GPT-J-6B (#13022)

* Test GPTJ implementation * Fixed conflicts * Update __init__.py * Update __init__.py * change GPT_J to GPTJ * fix missing imports and typos * use einops for now (need to change to torch ops later) * Use torch ops instead of einsum * remove einops deps * Update configuration_auto.py * Added GPT J * Update gptj.rst * Update __init__.py * Update test_modeling_gptj.py * Added GPT J * Changed configs to match GPT2 instead of GPT Neo * Removed non-existent sequence model * Update configuration_auto.py * Update configuration_auto.py * Update configuration_auto.py * Update modeling_gptj.py * Update modeling_gptj.py * Progress on updating configs to agree with GPT2 * Update modeling_gptj.py * num_layers -> n_layer * layer_norm_eps -> layer_norm_epsilon * attention_layers -> num_hidden_layers * Update modeling_gptj.py * attention_pdrop -> attn_pdrop * hidden_act -> activation_function * Update configuration_gptj.py * Update configuration_gptj.py * Update configuration_gptj.py * Update configuration_gptj.py * Update configuration_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * fix layernorm and lm_head size delete attn_type * Update docs/source/model_doc/gptj.rst Co-authored-by: Suraj Patil <surajp815@gmail.com> * removed claim that GPT J uses local attention * Removed GPTJForSequenceClassification * Update src/transformers/models/gptj/configuration_gptj.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Removed unsupported boilerplate * Update tests/test_modeling_gptj.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update tests/test_modeling_gptj.py Co-authored-by: Eric Hallahan <eric@hallahans.name> * Update tests/test_modeling_gptj.py Co-authored-by: Eric Hallahan <eric@hallahans.name> * Update tests/test_modeling_gptj.py Co-authored-by: Eric Hallahan <eric@hallahans.name> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update __init__.py * Update configuration_gptj.py * Update modeling_gptj.py * Corrected indentation * Remove stray backslash * Delete .DS_Store * Delete .DS_Store * Delete .DS_Store * Delete .DS_Store * Delete .DS_Store * Update docs to match * Remove tf loading * Remove config.jax * Remove stray `else:` statement * Remove references to `load_tf_weights_in_gptj` * Adapt tests to match output from GPT-J 6B * Apply suggestions from code review Co-authored-by: Suraj Patil <surajp815@gmail.com> * Default `activation_function` to `gelu_new` - Specify the approximate formulation of GELU to ensure parity with the default setting of `jax.nn.gelu()` * Fix part of the config documentation * Revert "Update configuration_auto.py" This reverts commit e9860e9c043b6ebf57a0e705044e9ec9ba2263bb. * Revert "Update configuration_auto.py" This reverts commit cfaaae4c4dc70f1fbe9abd60fc8bd0b863b8c011. * Revert "Update configuration_auto.py" This reverts commit 687788954fd0cfbc567fa1202d56a4ff9271944f. * Revert "Update configuration_auto.py" This reverts commit 194d024ea87d4fcef0dcb08e57f52c47511a9fc6. * Hyphenate GPT-J * Undid sorting of the models alphabetically * Reverting previous commit * fix style and quality issues * Update docs/source/model_doc/gptj.rst Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/__init__.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update tests/test_modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/__init__.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/configuration_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/configuration_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/configuration_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Replaced GPTJ-specific code with generic code * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Made the code always use rotary positional encodings * Update index.rst * Fix documentation * Combine attention classes - Condense all attention operations into `GPTJAttention` - Replicate GPT-2 and improve code clarity by renaming `GPTJAttention.attn_pdrop` and `GPTJAttention.resid_pdrop` to `GPTJAttention.attn_dropout` and `GPTJAttention.resid_dropout` * Removed `config.rotary_dim` from tests * Update test_modeling_gptj.py * Update test_modeling_gptj.py * Fix formatting * Removed depreciated argument `layer_id` to `GPTJAttention` * Update modeling_gptj.py * Update modeling_gptj.py * Fix code quality * Restore model functionality * Save `lm_head.weight` in checkpoints * Fix crashes when loading with reduced precision * refactor self._attn(...)` and rename layer weights" * make sure logits are in fp32 for sampling * improve docs * Add `GPTJForCausalLM` to `TextGenerationPipeline` whitelist * Added GPT-J to the README * Fix doc/readme consistency * Add rough parallelization support - Remove unused imports and variables - Clean up docstrings - Port experimental parallelization code from GPT-2 into GPT-J * Clean up loose ends * Fix index.rst Co-authored-by: kurumuz <kurumuz1@gmail.com> Co-authored-by: Suraj Patil <surajp815@gmail.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Eric Hallahan <eric@hallahans.name> Co-authored-by: Leo Gao <54557097+leogao2@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: your_github_username <your_github_email> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

