HyperCLOVA 82B
Sin estimación
No hay requisitos de hardware para este modelo
Los pesos de este modelo no han sido publicados, por lo que no se puede descargar ni ejecutar en su propio hardware en ningún tamaño. Solo es accesible a través de su proveedor, y ninguna tarjeta gráfica lo cambia.
En registro
Especificación completa
Todo lo registrado para este modelo. La mayoría de ello describe cómo fue entrenado en lugar de cómo se ejecuta — contexto útil para juzgar cuánto trabajo se dedicó a ello y cómo se compara con modelos construidos a una escala diferente.
Origen
¿Quién construyó este modelo, dónde y cuándo fue publicado?
- Organización
- NAVER,Search Solutions
- Tipo de organización
- Industry,Industry
- País
- Korea (Republic of)
- Publicado
- 10 September 2021
- Autores
- Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Dong Hyeon Jeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon …
Lo que hace
Las áreas problemáticas para las que se construyó el modelo. Un modelo puede llevar varios de cada uno.
- Dominio
- Language
- Tarea
- Language modeling/generation, Chat, Translation, Text classification
- Enfoque
- Self-supervised learning
Tamaño
Qué tan grande es el modelo y cuántos datos se utilizaron para entrenarlo. Los parámetros son la cifra que decide si se ajusta en una tarjeta gráfica dada.
- Parámetros
- 82B
- Datos de entrenamiento
- 300,000,000,000 tokens
"We introduce a Korean in-context large-scale LM with 82B parameters, i.e., HyperCLOVA. This is the first discovery on near 100B-scale non-English LM." According to media reports, HyperCLOVA has 204B parameters (i.e. a different version than in the paper) https://m.koreaherald.com/view.php?ud=20210525000824
"However, experiments in Section 5.2 use the model trained with 300B tokens, as HyperCLOVA Studio provided the 39B and 82B models trained with 300B tokens." "We introduce HyperCLOVA, a large-scale Korean in-context learning-based LM with nearly 100B parameters, by constructing a large Korean-centric corpus of 560B tokens." Based on tokenizing the Hyperclova article itself using OpenAI's tiktoken BPE tokenizer (https://github.com/openai/tiktoken), there are 3285 tokens for 1069 words - about 3 …
Entrenamiento de computación
La aritmética realizada para entrenar el modelo, medida en operaciones de punto flotante. Es una medida de lo que costó la ejecución del entrenamiento, no de cuán rápido responde el modelo terminado.
- Entrenamiento de computación
- 1.5 × 10²³ FLOP
- Cómo se estableció
- Operation counting,Hardware
"For experiments in Section 4, the model trained with 150B is used for fair comparison, because not all models are finished training at the same iteration. However, experiments in Section 5.2 use the model trained with 300B tokens, as HyperCLOVA Studio provided the 39B and 82B models trained with 300B tokens." 82e9 connections * 2 FLOP/connection * 300e9 tokens * 3 backward pass = 1.476e23 FLOP Calculation using GPU time corroborates this: - "Our model is based on megatron-LM (Shoeybi et al., …
El entrenamiento
Lo que se necesitó físicamente para entrenar: qué chips, cuántos, durante cuánto tiempo y qué consumió de la pared.
- Hardware de entrenamiento
- NVIDIA A100
- Chips usados
- 1,024
- Horas de chip
- 658,637
- Tiempo en tiempo real
- 643 hours (26.8 days)
- Hardware utilisation
- MFU 20.0%
- Consumo de energía
- 827.0 kW
- Costo de computación
- $586,409
see compute notes
6ND method gives 1.476e23 FLOP needed to train the model. Actual use was 1,024 A100 GPUs for 13.4 days for 150B tokens; 300B model probably took twice as long. 26.8 * 24 * 3600 * 1024 * 3.12e14 = 7.3978e23 1.476e23 / 7.3978e23 MFU = 0.1995
Disponibilidad
Si puedes obtener el modelo y ejecutarlo en tu propio hardware, lo cual es lo que decide si alguna de las cifras de la tarjeta gráfica en esta página aplica.
- Pesos
- Closed — provider access only
- Acceso al modelo
- API access
- Código de entrenamiento
- Unreleased
"We introduce HyperCLOVA Studio, an interactive prompt engineering interface which provides GUI and API interfaces like the OpenAI playground1"
Cómo se clasifica
Etiquetas que aplica el conjunto de datos de origen al rastrear modelos notables y cuán seguro está de la entrada.
- Modelo base
- Yes
- Probablemente por encima de 10²³ FLOP
- Yes
- Por qué se rastrea
- SOTA improvement
- Registrar confianza
- Confident
- Citas
- 131
"HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean" I don't see any standard benchmark that they claim SOTA on
Fuentes
De dónde proviene este registro y cuándo fue revisado por última vez.
- Referencia
- What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
- Última actualización
- 25 May 2026
Qué significan los números
Qué es este modelo
HyperCLOVA 82B was published by NAVER,Search Solutions, in Korea (Republic of), in September 2021. The organisation is categorised as industry,Industry.
It works in Language, and is recorded as doing language modeling/generation, Chat, Translation, Text classification.
Debido a que los pesos no están disponibles, ninguna de las cifras de hardware en otro lugar de este sitio se aplica a él.
Training and provenance
The training run consumed about 1.5 × 10²³ FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 300,000,000,000 tokens.
Its inclusion criterion is sOTA improvement.
Respuestas
HyperCLOVA 82B — Preguntas frecuentes
What GPU do I need to run HyperCLOVA 82B?
None. HyperCLOVA 82B is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is HyperCLOVA 82B open source?
No. HyperCLOVA 82B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does HyperCLOVA 82B have?
HyperCLOVA 82B has 82B parameters. "We introduce a Korean in-context large-scale LM with 82B parameters, i.e., HyperCLOVA. This is the first discovery on near 100B-scale non-English LM." According to media reports, HyperCLOVA has 204B parameters (i.e. a different version than in the paper) https://m.koreaherald.com/view.php?ud=20210525000824. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created HyperCLOVA 82B?
HyperCLOVA 82B was published by NAVER,Search Solutions, based in Korea (Republic of), categorised as industry,Industry.
When was HyperCLOVA 82B released?
HyperCLOVA 82B was published in September 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is HyperCLOVA 82B used for?
HyperCLOVA 82B works in Language, and is recorded as handling language modeling/generation, Chat, Translation, Text classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train HyperCLOVA 82B?
Around 1.5 × 10²³ FLOP, on NVIDIA A100. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
La otra dirección
¿Mirándolo desde el otro lado?
Esta página comienza desde el modelo. Si ya posee una tarjeta y desea saber todo lo que podrá ejecutar, empiece por el hardware en su lugar.