[`Llava`] Add Llava to transformers (#27662)

* add model like * logits match * minor fixes * fixes * up * up * add todo * llava processor * keep the processor simple * add conversion script * fixup * fix copies * up * add to index * fix config + logits * fix * refactor * more refactor * more refactor * fix copies * add authors * v1 tests * add `LlavaProcessor` in init * remove unneeded import * up * up * docs * up * fix CI * fix CI * add attention mask in test * make fixup * remove the vision model * that' s the dirty way to do it * nits * nits * updates * add more tests * add input tests * fixup * more styling * nits * updates amd cleanup * fixup the generation expected results * fix the testing script * some cleanup and simplification which does not work yet but almost there! * make correct dispatch operations * vectorize works for batch of images and text * last todos * nits * update test and modeling code * remove useless function for now * fix few issues * fix generation * some nits * add bakllava * nits * remove duplicated code * finis merge * cleanup * missed this line * fill the todos * add left padding offset * add left and rignt padding logic * bool to properly index * make sure * more cleanups * batch is fixed 😉 * add correct device for tensor creation * fix some dtype missmatch * ruff * update conversion script * Update src/transformers/__init__.py * fa 2 support + fix conversion script * more * correct reshaping * fix test dict * fix copies by ignoring * fix nit * skip clip vision model * fixup * fixup * LlavaForVisionText2Text -> LlavaForCausalLM * update * fix * raise correct errors * fix * docs * nuke for now * nits here and there * fixup * fix remaining tests * update LlavaForConditionalGeneration instead of CausalLM * fixups * pipeline support * slow and piepline tests * supports batch * nits * cleanup * fix first integration tests * add pad token where needed * correct etsts * fixups * update pipeline testr * fix quality * nits * revert unneeded change * nit * use BatchFeature * from ...feature_extraction_utils import BatchFeature * nits * nits * properly update * more f*** nits * fix copies * comment * keep slow test slow * Update src/transformers/models/llava/processing_llava.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * add piepline example * add pixel values in docstrign * update pr doctest * fix * fix slow tests * remove hack * fixup * small note * forward contrib credits from PR25789 * forward contrib credits from original implementation and work * add arthur * Update src/transformers/models/llava/processing_llava.py Co-authored-by: Lysandre Debut <hi@lysand.re> * update docstring * nit * move to not doctested because of timeout issues * fixup * add description * more * fix-copies * fix docs * add beam search * add more comments * add typehints on processor * add speedup plot * update slow tests and docs * push test * push batched test * fix batched generation with different number of images * remove benchmark due to a bug * fix test * fix copies * add gcolab demo --------- Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: shauray8 <shauray8@users.noreply.github.com> Co-authored-by: haotian-liu <haotian-liu@users.noreply.github.com> Co-authored-by: Lysandre Debut <hi@lysand.re>