GPT-J-6B (#13022)
* Test GPTJ implementation * Fixed conflicts * Update __init__.py * Update __init__.py * change GPT_J to GPTJ * fix missing imports and typos * use einops for now (need to change to torch ops later) * Use torch ops instead of einsum * remove einops deps * Update configuration_auto.py * Added GPT J * Update gptj.rst * Update __init__.py * Update test_modeling_gptj.py * Added GPT J * Changed configs to match GPT2 instead of GPT Neo * Removed non-existent sequence model * Update configuration_auto.py * Update configuration_auto.py * Update configuration_auto.py * Update modeling_gptj.py * Update modeling_gptj.py * Progress on updating configs to agree with GPT2 * Update modeling_gptj.py * num_layers -> n_layer * layer_norm_eps -> layer_norm_epsilon * attention_layers -> num_hidden_layers * Update modeling_gptj.py * attention_pdrop -> attn_pdrop * hidden_act -> activation_function * Update configuration_gptj.py * Update configuration_gptj.py * Update configuration_gptj.py * Update configuration_gptj.py * Update configuration_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * Update modeling_gptj.py * fix layernorm and lm_head size delete attn_type * Update docs/source/model_doc/gptj.rst Co-authored-by: Suraj Patil <surajp815@gmail.com> * removed claim that GPT J uses local attention * Removed GPTJForSequenceClassification * Update src/transformers/models/gptj/configuration_gptj.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Removed unsupported boilerplate * Update tests/test_modeling_gptj.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update tests/test_modeling_gptj.py Co-authored-by: Eric Hallahan <eric@hallahans.name> * Update tests/test_modeling_gptj.py Co-authored-by: Eric Hallahan <eric@hallahans.name> * Update tests/test_modeling_gptj.py Co-authored-by: Eric Hallahan <eric@hallahans.name> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update __init__.py * Update configuration_gptj.py * Update modeling_gptj.py * Corrected indentation * Remove stray backslash * Delete .DS_Store * Delete .DS_Store * Delete .DS_Store * Delete .DS_Store * Delete .DS_Store * Update docs to match * Remove tf loading * Remove config.jax * Remove stray `else:` statement * Remove references to `load_tf_weights_in_gptj` * Adapt tests to match output from GPT-J 6B * Apply suggestions from code review Co-authored-by: Suraj Patil <surajp815@gmail.com> * Default `activation_function` to `gelu_new` - Specify the approximate formulation of GELU to ensure parity with the default setting of `jax.nn.gelu()` * Fix part of the config documentation * Revert "Update configuration_auto.py" This reverts commit e9860e9c043b6ebf57a0e705044e9ec9ba2263bb. * Revert "Update configuration_auto.py" This reverts commit cfaaae4c4dc70f1fbe9abd60fc8bd0b863b8c011. * Revert "Update configuration_auto.py" This reverts commit 687788954fd0cfbc567fa1202d56a4ff9271944f. * Revert "Update configuration_auto.py" This reverts commit 194d024ea87d4fcef0dcb08e57f52c47511a9fc6. * Hyphenate GPT-J * Undid sorting of the models alphabetically * Reverting previous commit * fix style and quality issues * Update docs/source/model_doc/gptj.rst Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/__init__.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update tests/test_modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/__init__.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/configuration_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/configuration_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/configuration_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Replaced GPTJ-specific code with generic code * Update src/transformers/models/gptj/modeling_gptj.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Made the code always use rotary positional encodings * Update index.rst * Fix documentation * Combine attention classes - Condense all attention operations into `GPTJAttention` - Replicate GPT-2 and improve code clarity by renaming `GPTJAttention.attn_pdrop` and `GPTJAttention.resid_pdrop` to `GPTJAttention.attn_dropout` and `GPTJAttention.resid_dropout` * Removed `config.rotary_dim` from tests * Update test_modeling_gptj.py * Update test_modeling_gptj.py * Fix formatting * Removed depreciated argument `layer_id` to `GPTJAttention` * Update modeling_gptj.py * Update modeling_gptj.py * Fix code quality * Restore model functionality * Save `lm_head.weight` in checkpoints * Fix crashes when loading with reduced precision * refactor self._attn(...)` and rename layer weights" * make sure logits are in fp32 for sampling * improve docs * Add `GPTJForCausalLM` to `TextGenerationPipeline` whitelist * Added GPT-J to the README * Fix doc/readme consistency * Add rough parallelization support - Remove unused imports and variables - Clean up docstrings - Port experimental parallelization code from GPT-2 into GPT-J * Clean up loose ends * Fix index.rst Co-authored-by: kurumuz <kurumuz1@gmail.com> Co-authored-by: Suraj Patil <surajp815@gmail.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Eric Hallahan <eric@hallahans.name> Co-authored-by: Leo Gao <54557097+leogao2@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: your_github_username <your_github_email> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
c02cd95c · Stella Biderman · GitHub · e53af030 · c02cd95c · c02cd95c
Unverified Commit c02cd95c authored Aug 31, 2021 by Stella Biderman Committed by GitHub Aug 31, 2021
13 changed files
--- a/README.md
+++ b/README.md
@@ -240,6 +240,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[Funnel Transformer](https://huggingface.co/transformers/model_doc/funnel.html)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
 1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
 1. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
+1. **[GPT-J](https://huggingface.co/transformers/model_doc/gptj.html)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
 1. **[GPT Neo](https://huggingface.co/transformers/model_doc/gpt_neo.html)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
 1. **[Hubert](https://huggingface.co/transformers/model_doc/hubert.html)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
 1. **[I-BERT](https://huggingface.co/transformers/model_doc/ibert.html)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer

