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chinchilla language model wikipedia jump to content main menu main menu move to sidebar hide navigation main page contents current events random article about wikipedia contact us contribute help learn to edit community portal recent changes upload file special pages search search appearance donate create account log in personal tools donate create account log in contents move to sidebar hide top 1 models 2 architecture 3 see also 4 references toggle the table of contents chinchilla language model 6 languages العربية català español فارسی ido русский edit links article talk english read edit view history tools tools move to sidebar hide actions read edit view history general what links here related changes upload file permanent link page information cite this page get shortened url switch to legacy parser print export download as pdf printable version in other projects wikidata item appearance move to sidebar hide from wikipedia the free encyclopedia language model by deepmind this article may require cleanup to meet wikipedia s quality standards the specific problem is writing style does not match wikipedia s standards of prose please help improve this article if you can march 2026 learn how and when to remove this message chinchilla is a family of large language models llms developed by the research team at google deepmind presented in march 2022 1 models edit it is named chinchilla because it is a further development over a previous model family named gopher both model families were trained in order to investigate the scaling laws of large language models 2 it claimed to outperform gpt 3 it considerably simplifies downstream utilization because it requires much less computer power for inference and fine tuning based on the training of previously employed language models it has been determined that if one doubles the model size one must also have twice the number of training tokens this hypothesis has been used to train chinchilla by deepmind similar to gopher in terms of cost chinchilla has 70b parameters and four times as much data 1 4t tokens vs 300b 3 chinchilla has an average accuracy of 67 5 on the measuring massive multitask language understanding mmlu benchmark which is 7 higher than gopher s performance chinchilla was still in the testing phase as of january 12 2023 4 chinchilla contributes to developing an effective training paradigm for large autoregressive language models with limited compute resources the chinchilla team recommends that the number of training tokens is twice for every model size doubling meaning that using larger higher quality training datasets can lead to better results on downstream tasks 5 6 it has been used for the flamingo vision language model 7 architecture edit both the gopher family and chinchilla family are families of transformer models in particular they are essentially the same as gpt 2 with different sizes and minor modifications gopher family uses rmsnorm instead of layernorm relative positional encoding rather than absolute positional encoding the chinchilla family is the same as the gopher family but trained with adamw instead of adam optimizer the gopher family contains six models of increasing size from 44 million parameters to 280 billion parameters they refer to the largest one as gopher by default similar naming conventions apply for the chinchilla family table 1 of 2 shows the entire gopher family model specifications for gopher family parameter count layers number of heads key value size internal dimension max learning rate batch size 44m 8 16 32 512 6 10 4 0 25m 117m 12 12 64 768 6 10 4 0 25m 417m 12 12 128 1 536 2 10 4 0 25m 1 4b 24 16 128 2 048 2 10 4 0 25m 7 1b 32 32 128 4 096 1 2 10 4 2m gopher 280b 80 128 128 16 384 4 10 5 3m 6m table 4 of 1 compares the 70 billion parameter chinchilla with gopher 280b comparison between chinchilla and gopher parameter count layers number of heads key value size internal dimension max learning rate batch size gopher 280b 80 128 128 16 384 4 10 5 3m 6m chinchilla 70b 80 64 128 8 192 1 10 4 1 5m 3m see also edit lamda references edit 1 2 hoffmann jordan borgeaud sebastian mensch arthur buchatskaya elena cai trevor rutherford eliza casas diego de las hendricks lisa anne welbl johannes clark aidan hennigan tom noland eric millican katie driessche george van den damoc bogdan 2022 03 29 training compute optimal large language models arxiv 2203 15556 cs cl 1 2 rae jack w borgeaud sebastian cai trevor millican katie hoffmann jordan song francis aslanides john henderson sarah ring roman young susannah rutherford eliza hennigan tom menick jacob cassirer albin powell richard 2022 01 21 scaling language models methods analysis insights from training gopher arxiv 2112 11446 cs cl eliaçık eray january 12 2023 chinchilla ai is coming for the gpt 3 s throne dataconomy archived from the original on march 26 2023 hendrycks dan 2023 03 14 measuring massive multitask language understanding archived from the