InstructRetro

Retro (Borgeaud et al., 2022) is an autoregressive decoder-only language model (LM) pretrained with retrieval-augmentation.\nRetro features practical scalibility to support large-scale pretraining from scratch by retrieving from trillions of token.\nPretraining with retrieval provides a more efficient storage mechanism of factual knowledge, when compared to storing factual knowledge implicitly within the network's parameters, thus largely reducing model parameters while achieving lower perplexity than standard GPT.\nRetro also provides the flexibility to update the\nknowledge stored in LMs (Wang et al., 2023a)\nby updating the retrieval database without training LMs again.

InstructRetro (Wang et al., 2023b) further scales up the size of Retro to 48B, featuring the largest LLM pretrained with retrieval (as of December 2023).\nThe obtained foundation model, Retro 48B, largely outperforms the GPT counterpart in terms of perplexity.\nWith instruction tuning on Retro, InstructRetro demonstrates significant improvement over the instruction tuned GPT on downstream tasks in the zero-shot setting. Specifically, the average improvement of InstructRetro is 7% over its GPT counterpart across 8 short-form QA tasks, and 10% over GPT across 4 challenging long-form QA tasks. We also find that one can ablate the encoder from InstructRetro architecture and directly use the InstructRetro decoder backbone as GPT, while achieving comparable results.

Model Overview

License

The use of this model is governed by the NVIDIA AI Foundation Models Community License Agreement.

Supported Hardware

H100
A100 80GB, A100 40GB

Model Version(s)

retro-48b-instruct-4k: Pretrained Retro 48B LM with instruction tuning.

Toolkit

Megatron-LM Framework

Environment

We recommend using docker environment to run the code.

Docker image

We provide a docker build file in Dockerfile for the reproduction. The docker image is based on nvcr.io/nvidia/pytorch:23.09-py3.

Install dependencies

Clone the Megatron repo:

git clone --branch InstructRetro https://github.com/NVIDIA/Megatron-LM.git\n

If docker is not available, we recommend starting from a clean conda environment with the following runtime dependencies:

Python 3.10
NVIDIA CUDA® 12.2.1
NVIDIA cuBLAS 12.2.5.6
NVIDIA cuDNN 8.9.5
NVIDIA NCCL 2.18.5
PyTorch 2.1.0a0+32f93b1

Then install Retro-specific dependencies, including:

pip install -U faiss-gpu\npip install -U transformers\npip install -U sentencepiece\npip install -U h5py\npip install -U nltk\npip install -U einops\n

Evaluation Command

Download our model checkpoint and tokenizer.

Specify the blank args in the tools/retro/text_generation/retro_generate.sh script, including model path, Retro workdir, and model related params.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Parameter	Value	Explanation
mod_par	8	Tensor parallelism
layers	48	Number of layers in the model
hid_dim	8192	Hidden dimension size
heads	64	Number of attention heads
pip_par	1	Pipeline parallelism

We present an example command to run retro generation with the InstructRetro checkpoints for the Natural Question (NQ) task. The example command is for the 48b InstructRetro. Please specify the directory for the NQ dataset and update the command accordingly for other checkpoints.

2\n\">

bash tools/retro/text_generation/retro_generate.sh nq 48b greedy test  0 20000 1000 5 pp1 <path/to/checkpoint> 2\n

The generated responses will be saved in the corresponding checkpoint directory. For example, for the 48b InstructRetro, it will be saved to\n<path/to/retro>/retro-generate-nq_5_2_48b_test_greedy_0_20000_1000.txt.

To evaluate the F1 / Exact Match (EM) scores of the generated responses, we provide an example script to run the evaluation on the NQ dataset. Please specify the directory for the NQ dataset and update the command accordingly for other checkpoints and downstream tasks.

python3 tools/retro/text_generation/evaluate.py\n

Citations

See more details from our papers:

Shall we Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study.

Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro. (EMNLP 2023)

InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining.

Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro.

Please cite the papers as follows if you use the data or code from this repo:

@inproceedings{wang2023shall,\n    title   = {Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study},\n    author  = {Boxin Wang and Wei Ping and Peng Xu and Lawrence McAfee and Zihan Liu and Mohammad Shoeybi and Yi Dong and Oleksii Kuchaiev and Bo Li and Chaowei Xiao and Anima Anandkumar and Bryan Catanzaro},\n    journal = {The 2023 Conference on Empirical Methods in Natural Language Processing},\n    year    = {2023}\n}\n\n@article{wang2023instructretro,\n    title   = {InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining},\n    author  = {Boxin Wang and Wei Ping and Lawrence McAfee and Peng Xu and Bo Li and Mohammad Shoeybi and Bryan Catanzaro},\n    year    = {2023},\n    journal = {arXiv preprint arXiv: 2310.07713}\n}\n