--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -191,116 +191,118 @@ Supported models
 30. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
    Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
    Luan, Dario Amodei** and Ilya Sutskever**.
-31. :doc:`GPT Neo <model_doc/gpt_neo>` (from EleutherAI) released in the repository `EleutherAI/gpt-neo
+31. :doc:`GPT-J <model_doc/gptj>` (from EleutherAI) released in the repository `kingoflolz/mesh-transformer-jax
+    <https://github.com/kingoflolz/mesh-transformer-jax/>`__ by Ben Wang and Aran Komatsuzaki.
+32. :doc:`GPT Neo <model_doc/gpt_neo>` (from EleutherAI) released in the repository `EleutherAI/gpt-neo
    <https://github.com/EleutherAI/gpt-neo>`__ by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
-32. :doc:`Hubert <model_doc/hubert>` (from Facebook) released with the paper `HuBERT: Self-Supervised Speech
+33. :doc:`Hubert <model_doc/hubert>` (from Facebook) released with the paper `HuBERT: Self-Supervised Speech
    Representation Learning by Masked Prediction of Hidden Units <https://arxiv.org/abs/2106.07447>`__ by Wei-Ning Hsu,
    Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
-33. :doc:`I-BERT <model_doc/ibert>` (from Berkeley) released with the paper `I-BERT: Integer-only BERT Quantization
+34. :doc:`I-BERT <model_doc/ibert>` (from Berkeley) released with the paper `I-BERT: Integer-only BERT Quantization
    <https://arxiv.org/abs/2101.01321>`__ by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer
-34. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+35. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
    of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
    Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-35. :doc:`LayoutLMv2 <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutLMv2:
+36. :doc:`LayoutLMv2 <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutLMv2:
    Multi-modal Pre-training for Visually-Rich Document Understanding <https://arxiv.org/abs/2012.14740>`__ by Yang Xu,
    Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min
    Zhang, Lidong Zhou.
-36. :doc:`LayoutXLM <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutXLM:
+37. :doc:`LayoutXLM <model_doc/layoutlmv2>` (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.
-37. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
+38. :doc:`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.
-38. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+39. :doc:`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.
-39. :doc:`LUKE <model_doc/luke>` (from Studio Ousia) released with the paper `LUKE: Deep Contextualized Entity
+40. :doc:`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.
-40. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+41. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
    Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
    by Hao Tan and Mohit Bansal.
-41. :doc:`M2M100 <model_doc/m2m_100>` (from Facebook) released with the paper `Beyond English-Centric Multilingual
+42. :doc:`M2M100 <model_doc/m2m_100>` (from Facebook) released with the paper `Beyond English-Centric Multilingual
    Machine Translation <https://arxiv.org/abs/2010.11125>`__ by by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi
    Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman
    Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
-42. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+43. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
    Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
    Translator Team.
-43. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+44. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
    Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
    Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-44. :doc:`MBart-50 <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Translation with Extensible
+45. :doc:`MBart-50 <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Translation with Extensible
    Multilingual Pretraining and Finetuning <https://arxiv.org/abs/2008.00401>`__ by Yuqing Tang, Chau Tran, Xian Li,
    Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-45. :doc:`Megatron-BERT <model_doc/megatron_bert>` (from NVIDIA) released with the paper `Megatron-LM: Training
+46. :doc:`Megatron-BERT <model_doc/megatron_bert>` (from NVIDIA) released with the paper `Megatron-LM: Training
    Multi-Billion Parameter Language Models Using Model Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad
    Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
-46. :doc:`Megatron-GPT2 <model_doc/megatron_gpt2>` (from NVIDIA) released with the paper `Megatron-LM: Training
+47. :doc:`Megatron-GPT2 <model_doc/megatron_gpt2>` (from NVIDIA) released with the paper `Megatron-LM: Training
    Multi-Billion Parameter Language Models Using Model Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad
    Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
-47. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
+48. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
    Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
    Jianfeng Lu, Tie-Yan Liu.
-48. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+49. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