original on 2023 03 15 retrieved 2023 03 15 chaithali g april 9 2022 check out this deepmind s new language model chinchilla 70b parameters which significantly outperforms gopher 280b and gpt 3 175b on a large range of downstream evaluation tasks archived from the original on march 27 2023 retrieved january 15 2023 wali kartik april 12 2022 deepmind launches gpt 3 rival chinchilla analytics india magazine archived from the original on march 26 2023 retrieved january 15 2023 alayrac jean baptiste donahue jeff luc pauline miech antoine barr iain hasson yana lenc karel mensch arthur millican katherine reynolds malcolm ring roman rutherford eliza cabi serkan han tengda gong zhitao 2022 12 06 flamingo a visual language model for few shot learning advances in neural information processing systems 35 23716 23736 arxiv 2204 14198 v t e google ai google google brain google deepmind computer programs alphago versions alphago 2015 master 2016 alphago zero 2017 alphazero 2017 muzero 2019 competitions fan hui 2015 lee sedol 2016 ke jie 2017 in popular culture alphago 2017 other alphafold 2018 alphastar 2019 alphatensor 2022 alphadev 2023 funsearch 2023 alphageometry 2024 alphaproof 2024 alphaevolve 2025 alphagenome 2025 machine learning neural networks inception 2014 wavenet 2016 mobilenet 2017 transformer 2017 efficientnet 2019 gato 2022 other quantum artificial intelligence lab tensorflow tensor processing unit generative ai chatbots assistant 2016 sparrow 2022 gemini 2023 nano banana 2025 models bert 2018 xlnet 2019 t5 2019 lamda 2021 chinchilla 2022 palm 2022 imagen 2023 gemini 2023 videopoet 2024 gemma 2024 genie 2024 veo 2024 other dreambooth 2022 notebooklm 2023 vids 2024 gemini robotics 2025 antigravity 2025 see also attention is all you need future of go summit generative pre trained transformer google labs google workspace category commons v t e large language models llms list of llms ai companies benchmarks list of chatbots foundation model generative ai concepts language model nlp nlg computational linguistics foundation model small language model reasoning model generative pre trained transformer transformer attention kv cache context window tokenization word embedding parameter hyperparameter autoregression mixture of experts moe inference model compression knowledge distillation speculative decoding pagedattention neural scaling law multimodality open weights training prompting and alignment self supervised learning supervised learning fine tuning instruction tuning rlhf constitutional ai ai alignment ai safety mechanistic interpretability prompt engineering in context learning chain of thought prompting rag prompt injection adversarial machine learning hallucination stochastic parrot glitch token generative engine optimization models apertus bert bloom chinchilla claude dbrx deepseek llm gemini gemma glove glm gpt gpt j pangu granite hy inkling jais lamda laguna s llama mimo minerva mistral and mixtral muse spark muse glimmer nemotron palm phi qwen seq2seq t5 vicuna word2vec xlnet chatbots and assistants amazon q chatgpt character ai claude claude code deepseek doubao ernie bot gemini grok kimi kimi code lumo microsoft copilot meta ai perplexity ai sparrow you com agents coding and applications ai agent intelligent agent autogpt crewai langchain manus model context protocol agent2agent openai codex vibe coding code generation question answering machine translation text summarization chatbot virtual assistant software pytorch tensorflow hugging face llama cpp lm studio ollama sglang tensorrt llm vllm onnx openvino vector database chromadb deep learning software open source ai software hardware and infrastructure ai data center cuda gpu high bandwidth memory neural processing unit tpu benchmarks evaluation and detection language model benchmark mmlu humanity s last exam lmarena llm as a judge perplexity metric gptzero artificial intelligence content detection undetectable ai datasets and data data set text corpus common crawl the pile web scraping synthetic data training validation and test data sets organizations ai21 labs alibaba anthropic baidu cohere deepseek eleutherai google deepmind hugging face meta ai microsoft ai mistral ai minimax moonshot ai nvidia openai openrouter sarvam ai stepfun technology innovation institute thinking machines lab xai people sam altman dario amodei yoshua bengio aidan gomez demis hassabis geoffrey hinton andrej karpathy yann lecun percy liang liang wenfeng christopher d manning arthur mensch mira murati alec radford noam shazeer ilya sutskever ashish vaswani andrew ng social economic and governance ai boom ai bubble ai slop ai anthropomorphism ai arms race chatbot psychosis competition copyright deaths linked to chatbots dependency gaid environmental impact regulation ethics existential risk in education in healthcare workplace impact category large language models v t e artificial intelligence ai history timeline glossary lists algorithms companies institutions projects software open source