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Specifically, the average improvement of InstructRetro is 7% over its GPT counterpart across 8 short-form QA tasks, and 10% over GPT across 4 challenging long-form QA tasks. We also find that one can ablate the encoder from InstructRetro architecture and directly use the InstructRetro decoder backbone as GPT, while achieving comparable results.\n\n### Model Overview\n\n#### License\n\nThe use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license).\n\n#### Supported Hardware\n\n- H100\n- A100 80GB, A100 40GB\n\n#### Model Version(s)\n\n`retro-48b-instruct-4k`: Pretrained Retro 48B LM with instruction tuning.\n\n#### Toolkit\n\n[Megatron-LM Framework](https://github.com/NVIDIA/Megatron-LM/tree/InstructRetro)\n\n### Environment\n\nWe recommend using docker environment to run the code.\n\n#### Docker image\n\nWe provide a docker build file in [Dockerfile](https://github.com/NVIDIA/Megatron-LM/blob/InstructRetro/tools/retro/examples/Dockerfile) for the reproduction. The docker image is based on `nvcr.io/nvidia/pytorch:23.09-py3`.\n\n#### Install dependencies\n\nClone the Megatron repo:\n\n```bash\ngit clone --branch InstructRetro https://github.com/NVIDIA/Megatron-LM.git\n```\n\nIf docker is not available, we recommend starting from a clean conda environment with the following runtime dependencies:\n\n- Python 3.10\n- NVIDIA CUDA® 12.2.1\n- NVIDIA cuBLAS 12.2.5.6\n- NVIDIA cuDNN 8.9.5\n- NVIDIA NCCL 2.18.5\n- PyTorch 2.1.0a0+32f93b1\n\nThen install Retro-specific dependencies, including:\n\n```bash\npip install -U faiss-gpu\npip install -U transformers\npip install -U sentencepiece\npip install -U h5py\npip install -U nltk\npip install -U einops\n```\n\n### Evaluation Command\n\nDownload our model checkpoint and tokenizer.\n\nSpecify the blank args in the [tools/retro/text_generation/retro_generate.sh](https://github.com/NVIDIA/Megatron-LM/blob/InstructRetro/tools/retro/text_generation/retro_generate.sh) script, including model path, Retro workdir, and model related params.\n\n| Parameter | Value | Explanation |\n|-----------|-------|-----------------------------------|\n| mod_par | 8 | Tensor parallelism |\n| layers | 48 | Number of layers in the model |\n| hid_dim | 8192 | Hidden dimension size |\n| heads | 64 | Number of attention heads |\n| pip_par | 1 | Pipeline parallelism |\n\nWe present an example command to run retro generation with the InstructRetro checkpoints for the Natural Question (NQ) task. The example command is for the 48b InstructRetro. Please specify the directory for the NQ dataset and update the command accordingly for other checkpoints.\n\n```\nbash tools/retro/text_generation/retro_generate.sh nq 48b greedy test 0 20000 1000 5 pp1 2\n```\n\nThe generated responses will be saved in the corresponding checkpoint directory. For example, for the 48b InstructRetro, it will be saved to\n`/retro-generate-nq_5_2_48b_test_greedy_0_20000_1000.txt`.\n\nTo evaluate the F1 / Exact Match (EM) scores of the generated responses, we provide an example script to run the evaluation on the NQ dataset. Please specify the directory for the NQ dataset and update the command accordingly for other checkpoints and downstream tasks.\n\n```bash\npython3 tools/retro/text_generation/evaluate.py\n```\n\n## Citations\n\nSee more details from our papers:\n\n[Shall we Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study.](https://arxiv.org/abs/2304.06762)\n\n_Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro._ (EMNLP 2023)\n\n[InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining.](https://arxiv.org/abs/2310.07713)\n\n_Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro._\n\nPlease cite the papers as follows if you use the data or code from this repo:\n\n```bibtex\n@inproceedings{wang2023shall,\n title = {Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study},\n author = {Boxin Wang and Wei Ping and Peng Xu and Lawrence McAfee and Zihan Liu and Mohammad Shoeybi and Yi Dong and Oleksii Kuchaiev and Bo Li and Chaowei Xiao and Anima Anandkumar and Bryan Catanzaro},\n journal = {The 2023 Conference on Empirical Methods in Natural Language Processing},\n year = {2023}\n}\n\n@article{wang2023instructretro,\n title = {InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining},\n author = {Boxin Wang and Wei Ping and Lawrence McAfee and Peng Xu and Bo Li and Mohammad Shoeybi and Bryan Catanzaro},\n year = {2023},\n journal = {arXiv preprint arXiv: 