    text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
    Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-49. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+50. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
    Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
    Mohammad Saleh and Peter J. Liu.
-50. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+51. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
    Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
    Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-51. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+52. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
    Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-52. :doc:`RemBERT <model_doc/rembert>` (from Google Research) released with the paper `Rethinking embedding coupling in
+53. :doc:`RemBERT <model_doc/rembert>` (from Google Research) released with the paper `Rethinking embedding coupling in
    pre-trained language models <https://arxiv.org/pdf/2010.12821.pdf>`__ by Hyung Won Chung, Thibault Févry, Henry
    Tsai, M. Johnson, Sebastian Ruder.
-53. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+54. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
    Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
    Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-54. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
+55. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
    Enhanced Transformer with Rotary Position Embedding <https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and
    Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
-55. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
+56. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
    `fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun
    Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-56. :doc:`Splinter <model_doc/splinter>` (from Tel Aviv University), released together with the paper `Few-Shot
+57. :doc:`Splinter <model_doc/splinter>` (from Tel Aviv University), released together with the paper `Few-Shot
    Question Answering by Pretraining Span Selection <https://arxiv.org/abs/2101.00438>`__ by Ori Ram, Yuval Kirstain,
    Jonathan Berant, Amir Globerson, Omer Levy.
-57. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+58. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
    about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
    Krishna, and Kurt W. Keutzer.
-58. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+59. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
    Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
    Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-59. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
+60. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
    Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
    Francesco Piccinno and Julian Martin Eisenschlos.
-60. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+61. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
    Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
    Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-61. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
+62. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
    Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`__ by Alexey Dosovitskiy,
    Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias
    Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
-62. :doc:`VisualBERT <model_doc/visual_bert>` (from UCLA NLP) released with the paper `VisualBERT: A Simple and
+63. :doc:`VisualBERT <model_doc/visual_bert>` (from UCLA NLP) released with the paper `VisualBERT: A Simple and
    Performant Baseline for Vision and Language <https://arxiv.org/pdf/1908.03557>`__ by Liunian Harold Li, Mark
    Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
-63. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
+64. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
    Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry
    Zhou, Abdelrahman Mohamed, Michael Auli.
-64. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+65. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
    Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
-65. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+66. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
    Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
    Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-66. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+67. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
    Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
    Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
    Zettlemoyer and Veselin Stoyanov.
-67. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
+68. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
    Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
    Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-68. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
+69. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
    Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis
    Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.