[`Llava`] Add Llava to transformers (#27662)
* add model like * logits match * minor fixes * fixes * up * up * add todo * llava processor * keep the processor simple * add conversion script * fixup * fix copies * up * add to index * fix config + logits * fix * refactor * more refactor * more refactor * fix copies * add authors * v1 tests * add `LlavaProcessor` in init * remove unneeded import * up * up * docs * up * fix CI * fix CI * add attention mask in test * make fixup * remove the vision model * that' s the dirty way to do it * nits * nits * updates * add more tests * add input tests * fixup * more styling * nits * updates amd cleanup * fixup the generation expected results * fix the testing script * some cleanup and simplification which does not work yet but almost there! * make correct dispatch operations * vectorize works for batch of images and text * last todos * nits * update test and modeling code * remove useless function for now * fix few issues * fix generation * some nits * add bakllava * nits * remove duplicated code * finis merge * cleanup * missed this line * fill the todos * add left padding offset * add left and rignt padding logic * bool to properly index * make sure * more cleanups * batch is fixed 😉 * add correct device for tensor creation * fix some dtype missmatch * ruff * update conversion script * Update src/transformers/__init__.py * fa 2 support + fix conversion script * more * correct reshaping * fix test dict * fix copies by ignoring * fix nit * skip clip vision model * fixup * fixup * LlavaForVisionText2Text -> LlavaForCausalLM * update * fix * raise correct errors * fix * docs * nuke for now * nits here and there * fixup * fix remaining tests * update LlavaForConditionalGeneration instead of CausalLM * fixups * pipeline support * slow and piepline tests * supports batch * nits * cleanup * fix first integration tests * add pad token where needed * correct etsts * fixups * update pipeline testr * fix quality * nits * revert unneeded change * nit * use BatchFeature * from ...feature_extraction_utils import BatchFeature * nits * nits * properly update * more f*** nits * fix copies * comment * keep slow test slow * Update src/transformers/models/llava/processing_llava.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * add piepline example * add pixel values in docstrign * update pr doctest * fix * fix slow tests * remove hack * fixup * small note * forward contrib credits from PR25789 * forward contrib credits from original implementation and work * add arthur * Update src/transformers/models/llava/processing_llava.py Co-authored-by: Lysandre Debut <hi@lysand.re> * update docstring * nit * move to not doctested because of timeout issues * fixup * add description * more * fix-copies * fix docs * add beam search * add more comments * add typehints on processor * add speedup plot * update slow tests and docs * push test * push batched test * fix batched generation with different number of images * remove benchmark due to a bug * fix test * fix copies * add gcolab demo --------- Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: shauray8 <shauray8@users.noreply.github.com> Co-authored-by: haotian-liu <haotian-liu@users.noreply.github.com> Co-authored-by: Lysandre Debut <hi@lysand.re>
44b5506d · Younes Belkada · GitHub · 0410a29a · 44b5506d · 44b5506d
Unverified Commit 44b5506d authored Dec 07, 2023 by Younes Belkada Committed by GitHub Dec 07, 2023
20 changed files
--- a/README.md
+++ b/README.md
@@ -397,6 +397,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
 1. **[LLaMA](https://huggingface.co/docs/transformers/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. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
+1. **[Llava](https://huggingface.co/docs/transformers/main/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
 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.

--- a/README_es.md
+++ b/README_es.md
@@ -372,6 +372,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
 1. **[LLaMA](https://huggingface.co/docs/transformers/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. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom..
+1. **[Llava](https://huggingface.co/docs/transformers/main/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
 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.

--- a/README_hd.md
+++ b/README_hd.md
@@ -346,6 +346,7 @@ conda install -c huggingface transformers
 1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (दक्षिण चीन प्रौद्योगिकी विश्वविद्यालय से) साथ में कागज [LiLT: एक सरल लेकिन प्रभावी भाषा-स्वतंत्र लेआउट ट्रांसफार्मर संरचित दस्तावेज़ समझ के लिए](https://arxiv.org/abs/2202.13669) जियापेंग वांग, लियानवेन जिन, काई डिंग द्वारा पोस्ट किया गया।
 1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (The FAIR team of Meta AI से) 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. द्वाराअनुसंधान पत्र [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) के साथ जारी किया गया
 1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (The FAIR team of Meta AI से) Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.. द्वाराअनुसंधान पत्र [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) के साथ जारी किया गया
+1. **[Llava](https://huggingface.co/docs/transformers/main/model_doc/llava)** (Microsoft Research & University of Wisconsin-Madison से) Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee. द्वाराअनुसंधान पत्र [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) के साथ जारी किया गया
 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)** (मैंडी गुओ, जोशुआ आइंस्ली, डेविड यूथस, सैंटियागो ओंटानन, जियानमो नि, यूं-हुआन सुंग, यिनफेई यांग द्वारा पोस्ट किया गया।
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (स्टूडियो औसिया से) साथ में पेपर [LUKE: डीप कॉन्टेक्स्टुअलाइज्ड एंटिटी रिप्रेजेंटेशन विद एंटिटी-अवेयर सेल्फ-अटेंशन](https ://arxiv.org/abs/2010.01057) Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto द्वारा।

