_, value_layer_, attention_mask_float, 0.0, is_causal=False
+                )
+                attention_scores = None
+
+            attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
+            attn_output = attn_output.permute(0, 2, 1, 3)
+            attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
+
+            output_tensor = self.dense(attn_output)
+
+            if output_attentions:
+                return output_tensor, present, attention_scores
+            else:
+                return output_tensor, present
+
+        else:
+            matmul_result = query_layer_ @ key_layer_.transpose(-1, -2)
+
+            # change view to [batch_size, num_heads, q_length, kv_length]
+            attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)
+
+            # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
+            input_dtype = attention_scores.dtype
+            # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
+            if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
+                attention_scores = attention_scores.to(torch.float32)
+            # Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by
+            # adding (alibi * self.inv_norm_factor) to attention_mask_float. I think this would be mathematically
+            # equivalent and more performant, but there might be a numerical difference. If you're reading this
+            # and you'd like to experiment and maybe file a PR, feel free!
+            attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
+            attention_logits *= self.inv_norm_factor
+            attention_probs = F.softmax(attention_logits + attention_mask_float, dim=-1, dtype=hidden_states.dtype)
+            # [batch_size, num_heads, q_length, kv_length]
+            attention_probs = self.attention_dropout(attention_probs)
+
+            if head_mask is not None:
+                attention_probs = attention_probs * head_mask
+
+            # change view [batch_size, num_heads, q_length, kv_length]
+            attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length)
+
+            # matmul: [batch_size * num_heads, q_length, head_dim]
+            context_layer = (attention_probs_reshaped @ value_layer_).flatten(0, 1)
+
+            # change view [batch_size, num_heads, q_length, head_dim]
+            context_layer = self._merge_heads(context_layer)
+
+            output_tensor = self.dense(context_layer)
+
+            if output_attentions:
+                return output_tensor, present, attention_probs
+            else:
+                return output_tensor, present
+
+
+class FalconMLP(nn.Module):
+    def __init__(self, config: FalconConfig):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.dense_h_to_4h = FalconLinear(hidden_size, 4 * hidden_size, bias=config.bias)
+        self.act = nn.GELU()
+        self.dense_4h_to_h = FalconLinear(4 * hidden_size, hidden_size, bias=config.bias)
+        self.hidden_dropout = config.hidden_dropout
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.act(self.dense_h_to_4h(x))
+        x = self.dense_4h_to_h(x)
+        return x
+
+
+class FalconDecoderLayer(nn.Module):
+    def __init__(self, config: FalconConfig):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.self_attention = FalconAttention(config)
+        self.mlp = FalconMLP(config)
+        self.hidden_dropout = config.hidden_dropout
+        self.config = config
+
+        if config.new_decoder_architecture:
+            # The layer norm before self-attention
+            self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+            # The layer norm before the MLP
+            self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        else:
+            self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+            if not config.parallel_attn:
+                self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        alibi: Optional[torch.Tensor],
+        attention_mask: torch.Tensor,
+        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        use_cache: bool = False,
+        output_attentions: bool = False,
+    ):
+        residual = hidden_states
+
+        if self.config.new_decoder_architecture:
+            attention_layernorm_out = self.ln_attn(hidden_states)
+            mlp_layernorm_out = self.ln_mlp(hidden_states)
+        else:
+            attention_layernorm_out = self.input_layernorm(hidden_states)
+
+        # Self attention.
+        attn_outputs = self.self_attention(
+            attention_layernorm_out,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            alibi=alibi,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+
+        attention_output = attn_outputs[0]
+
+        if not self.config.new_decoder_architecture:
+            if self.config.parallel_attn:
+                mlp_layernorm_out = attention_layernorm_out
+            else:
+                residual = dropout_add(
+                    attention_output, residual, self.config.attention_dropout, training=self.training
+                )
+                mlp_layernorm_out = self.post_attention_layernorm(residual)
+
+        outputs = attn_outputs[1:]
+
+        # MLP.
+        mlp_output = self.mlp(mlp_layernorm_out)
+
+        if self.config.new_decoder_architecture or self.config.parallel_attn:
+            mlp_output += attention_output
+
+        output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
+
+        if use_cache:
+            outputs = (output,) + outputs
+        else:
+            outputs = (output,) + outputs[1:]
+
+        return outputs  # hidden_states, present, attentions
+
+
+FALCON_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 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 ([`FalconConfig`]): 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.
+"""
+
+FALCON_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` 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[Tuple[torch.Tensor]]` of length `config.num_hidden_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.
+
+            Each element of `past_key_values` is a tuple (past_key, past_value):
+            - past_key: [batch_size * num_heads, head_dim, kv_length]