@@ -372,6 +374,8 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            GPT-J            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -574,6 +578,7 @@ Flax), PyTorch, and/or TensorFlow.
    model_doc/mt5
    model_doc/gpt
    model_doc/gpt2
+    model_doc/gptj
    model_doc/gpt_neo
    model_doc/hubert
    model_doc/pegasus

--- a/docs/source/model_doc/gptj.rst
+++ b/docs/source/model_doc/gptj.rst
+.. 
+    Copyright 2021 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.
+
+GPT-J
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The GPT-J model was released in the `kingoflolz/mesh-transformer-jax
+<https://github.com/kingoflolz/mesh-transformer-jax>`__ repository by Ben Wang and Aran Komatsuzaki. It is a GPT-2-like
+causal language model trained on `the Pile <https://pile.eleuther.ai/>`__ dataset.
+
+This model was contributed by `Stella Biderman <https://huggingface.co/stellaathena>`__.
+
+Tips:
+
+- Running [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) in float32 precision on GPU requires at least 24 GB of
+  RAM. On GPUs with less than 24 GB RAM, one should therefore load the model in half-precision:
+
+.. code-block::
+
+    >>> from transformers import GPTJForCausalLM
+    >>> import torch
+
+    >>> model =  GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
+
+Generation
+_______________________________________________________________________________________________________________________
+
+The :meth:`~transformers.generation_utils.GenerationMixin.generate` method can be used to generate text using GPT-J
+model.
+
+.. code-block::
+
+    >>> from transformers import AutoModelForCausalLM, AutoTokenizer
+    >>> model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
+
+    >>> prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
+    ...          "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
+    ...          "researchers was the fact that the unicorns spoke perfect English."
+
+    >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+    >>> gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
+    >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
+
+...or in float16 precision:
+
+.. code-block::
+
+    >>> from transformers import GPTJForCausalLM, AutoTokenizer
+    >>> import torch
+
+    >>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
+    >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
+
+    >>> prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
+    ...          "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
+    ...          "researchers was the fact that the unicorns spoke perfect English."
+
+    >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+    >>> gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
+    >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
+
+
+GPTJConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTJConfig
+    :members:
+
+GPTJModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTJModel
+    :members: forward
+
+
+GPTJForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTJForCausalLM
+    :members: forward
+
+
+GPTJForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTJForSequenceClassification
+    :members: forward
--- a/src/transformers/__init__.py
+++ b/src/transformers/__init__.py
@@ -213,6 +213,7 @@ _import_structure = {
    "models.funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig", "FunnelTokenizer"],
    "models.gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2Tokenizer"],
    "models.gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig"],
+    "models.gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig"],
    "models.herbert": ["HerbertTokenizer"],
    "models.hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"],
    "models.ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig"],
@@ -824,6 +825,15 @@ if is_torch_available():
            "load_tf_weights_in_gpt_neo",
        ]
    )
+    _import_structure["models.gptj"].extend(
+        [
+            "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "GPTJForCausalLM",
+            "GPTJForSequenceClassification",
+            "GPTJModel",
+            "GPTJPreTrainedModel",
+        ]
+    )
    _import_structure["models.hubert"].extend(
        [
            "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -1966,6 +1976,7 @@ if TYPE_CHECKING:
    from .models.funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig, FunnelTokenizer
    from .models.gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2Tokenizer
    from .models.gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig
+    from .models.gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig
    from .models.herbert import HerbertTokenizer
    from .models.hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig
    from .models.ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig
@@ -2486,6 +2497,13 @@ if TYPE_CHECKING:
            GPTNeoPreTrainedModel,
            load_tf_weights_in_gpt_neo,
        )
+        from .models.gptj import (
+            GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
+            GPTJForCausalLM,
+            GPTJForSequenceClassification,
+            GPTJModel,
+            GPTJPreTrainedModel,
+        )
        from .models.hubert import (
            HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            HubertForCTC,

--- a/src/transformers/models/__init__.py
+++ b/src/transformers/models/__init__.py
@@ -50,6 +50,7 @@ from . import (
    funnel,
    gpt2,
    gpt_neo,
+    gptj,
    herbert,
    hubert,
    ibert,