--- a/README_ja.md
+++ b/README_ja.md
@@ -406,6 +406,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (South China University of Technology から) Jiapeng Wang, Lianwen Jin, Kai Ding から公開された研究論文: [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669)
 1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (The FAIR team of Meta AI から) 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. から公開された研究論文 [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
 1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (The FAIR team of Meta AI から) Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.. から公開された研究論文 [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX)
+1. **[Llava](https://huggingface.co/docs/transformers/main/model_doc/llava)** (Microsoft Research & University of Wisconsin-Madison から) Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee. から公開された研究論文 [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744)
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (AllenAI から) Iz Beltagy, Matthew E. Peters, Arman Cohan から公開された研究論文: [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150)
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (Google AI から) Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang から公開された研究論文: [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916)
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (Studio Ousia から) Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto から公開された研究論文: [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057)

--- a/README_ko.md
+++ b/README_ko.md
@@ -321,6 +321,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (South China University of Technology 에서) Jiapeng Wang, Lianwen Jin, Kai Ding 의 [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) 논문과 함께 발표했습니다.
 1. **[LLaMA](https://huggingface.co/docs/transformers/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. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (The FAIR team of Meta AI 에서 제공)은 Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom..의 [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX)논문과 함께 발표했습니다.
+1. **[Llava](https://huggingface.co/docs/transformers/main/model_doc/llava)** (Microsoft Research & University of Wisconsin-Madison 에서 제공)은 Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.의 [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744)논문과 함께 발표했습니다.
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (AllenAI 에서) Iz Beltagy, Matthew E. Peters, Arman Cohan 의 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 논문과 함께 발표했습니다.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (Google AI 에서) Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang 의 [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) 논문과 함께 발표했습니다.
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (Studio Ousia 에서) Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto 의 [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) 논문과 함께 발표했습니다.

--- a/README_zh-hans.md
+++ b/README_zh-hans.md
@@ -345,6 +345,7 @@ conda install -c huggingface transformers
 1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (来自 South China University of Technology) 伴随论文 [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) 由 Jiapeng Wang, Lianwen Jin, Kai Ding 发布。
 1. **[LLaMA](https://huggingface.co/docs/transformers/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. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (来自 The FAIR team of Meta AI) 伴随论文 [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) 由 Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom. 发布。
+1. **[Llava](https://huggingface.co/docs/transformers/main/model_doc/llava)** (来自 Microsoft Research & University of Wisconsin-Madison) 伴随论文 [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) 由 Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee 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。

--- a/README_zh-hant.md
+++ b/README_zh-hant.md
@@ -357,6 +357,7 @@ conda install -c huggingface transformers
 1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
 1. **[LLaMA](https://huggingface.co/docs/transformers/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. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom..
+1. **[Llava](https://huggingface.co/docs/transformers/main/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
 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.

--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -703,6 +703,8 @@
        title: LayoutXLM
      - local: model_doc/lilt
        title: LiLT
+      - local: model_doc/llava
+        title: Llava
      - local: model_doc/lxmert
        title: LXMERT
      - local: model_doc/matcha

--- a/docs/source/en/index.md
+++ b/docs/source/en/index.md
@@ -169,6 +169,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                          [LiLT](model_doc/lilt)                          |       ✅        |         ❌         |      ❌      |
 |                         [LLaMA](model_doc/llama)                         |       ✅        |         ❌         |      ✅      |
 |                        [Llama2](model_doc/llama2)                        |       ✅        |         ❌         |      ✅      |
+|                         [Llava](model_doc/llava)                         |       ✅        |         ❌         |      ❌      |
 |                    [Longformer](model_doc/longformer)                    |       ✅        |         ✅         |      ❌      |
 |                        [LongT5](model_doc/longt5)                        |       ✅        |         ❌         |      ✅      |
 |                          [LUKE](model_doc/luke)                          |       ✅        |         ❌         |      ❌      |