+            - past_value: [batch_size * num_heads, kv_length, head_dim]
+        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        head_mask (`torch.FloatTensor` 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.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+
+            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 [`~file_utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class FalconPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = FalconConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["FalconDecoderLayer"]
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights."""
+        if isinstance(module, nn.Linear) or isinstance(module, FalconLinear):
+            # 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, LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    # Copied from transformers.models.bloom.modeling_bloom.BloomPreTrainedModel._set_gradient_checkpointing with BloomModel->FalconModel
+    def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
+        if isinstance(module, FalconModel):
+            module.gradient_checkpointing = value
+
+    @staticmethod
+    def _convert_cache_to_standard_format(
+        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
+    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
+        num_heads, ...]))
+        """
+        batch_size_times_num_heads, kv_length, head_dim = past_key_value[0][0].shape
+        # [batch_size * self.num_heads, kv_length, head_dim] -> [batch_size, num_heads, kv_length, head_dim]
+        # Note that don't want to use self.num_attention_heads because the number of heads may vary depending
+        # on whether we use multi_query attention.
+        num_heads = batch_size_times_num_heads // batch_size
+        return tuple(
+            (
+                layer_past[0].view(batch_size, num_heads, kv_length, head_dim),
+                layer_past[1].view(batch_size, num_heads, kv_length, head_dim),
+            )
+            for layer_past in past_key_value
+        )
+
+    @staticmethod
+    def _convert_to_rw_cache(
+        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
+    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
+        batch_size, num_heads, kv_length, head_dim = past_key_value[0][0].shape
+        batch_size_times_num_heads = batch_size * num_heads
+        # [batch_size, num_heads, kv_length, head_dim] -> [batch_size * num_heads, kv_length, head_dim]
+        return tuple(
+            (
+                layer_past[0].view(batch_size_times_num_heads, kv_length, head_dim),
+                layer_past[1].view(batch_size_times_num_heads, kv_length, head_dim),
+            )
+            for layer_past in past_key_value
+        )
+
+
+@add_start_docstrings(
+    "The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.",
+    FALCON_START_DOCSTRING,
+)
+class FalconModel(FalconPreTrainedModel):
+    def __init__(self, config: FalconConfig):
+        super().__init__(config)
+
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_alibi = config.alibi
+
+        # Embedding + LN Embedding
+        self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
+
+        # Transformer blocks
+        self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+        # Final Layer Norm
+        self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.word_embeddings
+
+    @staticmethod
+    def _prepare_attn_mask(
+        attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
+    ) -> torch.BoolTensor:
+        # Create a causal mask
+        # The attention mask we receive as input should cover the whole extended sequence, including any past
+        # cache, so its shape should be [batch_size, seq_length + past_key_values_length]
+        # The output shape will be [batch_size, 1, seq_length, seq_length + past_key_values_length]
+        if input_shape[1] + past_key_values_length != attention_mask.shape[1]:
+            raise ValueError(
+                "Attention mask shape should be (batch_size, seq_length + past_key_values_length)"
+                f" but is {attention_mask.shape} with input_ids shape {input_shape} and past length"
+                f" {past_key_values_length}."
+            )
+        combined_attention_mask = None
+        device = attention_mask.device
+        _, seq_length = input_shape
+
+        if seq_length > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape, device=device, past_key_values_length=past_key_values_length
+            )
+
+        # [batch_size, seq_length + past_key_values_length] -> [batch_size, 1, seq_length, seq_length + past_key_values_length]
+        expanded_attn_mask = _expand_mask(attention_mask, past_key_values_length=past_key_values_length)
+        combined_attention_mask = (
+            expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
+        )
+
+        return combined_attention_mask
+
+    def set_input_embeddings(self, new_embeddings: torch.Tensor):
+        self.word_embeddings = new_embeddings
+
+    @add_start_docstrings_to_model_forward(FALCON_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.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if past_key_values is None:
+            past_key_values = tuple([None] * len(self.h))
+        else:
+            past_key_values = self._convert_to_rw_cache(past_key_values)
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape batch_size x num_heads x N x N
+        # head_mask has shape n_layer x batch x num_heads x N x N
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        hidden_states = inputs_embeds
+
+        presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+
+        # Compute alibi tensor: check build_alibi_tensor documentation
+        past_key_values_length = 0
+        if past_key_values[0] is not None:
+            past_key_values_length = past_key_values[0][0].shape[1]  # 1 because RW-cache, not standard format
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=hidden_states.device)
+        else:
+            attention_mask = attention_mask.to(hidden_states.device)
+
+        if self.use_alibi:
+            alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