--- a/src/transformers/models/auto/configuration_auto.py
+++ b/src/transformers/models/auto/configuration_auto.py
@@ -26,6 +26,7 @@ from ...file_utils import CONFIG_NAME
 CONFIG_MAPPING_NAMES = OrderedDict(
    [
        # Add configs here
+        ("gptj", "GPTJConfig"),
        ("layoutlmv2", "LayoutLMv2Config"),
        ("beit", "BeitConfig"),
        ("rembert", "RemBertConfig"),
@@ -96,6 +97,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
 CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
    [
        # Add archive maps here
+        ("gptj", "GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("layoutlmv2", "LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("beit", "BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("rembert", "REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -158,6 +160,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
 MODEL_NAMES_MAPPING = OrderedDict(
    [
        # Add full (and cased) model names here
+        ("gptj", "GPT-J"),
        ("beit", "BeiT"),
        ("rembert", "RemBERT"),
        ("layoutlmv2", "LayoutLMv2"),

--- a/src/transformers/models/auto/modeling_auto.py
+++ b/src/transformers/models/auto/modeling_auto.py
@@ -28,6 +28,7 @@ logger = logging.get_logger(__name__)
 MODEL_MAPPING_NAMES = OrderedDict(
    [
        # Base model mapping
+        ("gptj", "GPTJModel"),
        ("layoutlmv2", "LayoutLMv2Model"),
        ("beit", "BeitModel"),
        ("rembert", "RemBertModel"),
@@ -135,6 +136,7 @@ MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
 MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
    [
        # Model with LM heads mapping
+        ("gptj", "GPTJForCausalLM"),
        ("rembert", "RemBertForMaskedLM"),
        ("roformer", "RoFormerForMaskedLM"),
        ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"),
@@ -183,6 +185,7 @@ MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
 MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Causal LM mapping
+        ("gptj", "GPTJForCausalLM"),
        ("rembert", "RemBertForCausalLM"),
        ("roformer", "RoFormerForCausalLM"),
        ("bigbird_pegasus", "BigBirdPegasusForCausalLM"),
@@ -286,6 +289,7 @@ MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
 MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Sequence Classification mapping
+        ("gptj", "GPTJForSequenceClassification"),
        ("layoutlmv2", "LayoutLMv2ForSequenceClassification"),
        ("rembert", "RemBertForSequenceClassification"),
        ("canine", "CanineForSequenceClassification"),