--- a/docs/source/en/model_doc/llava.md
+++ b/docs/source/en/model_doc/llava.md
+<!--Copyright 2023 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Llava
+
+## Overview
+
+Llava is an open-source chatbot trained by fine-tuning LlamA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. In other words, it is an multi-modal version of LLMs fine-tuned for chat / instructions.
+
+The Llava model was proposed in [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
+
+The abstract from the paper is the following:
+
+*Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with simple response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ∼1 day on a single 8-A100 node. We hope this can make state-of-the-art LMM research more accessible. Code and model will be publicly available*
+
+Tips:
+
+- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to call `processor.tokenizer.padding_side = "left"` before generating.
+
+- Note the model has not been explicitly trained to process multiple images in the same prompt, although this is technically possible, you may experience inaccurate results.
+
+- For better results, we recommend users to prompt the model with the correct prompt format: 
+
+```bash
+"USER: <image>\n<prompt>ASSISTANT:"
+```
+
+For multiple turns conversation:
+
+```bash
+"USER: <image>\n<prompt1>ASSISTANT: <answer1>USER: <prompt2>ASSISTANT: <answer2>USER: <prompt3>ASSISTANT:"
+```
+
+This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ) and [ybelkada](https://huggingface.co/ybelkada).
+The original code can be found [here](https://github.com/haotian-liu/LLaVA/tree/main/llava).
+
+Check out also this [Google Colab demo](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing) on how to run Llava on a free-tier Google colab instance.
+
+### Using Flash Attention 2
+
+Flash Attention 2 is an even faster, optimized version of the previous optimization, please refer to the [Flash Attention 2 section of performance docs](https://huggingface.co/docs/transformers/perf_infer_gpu_one).
+
+## LlavaConfig
+
+[[autodoc]] LlavaConfig
+
+## LlavaProcessor
+
+[[autodoc]] LlavaProcessor
+
+## LlavaForConditionalGeneration
+
+[[autodoc]] LlavaForConditionalGeneration
+    - forward
--- a/src/transformers/__init__.py
+++ b/src/transformers/__init__.py
@@ -542,6 +542,10 @@ _import_structure = {
    "models.levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig"],
    "models.lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
    "models.llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
+    "models.llava": [
+        "LLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "LlavaConfig",
+    ],
    "models.longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
@@ -2433,6 +2437,14 @@ else:
            "LlamaPreTrainedModel",
        ]
    )
+    _import_structure["models.llava"].extend(
+        [
+            "LLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "LlavaForConditionalGeneration",
+            "LlavaPreTrainedModel",
+            "LlavaProcessor",
+        ]
+    )
    _import_structure["models.longformer"].extend(
        [
            "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -5170,6 +5182,10 @@ if TYPE_CHECKING:
    from .models.levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig
    from .models.lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
    from .models.llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
+    from .models.llava import (
+        LLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        LlavaConfig,
+    )
    from .models.longformer import (
        LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LongformerConfig,
@@ -6869,11 +6885,12 @@ if TYPE_CHECKING:
            LiltModel,
            LiltPreTrainedModel,
        )
-        from .models.llama import (
-            LlamaForCausalLM,
-            LlamaForSequenceClassification,
-            LlamaModel,
-            LlamaPreTrainedModel,
+        from .models.llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
+        from .models.llava import (
+            LLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            LlavaForConditionalGeneration,
+            LlavaPreTrainedModel,
+            LlavaProcessor,
        )
        from .models.longformer import (
            LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,

--- a/src/transformers/models/__init__.py
+++ b/src/transformers/models/__init__.py
@@ -119,6 +119,7 @@ from . import (
    levit,
    lilt,
    llama,
+    llava,
    longformer,
    longt5,
    luke,