2310.07713}\n}\n```","displayName",["D",1706053258588],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/retro-48b-instruct-4k","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-and-AI.png","canGuestDownload","latestVersionSizeInBytes",76810537902,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244,245,246,247,248,249,250,251,252],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_211":287,"_254":288,"_256":289,"_258":259},{"_211":284,"_254":285,"_256":286,"_258":259},{"_211":281,"_254":282,"_256":283,"_258":259},{"_211":277,"_254":278,"_256":279,"_258":259},{"_211":274,"_254":274,"_256":275,"_258":259},{"_211":271,"_254":272,"_256":273,"_258":259},{"_211":269,"_254":269,"_256":270,"_258":259},{"_211":265,"_254":266,"_256":267,"_258":259},{"_211":262,"_254":262,"_256":263,"_258":259},{"_211":253,"_254":255,"_256":257,"_258":259},"Question Answering","value","uscs_question_answering","filter",{"_260":261,"_254":255},"category","label","field","application","PyTorch",{"_260":264,"_254":262},"framework","NVIDIA AI","soln_nvidia_ai",{"_260":268,"_254":266},"technology","Megatron-LM",{"_260":264,"_254":269},"Language Modeling","uscs_language_modeling",{"_260":261,"_254":272},"English",{"_260":276,"_254":274},"language","CUDA Toolkit","pltfm_cuda_toolkit",{"_260":280,"_254":278},"platform","CUDA","pltfm_cuda",{"_260":280,"_254":282},"Conversational AI","soln_conversational_ai",{"_260":268,"_254":285},"AI","soln_ai",{"_260":268,"_254":288},{"_122":1268,"_124":1269},"ngcApps",[1221],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1202":1203,"_1204":1205,"_1206":1207,"_1208":1209,"_1210":301,"_153":327,"_1211":1212,"_1213":59,"_1214":101,"_1215":101,"_1216":1217,"_1218":101,"_1219":1220},"amplitude",{"_1199":1200,"_839":-7,"_1201":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1193":1194,"_1195":1196,"_1197":1198},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789969289832,"1.17.0","defaultSearchSuggestions",{"_1079":228,"_1080":1081,"_1082":1083,"_1084":92},"announcements",[],"consentState",{"_365":1069,"_1070":34,"_1071":34,"_1072":101,"_1073":1074},"consentConfig",{"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366},"showContactAdmin",[],[],[340,341,342,343,344,345],{"_48":63,"_50":74,"_52":117,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":117,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":117,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":117,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":346,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620],[567,568,569,570,571],{"_406":481,"_211":481,"_181":228,"_379":591},{"_406":455,"_211":455,"_181":228,"_379":586},{"_406":581,"_211":581,"_181":228,"_379":582},{"_406":576,"_211":576,"_181":228,"_379":577},{"_406":572,"_211":572,"_181":228,"_379":573},"nvidia.partners",[574],{"_78":575,"_412":572,"_413":34,"_181":449},"edge","app.hushly.com",[578],{"_78":579,"_412":576,"_413":34,"_181":580},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[583],{"_78":584,"_412":581,"_413":34,"_181":585},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707],[659,660,661,662],{"_406":674,"_211":674,"_181":228,"_379":675},{"_406":669,"_211":669,"_181":228,"_379":670},{"_406":468,"_211":468,"_181":228,"_379":666},{"_406":581,"_211":581,"_181":228,"_379":663},[664],{"_78":665,"_412":581,"_413":101,"_181":585},"_gh_sess",[667],{"_78":668,"_412":468,"_413":101,"_181":475},"YSC","bizible.com",[671],{"_78":672,"_412":669,"_413":34,"_181":673},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[676],{"_78":677,"_412":674,"_413":34,"_181":678},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":749,"_412":527,"_413":34,"_181":750},{"_78":734,"_412":748,"_413":101,"_181":735},{"_78":717,"_412":530,"_413":34,"_181":642},{"_78":717,"_412":630,"_413":34,"_181":642},{"_78":746,"_412":527,"_413":34,"_181":747},{"_78":734,"_412":533,"_413":101,"_181":735},{"_78":744,"_412":527,"_413":34,"_181":745},{"_78":734,"_412":530,"_413":101,"_181":735},{"_78":742,"_412":629,"_413":34,"_181":743},{"_78":734,"_412":651,"_413":101,"_181":735},{"_78":714,"_412":530,"_413":34,"_181":716},{"_78":740,"_412":527,"_413":101,"_181":741},{"_78":738,"_412":527,"_413":34,"_181":739},{"_78":736,"_412":527,"_413":101,"_181":737},{"_78":734,"_412":625,"_413":101,"_181":735},{"_78":732,"_412":527,"_413":34,"_181":733},{"_78":730,"_412":527,"_413":34,"_181":731},{"_78":728,"_412":527,"_413":101,"_181":729},{"_78":726,"_412":634,"_413":34,"_181":727},{"_78":714,"_412":625,"_413":34,"_181":716},{"_78":724,"_412":527,"_413":34,"_181":725},{"_78":722,"_412":527,"_413":101,"_181":723},{"_78":720,"_412":527,"_413":34,"_181":721},{"_78":718,"_412":527,"_413":34,"_181":719},{"_78":717,"_412":527,"_413":34,"_181":642},{"_78":714,"_412":715,"_413":34,"_181":716},{"_78":712,"_412":527,"_413":34,"_181":713},{"_78":710,"_412":527,"_413":101,"_181":711},{"_78":708,"_412":527,"_413":34,"_181":709},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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