+        else:
+            alibi = None
+
+        causal_mask = self._prepare_attn_mask(
+            attention_mask,
+            input_shape=(batch_size, seq_length),
+            past_key_values_length=past_key_values_length,
+        )
+
+        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:
+                if use_cache:
+                    logger.warning(
+                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    alibi,
+                    causal_mask,
+                    head_mask[i],
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    attention_mask=causal_mask,
+                    head_mask=head_mask[i],
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                    alibi=alibi,
+                )
+
+            hidden_states = outputs[0]
+            if use_cache is True:
+                presents = presents + (outputs[1],)
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
+
+        # Add last hidden state
+        hidden_states = self.ln_f(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if presents is not None:
+            presents = self._convert_cache_to_standard_format(presents, batch_size)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_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,
+        )
+
+
+@add_start_docstrings(
+    "The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).",
+    FALCON_START_DOCSTRING,
+)
+class FalconForCausalLM(FalconPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: FalconConfig):
+        super().__init__(config)
+        self.transformer = FalconModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings: torch.Tensor):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> dict:
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache"),
+            "attention_mask": attention_mask,
+        }
+
+    @add_start_docstrings_to_model_forward(FALCON_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.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: 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[torch.Tensor], CausalLMOutputWithCrossAttentions]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            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]
+
+        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()
+            batch_size, seq_length, vocab_size = shift_logits.shape
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
+            )
+
+        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,
+        )
+
+    def _reorder_cache(
+        self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
+    ) -> Tuple[Tuple[torch.Tensor, 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.
+
+        Output shares the same memory storage as `past`.
+        """
+
+        # Get a copy of `beam_idx` on all the devices where we need those indices.
+        device_to_beam_idx = {
+            past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
+        }
+        reordered_past = tuple(
+            (
+                layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
+                layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
+            )
+            for layer_past in past
+        )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Falcon Model transformer with a sequence classification head on top (linear layer).
+
+    [`FalconForSequenceClassification`] 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).
+    """,
+    FALCON_START_DOCSTRING,
+)
+class FalconForSequenceClassification(FalconPreTrainedModel):
+    def __init__(self, config: FalconConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = FalconModel(config)
+        self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: 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[torch.Tensor], SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            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 = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
+            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:
+            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, labels)
+            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(
+    """
+    Falcon 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.
+    """,
+    FALCON_START_DOCSTRING,
+)
+class FalconForTokenClassification(FalconPreTrainedModel):
+    def __init__(self, config: FalconConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.transformer = FalconModel(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) 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(FALCON_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: 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[torch.Tensor], TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            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:
+            batch_size, seq_length = labels.shape
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
+            )
+
+        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,
+        )
+
+
+@add_start_docstrings(
+    """
+    The Falcon Model transformer with a span classification head on top for extractive question-answering tasks like
+    SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    FALCON_START_DOCSTRING,
+)
+class FalconForQuestionAnswering(FalconPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = FalconModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

pytorch_model.bin.index.json

@@ -0,0 +1,203 @@
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+  }
+}

special_tokens_map.json

@@ -0,0 +1,16 @@
+{
+  "additional_special_tokens": [
+    ">>TITLE<<",
+    ">>ABSTRACT<<",
+    ">>INTRODUCTION<<",
+    ">>SUMMARY<<",
+    ">>COMMENT<<",
+    ">>ANSWER<<",
+    ">>QUESTION<<",
+    ">>DOMAIN<<",
+    ">>PREFIX<<",
+    ">>SUFFIX<<",
+    ">>MIDDLE<<"
+  ],
+  "eos_token": "<|endoftext|>"
+}

tokenizer_config.json

@@ -0,0 +1,12 @@
+{
+  "add_prefix_space": false,
+  "eos_token": "<|endoftext|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 2048,
+  "name_or_path": "tiiuae/falcon_tokenizer",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}