LLaMA Implementation (huggingface#21955)

* LLaMA

* sharding and docs

* tweak

* black

* inits

* ruff

* LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP

* init

* no checkpoint

* docs

* ruff

* type_vocab_size

* tokenizer fixes

* tokenizer fixes

* Update tokenization_llama.py

* Update tokenization_llama.py

* Update configuration_llama.py

* Update modeling_llama.py

* tokenizer add_bos by default

* licenses

* remove decoder

* norms and mlp

* rope overhaul

* tweaks

* black

* mention OPT implementation

* off-by-one naming

* typo

* fix

* tokenization fix and slicing bug

* padding config

* cleanup

* black

* update tests

* undo typo

* fix vocab caching logic

* ruff

* docbuilder

* attn fix from BlackSamorez

* initial feedback

* typo

* docs

* llama case

* llama case

* load checkpoint docs

* comment about tokenizer

* tokenizer defaults

* clear past_key_values if use_cache=False

* last tweaks

* last tweaks

* last tweaks

* last tweaks

---------

Co-authored-by: Stella Biderman <[email protected]>

Author: 2 people

README.md

@@ -322,6 +322,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
 1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
+1. **[LLaMA](https://huggingface.co/docs/transformers/main/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.

README_ko.md

@@ -272,6 +272,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
 1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
+1. **[LLaMA](https://huggingface.co/docs/transformers/main/model_doc/llama)** (The FAIR team of Meta AI 에서 제공)은 Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.의 [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)논문과 함께 발표했습니다.
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.

README_zh-hans.md

@@ -296,6 +296,7 @@ conda install -c huggingface transformers
 1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (来自 Microsoft Research Asia) 伴随论文 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) 由 Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei 发布。
 1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
 1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (来自 Meta AI) 伴随论文 [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) 由 Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze 发布。
+1. **[LLaMA](https://huggingface.co/docs/transformers/main/model_doc/llama)** (来自 The FAIR team of Meta AI) 伴随论文 [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) 由 Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample 发布。
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (来自 Google AI) released 伴随论文 [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) 由 Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang 发布。
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (来自 Studio Ousia) 伴随论文 [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) 由 Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto 发布。

README_zh-hant.md

@@ -308,6 +308,7 @@ conda install -c huggingface transformers
 1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
 1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
+1. **[LLaMA](https://huggingface.co/docs/transformers/main/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.

docs/source/en/_toctree.yml

@@ -275,6 +275,8 @@
         title: LayoutLM
       - local: model_doc/led
         title: LED
+      - local: model_doc/llama
+        title: LLaMA
       - local: model_doc/longformer
         title: Longformer
       - local: model_doc/longt5

docs/source/en/index.mdx

@@ -112,6 +112,7 @@ The documentation is organized into five sections:
 1. **[LayoutXLM](model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
 1. **[LED](model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LeViT](model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
+1. **[LLaMA](model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
 1. **[Longformer](model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LongT5](model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
 1. **[LUKE](model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
@@ -262,6 +263,7 @@ Flax), PyTorch, and/or TensorFlow.
 |         LayoutLMv3          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            LeViT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             LLaMA           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           LongT5            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |            LUKE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/llama.mdx

@@ -0,0 +1,66 @@
+<!--Copyright 2022 The HuggingFace 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.
+-->
+
+# LLaMA
+
+## Overview
+
+The LLaMA model was proposed in [LLaMA: Open and Efficient Foundation Language Models](LLaMA: Open and Efficient Foundation Language Models)  by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample. It is a collection of foundation language models ranging from 7B to 65B parameters.
+
+The abstract from the paper is the following:
+
+*We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community. *
+
+Tips:
+
+- Weights for the LLaMA models can be obtained from by filling out [this form]()https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form)
+- After downloading the weights, they will need to be converted to the Hugging Face Transformers format using the [conversion script](/src/transformers/models/llama/convert_llama_weights_to_hf.py). The script can be called with the following (example) command:
+
+```bash
+python src/transformers/models/llama/convert_llama_weights_to_hf.py \
+    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path
+```
+
+- After conversion, the model and tokenizer can be loaded via:
+
+```python
+tokenizer = transformers.LlamaTokenizer.from_pretrained("/output/path/tokenizer/")
+model = transformers.LlamaForCausalLM.from_pretrained("/output/path/llama-7b/")
+```
+
+- The LLaMA tokenizer is based on [sentencepiece](https://github.com/google/sentencepiece). One quick of sentencepiece is that when decoding a sequence, if the first token is the start of the word (e.g. "Banana"), the tokenizer does not prepend the prefix space to the string. To have the tokenizer output the prefix space, set `decode_with_prefix_space=True` in the `LlamaTokenizer` object or in the tokenizer configuration.
+
+This model was contributed by [zphang](https://huggingface.co/zphang) with contributions from [BlackSamorez](https://huggingface.co/BlackSamorez). The code of the implementation in Hugging Face is based on GPT-NeoX [here](https://github.com/EleutherAI/gpt-neox). The original code of the authors can be found [here](https://github.com/facebookresearch/llama).
+
+## LlamaConfig
+
+[[autodoc]] LlamaConfig
+
+
+## LlamaTokenizer
+
+[[autodoc]] LlamaTokenizer
+    - build_inputs_with_special_tokens
+    - get_special_tokens_mask
+    - create_token_type_ids_from_sequences
+    - save_vocabulary
+
+## LlamaModel
+
+[[autodoc]] LlamaModel
+    - forward
+
+
+## LlamaForCausalLM
+
+[[autodoc]] LlamaForCausalLM
+    - forward