--- a/src/transformers/models/auto/configuration_auto.py
+++ b/src/transformers/models/auto/configuration_auto.py
@@ -56,6 +56,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
        ("chinese_clip", "ChineseCLIPConfig"),
        ("clap", "ClapConfig"),
        ("clip", "CLIPConfig"),
+        ("clip_vision_model", "CLIPVisionConfig"),
        ("clipseg", "CLIPSegConfig"),
        ("clvp", "ClvpConfig"),
        ("code_llama", "LlamaConfig"),
@@ -126,6 +127,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
        ("levit", "LevitConfig"),
        ("lilt", "LiltConfig"),
        ("llama", "LlamaConfig"),
+        ("llava", "LlavaConfig"),
        ("longformer", "LongformerConfig"),
        ("longt5", "LongT5Config"),
        ("luke", "LukeConfig"),
@@ -348,6 +350,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
        ("levit", "LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("lilt", "LILT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("llama", "LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP"),
+        ("llava", "LLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("longt5", "LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("luke", "LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -494,6 +497,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
        ("chinese_clip", "Chinese-CLIP"),
        ("clap", "CLAP"),
        ("clip", "CLIP"),
+        ("clip_vision_model", "CLIPVisionModel"),
        ("clipseg", "CLIPSeg"),
        ("clvp", "CLVP"),
        ("code_llama", "CodeLlama"),
@@ -573,6 +577,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
        ("lilt", "LiLT"),
        ("llama", "LLaMA"),
        ("llama2", "Llama2"),
+        ("llava", "Llava"),
        ("longformer", "Longformer"),
        ("longt5", "LongT5"),
        ("luke", "LUKE"),
@@ -740,6 +745,7 @@ SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict(
        ("kosmos-2", "kosmos2"),
        ("maskformer-swin", "maskformer"),
        ("xclip", "x_clip"),
+        ("clip_vision_model", "clip"),
    ]
 )


--- a/src/transformers/models/auto/image_processing_auto.py
+++ b/src/transformers/models/auto/image_processing_auto.py
@@ -75,6 +75,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
+        ("llava", "CLIPImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),

--- a/src/transformers/models/auto/modeling_auto.py
+++ b/src/transformers/models/auto/modeling_auto.py
@@ -59,6 +59,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
        ("chinese_clip", "ChineseCLIPModel"),
        ("clap", "ClapModel"),
        ("clip", "CLIPModel"),
+        ("clip_vision_model", "CLIPVisionModel"),
        ("clipseg", "CLIPSegModel"),
        ("clvp", "ClvpModelForConditionalGeneration"),
        ("code_llama", "LlamaModel"),
@@ -270,6 +271,7 @@ MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
        ("ibert", "IBertForMaskedLM"),
        ("idefics", "IdeficsForVisionText2Text"),
        ("layoutlm", "LayoutLMForMaskedLM"),
+        ("llava", "LlavaForConditionalGeneration"),
        ("longformer", "LongformerForMaskedLM"),
        ("luke", "LukeForMaskedLM"),
        ("lxmert", "LxmertForPreTraining"),
@@ -592,6 +594,7 @@ MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict(
        ("git", "GitForCausalLM"),
        ("instructblip", "InstructBlipForConditionalGeneration"),
        ("kosmos-2", "Kosmos2ForConditionalGeneration"),
+        ("llava", "LlavaForConditionalGeneration"),
        ("pix2struct", "Pix2StructForConditionalGeneration"),
        ("vision-encoder-decoder", "VisionEncoderDecoderModel"),
    ]

--- a/src/transformers/models/auto/processing_auto.py
+++ b/src/transformers/models/auto/processing_auto.py
@@ -64,6 +64,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
        ("kosmos-2", "Kosmos2Processor"),
        ("layoutlmv2", "LayoutLMv2Processor"),
        ("layoutlmv3", "LayoutLMv3Processor"),
+        ("llava", "LlavaProcessor"),
        ("markuplm", "MarkupLMProcessor"),
        ("mctct", "MCTCTProcessor"),
        ("mgp-str", "MgpstrProcessor"),

--- a/src/transformers/models/auto/tokenization_auto.py
+++ b/src/transformers/models/auto/tokenization_auto.py
@@ -203,6 +203,7 @@ else:
                    "LlamaTokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
+            ("llava", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)),
            ("longformer", ("LongformerTokenizer", "LongformerTokenizerFast" if is_tokenizers_available() else None)),
            (
                "longt5",