proprietary concepts automated reasoning automated planning constraint satisfaction knowledge representation parameter hyperparameter loss functions regression bias variance tradeoff double descent overfitting clustering gradient descent sgd quasi newton method conjugate gradient method backpropagation attention convolution normalization batchnorm activation softmax sigmoid rectifier gating weight initialization regularization datasets augmentation prompt engineering reinforcement learning q learning sarsa imitation policy gradient diffusion latent diffusion model autoregression adversary rag uncanny valley llm post training rlhf self supervised learning reflection recursive self improvement hallucination word embedding vibe coding blended ai open source ai sovereign ai symbolic ai neuro symbolic ai situated approach actor critic algorithm applications automated theorem proving general game playing machine learning in context learning artificial neural network deep learning language model large nmt reasoning model context protocol intelligent agent ai agent artificial human companion humanity s last exam lethal autonomous weapons laws generative ai weak ai hypothetical artificial general intelligence agi artificial superintelligence asi agent2agent protocol physical ai implementations audio visual alexnet wavenet human image synthesis hwr ocr computer vision speech synthesis 15 ai elevenlabs speech recognition whisper facial recognition alphafold text to image models aurora dall e firefly flux gpt image ideogram imagen midjourney recraft stable diffusion text to video models dream machine runway gen hailuo ai kling sora seedance veo music generation riffusion suno udio world models genie oasis text list of large language models project debater ibm watson ibm watsonx decisional alphago alphazero openai five self driving car muzero action selection autogpt robot control reasoning systems deductive classifiers expert systems inference engines knowledge based systems logic programs procedural reasoning systems semantic reasoners rule based systems cognitive architectures act r soar clarion lida opencog knowledge bases conceptnet wikidata dbpedia yago people alan turing warren sturgis mcculloch walter pitts john von neumann christopher d manning claude shannon shun ichi amari kunihiko fukushima takeo kanade marvin minsky john mccarthy nathaniel rochester allen newell cliff shaw herbert a simon oliver selfridge frank rosenblatt bernard widrow joseph weizenbaum seymour papert seppo linnainmaa paul werbos geoffrey hinton john hopfield jürgen schmidhuber yann lecun yoshua bengio lotfi a zadeh stephen grossberg alex graves james goodnight andrew ng fei fei li alex krizhevsky ilya sutskever oriol vinyals quoc v le ian goodfellow demis hassabis david silver andrej karpathy ashish vaswani noam shazeer aidan gomez john schulman mustafa suleyman jan leike daniel kokotajlo françois chollet neural network architectures neural turing machine differentiable neural computer transformer vision transformer vit recurrent neural network rnn long short term memory lstm gated recurrent unit gru echo state network multilayer perceptron mlp convolutional neural network cnn residual neural network rnn highway network mamba autoencoder variational autoencoder vae generative adversarial network gan graph neural network gnn political ai cold war ai in government ai safety alignment ai takeover elections ethics of ai eu ai act nationalism precautionary principle regulation of ai us virtual politician propaganda opposition to ai data centers social and economic ai boom ai bubble ai data center ai effect ai infrastructure ai literacy ai slop ai winter anthropomorphism arms race competition environmental impact explainable ai generative engine optimization in architecture in education in fiction in healthcare chatbot psychosis in marketing in video games in visual art military applications ai warfare workplace impact category retrieved from https en wikipedia org w index php title chinchilla_ language_model oldid 1360908667 categories chatbots google deepmind large language models 2022 in artificial intelligence generative pre trained transformers hidden categories articles with short description short description matches wikidata articles needing cleanup from march 2026 all pages needing cleanup wikipedia pages needing cleanup from march 2026 this page was last edited on 24 june 2026 at 12 04 utc page was rendered with parsoid text is available under the creative commons attribution sharealike 4 0 license additional terms may apply by using this site you agree to the terms of use and privacy policy wikipedia is a registered trademark of the wikimedia foundation inc a non profit organization privacy policy about wikipedia disclaimers contact wikipedia legal safety contacts code of conduct developers statistics cookie statement mobile view search search toggle the table of contents chinchilla language model 6 languages add 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