docs/source/en/tasks/language_modeling.mdx

@@ -412,4 +412,4 @@ For a more in-depth example of how to fine-tune a model for causal language mode
 [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)
 or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
 
-</Tip>
+</Tip>

src/transformers/__init__.py

@@ -257,6 +257,7 @@
     "models.layoutxlm": ["LayoutXLMProcessor"],
     "models.led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig", "LEDTokenizer"],
     "models.levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig"],
+    "models.llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
     "models.longformer": ["LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongformerConfig", "LongformerTokenizer"],
     "models.longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config"],
     "models.luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig", "LukeTokenizer"],
@@ -528,6 +529,7 @@
     _import_structure["models.deberta_v2"].append("DebertaV2Tokenizer")
     _import_structure["models.fnet"].append("FNetTokenizer")
     _import_structure["models.layoutxlm"].append("LayoutXLMTokenizer")
+    _import_structure["models.llama"].append("LlamaTokenizer")
     _import_structure["models.m2m_100"].append("M2M100Tokenizer")
     _import_structure["models.marian"].append("MarianTokenizer")
     _import_structure["models.mbart"].append("MBartTokenizer")
@@ -1468,6 +1470,13 @@
             "LevitPreTrainedModel",
         ]
     )
+    _import_structure["models.llama"].extend(
+        [
+            "LlamaForCausalLM",
+            "LlamaModel",
+            "LlamaPreTrainedModel",
+        ]
+    )
     _import_structure["models.longformer"].extend(
         [
             "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -3260,6 +3269,7 @@
     from .models.layoutxlm import LayoutXLMProcessor
     from .models.led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig, LEDTokenizer
     from .models.levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig
+    from .models.llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
     from .models.longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerTokenizer
     from .models.longt5 import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongT5Config
     from .models.luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig, LukeTokenizer
@@ -3504,6 +3514,7 @@
         from .models.deberta_v2 import DebertaV2Tokenizer
         from .models.fnet import FNetTokenizer
         from .models.layoutxlm import LayoutXLMTokenizer
+        from .models.llama import LlamaTokenizer
         from .models.m2m_100 import M2M100Tokenizer
         from .models.marian import MarianTokenizer
         from .models.mbart import MBart50Tokenizer, MBartTokenizer
@@ -3747,7 +3758,6 @@
         from .generation_utils import top_k_top_p_filtering
         from .modeling_utils import PreTrainedModel
 
-        # PyTorch model imports
         from .models.albert import (
             ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             AlbertForMaskedLM,
@@ -4256,6 +4266,22 @@
             LevitModel,
             LevitPreTrainedModel,
         )
+<<<<<<< HEAD
+=======
+        from .models.lilt import (
+            LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            LiltForQuestionAnswering,
+            LiltForSequenceClassification,
+            LiltForTokenClassification,
+            LiltModel,
+            LiltPreTrainedModel,
+        )
+        from .models.llama import (
+            LlamaForCausalLM,
+            LlamaModel,
+            LlamaPreTrainedModel,
+        )
+>>>>>>> 0041be5b3... LLaMA Implementation (#21955)
         from .models.longformer import (
             LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             LongformerForMaskedLM,

src/transformers/models/__init__.py

@@ -83,6 +83,7 @@
     layoutxlm,
     led,
     levit,
+    llama,
     longformer,
     longt5,
     luke,