--- a/src/transformers/models/llava/__init__.py
+++ b/src/transformers/models/llava/__init__.py
+# Copyright 2023 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.
+from typing import TYPE_CHECKING
+
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
+
+
+_import_structure = {"configuration_llava": ["LLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlavaConfig"]}
+
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_llava"] = [
+        "LLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "LlavaForConditionalGeneration",
+        "LlavaPreTrainedModel",
+    ]
+    _import_structure["processing_llava"] = ["LlavaProcessor"]
+
+
+if TYPE_CHECKING:
+    from .configuration_llava import LLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlavaConfig
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_llava import (
+            LLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            LlavaForConditionalGeneration,
+            LlavaPreTrainedModel,
+        )
+        from .processing_llava import LlavaProcessor
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
--- a/src/transformers/models/llava/configuration_llava.py
+++ b/src/transformers/models/llava/configuration_llava.py
+# coding=utf-8
+# Copyright 2023 Microsoft Research & University of Wisconsin-Madison and the 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.
+""" Llava model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto import CONFIG_MAPPING
+
+
+logger = logging.get_logger(__name__)
+
+LLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "llava-hf/llava-v1.5-7b": "https://huggingface.co/llava-hf/llava-v1.5-7b/resolve/main/config.json",
+}
+
+
+class LlavaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlavaForConditionalGeneration`]. It is used to instantiate an
+    Llava 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 Llava-9B.
+
+    e.g. [llava-hf/llava-9b](https://huggingface.co/llava-hf/llava-9b)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vision_config (`LlavaVisionConfig`,  *optional*):
+            Custom vision config or dict
+        text_config (`Union[AutoConfig, dict]`, *optional*):
+            The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
+        ignore_index (`int`, *optional*, defaults to -100):
+            The ignore index for the loss function.
+        image_token_index (`int`, *optional*, defaults to 32000):
+            The image token index to encode the image prompt.
+        projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+            The activation function used by the multimodal projector.
+        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+            The feature selection strategy used to select the vision feature from the CLIP backbone.
+        vision_feature_layer (`int`, *optional*, defaults to -2):
+            The index of the layer to select the vision feature.
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Llava model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`~LlavaForConditionalGeneration`]
+
+    Example:
+
+    ```python
+    >>> from transformers import LlavaForConditionalGeneration, LlavaConfig, CLIPVisionConfig, LlamaConfig
+
+    >>> # Initializing a CLIP-vision config
+    >>> vision_config = CLIPVisionConfig()
+
+    >>> # Initializing a Llama config
+    >>> text_config = LlamaConfig()
+
+    >>> # Initializing a Llava llava-1.5-7b style configuration
+    >>> configuration = LlavaConfig(vision_config, text_config)
+
+    >>> # Initializing a model from the llava-1.5-7b style configuration
+    >>> model = LlavaForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "llava"
+    is_composition = False
+
+    def __init__(
+        self,
+        vision_config=None,
+        text_config=None,
+        ignore_index=-100,
+        image_token_index=32000,
+        projector_hidden_act="gelu",
+        vision_feature_select_strategy="default",
+        vision_feature_layer=-2,
+        vocab_size=32000,
+        **kwargs,
+    ):
+        self.ignore_index = ignore_index
+        self.image_token_index = image_token_index
+        self.projector_hidden_act = projector_hidden_act
+        self.vision_feature_select_strategy = vision_feature_select_strategy
+        self.vision_feature_layer = vision_feature_layer
+        self.vocab_size = vocab_size
+
+        self.vision_config = vision_config
+
+        if isinstance(self.vision_config, dict):
+            vision_config["model_type"] = (
+                vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
+            )
+            self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
+        elif vision_config is None:
+            self.vision_config = CONFIG_MAPPING["clip_vision_model"](
+                intermediate_size=4096,