--- a/src/transformers/models/gptj/__init__.py
+++ b/src/transformers/models/gptj/__init__.py
+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+# Copyright 2021 The EleutherAI and HuggingFace Teams. 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.
+from typing import TYPE_CHECKING
+
+from ...file_utils import _LazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig"],
+}
+
+if is_torch_available():
+    _import_structure["modeling_gptj"] = [
+        "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "GPTJForCausalLM",
+        "GPTJForSequenceClassification",
+        "GPTJModel",
+        "GPTJPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig
+
+    if is_torch_available():
+        from .modeling_gptj import (
+            GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
+            GPTJForCausalLM,
+            GPTJForSequenceClassification,
+            GPTJModel,
+            GPTJPreTrainedModel,
+        )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
--- a/src/transformers/models/gptj/configuration_gptj.py
+++ b/src/transformers/models/gptj/configuration_gptj.py
+# coding=utf-8
+# Copyright 2021 The EleutherAI and HuggingFace Teams. 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.
+""" GPT-J model configuration """
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
+    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
+}
+
+
+class GPTJConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a :class:`~transformers.GPTJModel`. It is used to
+    instantiate a GPT-J model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the GPT-J `gpt-j-6B
+    <https://huggingface.co/EleutherAI/gpt-j-6B>`__ architecture. Configuration objects inherit from
+    :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from
+    :class:`~transformers.PretrainedConfig` for more information.
+
+    Args:
+        vocab_size (:obj:`int`, `optional`, defaults to 50400):
+            Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.GPTJModel`.
+        n_positions (:obj:`int`, `optional`, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        n_ctx (:obj:`int`, `optional`, defaults to 2048):
+            Dimensionality of the causal mask (usually same as n_positions).
+        n_embd (:obj:`int`, `optional`, defaults to 4096):
+            Dimensionality of the embeddings and hidden states.
+        n_layer (:obj:`int`, `optional`, defaults to 28):
+            Number of hidden layers in the Transformer encoder.
+        n_head (:obj:`int`, `optional`, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        rotary_dim (:obj:`int`, `optional`, defaults to 64):
+            Number of dimensions in the embedding that Rotary Position Embedding is applied to.
+        n_inner (:obj:`int`, `optional`, defaults to None):
+            Dimensionality of the inner feed-forward layers. :obj:`None` will set it to 4 times n_embd
+        activation_function (:obj:`str`, `optional`, defaults to :obj:`"gelu_new"`):
+            Activation function, to be selected in the list :obj:`["relu", "silu", "gelu", "tanh", "gelu_new"]`.
+        resid_pdrop (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        embd_pdrop (:obj:`int`, `optional`, defaults to 0.1):
+            The dropout ratio for the embeddings.
+        attn_pdrop (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout ratio for the attention.
+        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
+            The epsilon to use in the layer normalization layers.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        scale_attn_weights (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Scale attention weights by dividing by sqrt(hidden_size).
+        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.
+        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+
+    Example::
+
+        >>> from transformers import GPTJModel, GPTJConfig
+
+        >>> # Initializing a GPT-J 6B configuration
+        >>> configuration = GPTJConfig()
+
+        >>> # Initializing a model from the configuration
+        >>> model = GPTJModel(configuration)
+
+        >>> # Accessing the model configuration
+        >>> configuration = model.config
+    """
+    model_type = "gptj"
+
+    def __init__(
+        self,
+        vocab_size=50400,
+        n_positions=2048,
+        n_ctx=2048,
+        n_embd=4096,
+        n_layer=28,
+        n_head=16,
+        rotary_dim=64,
+        n_inner=None,
+        activation_function="gelu_new",
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attn_pdrop=0.0,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        scale_attn_weights=True,
+        gradient_checkpointing=False,
+        use_cache=True,
+        bos_token_id=50256,
+        eos_token_id=50256,
+        **kwargs
+    ):
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.n_ctx = n_ctx
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_inner = n_inner
+        self.rotary_dim = rotary_dim
+        self.activation_function = activation_function
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attn_pdrop = attn_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.gradient_checkpointing = gradient_checkpointing
+        self.scale_attn_weights = scale_attn_weights
+        self.use_cache = use_cache
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+    @property
+    def max_position_embeddings(self):
+        return self.n_positions
+
+    @property
+    def hidden_size(self):
+        return self.n_embd
+
+    @property
+    def num_attention_heads(self):
+        return self.n_head
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layer
--- a/src/transformers/models/gptj/modeling_gptj.py
+++ b/src/transformers/models/gptj/modeling_gptj.py
--- a/src/transformers/pipelines/text_generation.py
+++ b/src/transformers/pipelines/text_generation.py
@@ -32,6 +32,22 @@ class TextGenerationPipeline(Pipeline):
    begging for his blessing. <eod> </s> <eos>
    """

+    ALLOWED_MODELS = [
+        "XLNetLMHeadModel",
+        "TransfoXLLMHeadModel",
+        "ReformerModelWithLMHead",
+        "GPT2LMHeadModel",
+        "GPTJForCausalLM",
+        "GPTNeoForCausalLM",
+        "OpenAIGPTLMHeadModel",
+        "CTRLLMHeadModel",
+        "TFXLNetLMHeadModel",
+        "TFTransfoXLLMHeadModel",
+        "TFGPT2LMHeadModel",
+        "TFOpenAIGPTLMHeadModel",
+        "TFCTRLLMHeadModel",
+    ]
+
    def __init__(self, *args, return_full_text=True, **kwargs):
        super().__init__(*args, **kwargs)
        self.check_model_type(

--- a/src/transformers/utils/dummy_pt_objects.py
+++ b/src/transformers/utils/dummy_pt_objects.py
@@ -1808,6 +1808,45 @@ def load_tf_weights_in_gpt_neo(*args, **kwargs):
    requires_backends(load_tf_weights_in_gpt_neo, ["torch"])


+GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class GPTJForCausalLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
+class GPTJForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
+class GPTJModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
+class GPTJPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None



--- a/tests/test_modeling_gptj.py
+++ b/tests/test_modeling_gptj.py