PermuteFormer
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
- Peking University
- Tipo de organización
- Academia
- País
- China
- Publicado
- 6 September 2021
- Autores
- Peng Chen
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
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
- 149.7M
- Datos de entrenamiento
- 103,000,000 tokens
- Épocas
- 30
Parameterization appears to be similar to a vanilla transformer. 6 layers, hidden dimension of 512, feed forward dimension of 1024, 8 attention heads. This would imply 20,447,232 parameters without embedding weights, and 512*vocab_size embedding weights (assuming tied embedding and unembedding projections) They appear to use word-level tokenization: "We evaluate unidirectional PermuteFormer on WikiText-103 (Merity et al., 2017). It is a language modeling dataset with about 103 million tokens," …
WikiText-103 is about 103M tokens
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
- 2.8 × 10¹⁸ FLOP
- Cómo se estableció
- Operation counting
6 * (30 * 103M) * 149,697,024 = 2.775e18 This seems a bit small relative to their statement: "It takes about 10 days on 8 V100 GPUs to get all the figures in this paper" which suggests about 2.7e20 FLOPs at 30% MFU. Table 2 indicates that Performer and PermuteFormer take 0.23x to 0.58x as long to train as a Transformer model. Figure 2 appears to be the most compute intensive figure, and would take about 4 * (2.775e18) + 1 * (2.775e18 / 0.365) = 1.9e19 FLOPs.
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 V100
- Chips usados
- 8
- Consumo de energía
- 4.8 kW
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
- Unreleased
- Código de entrenamiento
- Open source
no specific license. training code: https://github.com/cpcp1998/PermuteFormer/tree/master/language_model
Cómo se clasifica
Etiquetas que aplica el conjunto de datos de origen al rastrear modelos notables y cuán seguro está de la entrada.
- Por qué se rastrea
- SOTA improvement
- Registrar confianza
- Speculative
- Citas
- 23
- Benchmark data
- PermuteFormer
"Results show that PermuteFormer uniformly improves the performance of Performer, accelerates convergence, and achieves state-of-the-art on some tasks." "We conduct extensive experiments to evaluate PermuteFormer. It achieves strong empirical performance and obtains state-of-the-art on Long-Range Arena, a benchmark for efficient Transformers. It also improves the performance of Performer on language modeling tasks like WikiText-103"
Fuentes
De dónde proviene este registro y cuándo fue revisado por última vez.
- Referencia
- PermuteFormer: Efficient Relative Position Encoding for Long Sequences
- Última actualización
- 25 May 2026
Qué significan los números
Background
PermuteFormer was published by Peking University, in China, in September 2021. It comes out of academia.
It works in Language, and is recorded as doing language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Qué se utilizó para construirlo
The training run consumed about 2.8 × 10¹⁸ FLOP, on NVIDIA V100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 103,000,000 tokens went into training it.
Se rastrea en el conjunto de datos subyacente por una razón en particular: mejora sOTA.
Respuestas
PermuteFormer — Preguntas frecuentes
How much compute was used to train PermuteFormer?
Around 2.8 × 10¹⁸ FLOP, on NVIDIA V100. 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.
What GPU do I need to run PermuteFormer?
None. PermuteFormer 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 PermuteFormer open source?
No. PermuteFormer has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does PermuteFormer have?
PermuteFormer has 149.7M parameters. Parameterization appears to be similar to a vanilla transformer. 6 layers, hidden dimension of 512, feed forward dimension of 1024, 8 attention heads. This would imply 20,447,232 parameters without embedding weights, and 512*vocab_size embedding weights (assuming tied embedding and unembedding projections) They appear to use word-level tokenization: "We evaluate unidirectional PermuteFormer on WikiText-103 (Merity et al., 2017). It is a language modeling dataset with about 103 million tokens," and I confirmed that word-level tokenization results in about 102M words across train-test-validation. If this is the case, there are 267,735 unique words, so the embedding layer alone would be 137,080,320 parameters, for a total of 149,697,024. 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 PermuteFormer?
PermuteFormer was published by Peking University, based in China, categorised as academia.
When was PermuteFormer released?
PermuteFormer 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 PermuteFormer used for?
PermuteFormer works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
La otra dirección
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