+                hidden_size=1024,
+                patch_size=14,
+                image_size=336,
+                num_hidden_layers=24,
+                num_attention_heads=16,
+                vocab_size=32000,
+                projection_dim=768,
+            )
+        self.vocab_size = self.vocab_size
+
+        self.text_config = text_config
+
+        if isinstance(self.text_config, dict):
+            text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
+            self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+            self.vocab_size = self.text_config.vocab_size
+        elif text_config is None:
+            self.text_config = CONFIG_MAPPING["llama"]()
+
+        super().__init__(**kwargs)
--- a/src/transformers/models/llava/convert_llava_weights_to_hf.py
+++ b/src/transformers/models/llava/convert_llava_weights_to_hf.py
+# Copyright 2023 The 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.
+import argparse
+
+import torch
+from huggingface_hub import hf_hub_download
+
+from transformers import (
+    AddedToken,
+    AutoConfig,
+    AutoTokenizer,
+    CLIPImageProcessor,
+    LlavaConfig,
+    LlavaForConditionalGeneration,
+    LlavaProcessor,
+)
+
+
+KEYS_TO_MODIFY_MAPPING = {
+    "model.vision_tower.": "",
+    "model.mm_projector": "multi_modal_projector",
+    "model": "model.model",
+    "vision_model.model": "vision_model",
+    "lm_head": "language_model.lm_head",
+    "model.model": "language_model.model",
+    "multi_modal_projector.0": "multi_modal_projector.linear_1",
+    "multi_modal_projector.2": "multi_modal_projector.linear_2",
+}
+
+
+def convert_state_dict_to_hf(state_dict):
+    new_state_dict = {}
+    for key, value in state_dict.items():
+        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
+            if key_to_modify in key:
+                key = key.replace(key_to_modify, new_key)
+
+        new_state_dict[key] = value
+    return new_state_dict
+
+
+def convert_llava_llama_to_hf(text_model_id, vision_model_id, output_hub_path, old_state_dict_id):
+    torch.set_default_dtype(torch.float16)
+    text_config = AutoConfig.from_pretrained(text_model_id)
+
+    tokenizer = AutoTokenizer.from_pretrained(text_model_id)
+    tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special=True)
+    tokenizer.add_special_tokens({"pad_token": "<pad>"})
+
+    image_processor = CLIPImageProcessor.from_pretrained(vision_model_id)
+
+    processor = LlavaProcessor(tokenizer=tokenizer, image_processor=image_processor)
+
+    config = LlavaConfig(text_config=text_config)
+    config.pad_token_id = 32001
+
+    with torch.device("meta"):
+        model = LlavaForConditionalGeneration(config)
+
+    # Pad to 64 for performance reasons
+    pad_shape = 64
+
+    state_dict_path = hf_hub_download(old_state_dict_id, "model_state_dict.bin")
+
+    state_dict = torch.load(state_dict_path, map_location="cpu")
+    state_dict = convert_state_dict_to_hf(state_dict)
+    model.load_state_dict(state_dict, strict=True, assign=True)
+
+    pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
+    mu = torch.mean(pre_expansion_embeddings, dim=0).float()
+    n = pre_expansion_embeddings.size()[0]
+    sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
+    dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
+
+    # We add an image token so we resize the model
+    model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape)
+    model.language_model.model.embed_tokens.weight.data[32000:] = torch.stack(
+        tuple((dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[32000:].shape[0]))),
+        dim=0,
+    )
+    model.language_model.lm_head.weight.data[32000:] = torch.stack(
+        tuple((dist.sample() for _ in range(model.language_model.lm_head.weight.data[32000:].shape[0]))),
+        dim=0,
+    )
+    model.config.vocab_size = model.config.vocab_size + pad_shape
+    model.config.text_config.vocab_size = model.config.text_config.vocab_size + pad_shape
+
+    model.push_to_hub(output_hub_path)
+    processor.push_to_hub(output_hub_path)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--text_model_id",
+        help="Hub location of the text model",
+    )
+    parser.add_argument(
+        "--vision_model_id",
+        help="Hub location of the vision model",
+    )
+    parser.add_argument(
+        "--output_hub_path",
+        help="Location on the hub of the converted model",
+    )
+    parser.add_argument(
+        "--old_state_dict_id",
+        help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`",
+    )
+    args = parser.parse_args()
+    convert_llava_llama_to_hf(args.text_model_id, args.vision_model_id, args.output_hub_path, args.old_state_dict_id)
+
+
+if __name__ == "__main__":
+    main()