LBL
Žádný odhad
Žádné hardwareové požadavky na tento model
Hmotnosti tohoto modelu nebyly publikovány, takže jej nelze stáhnout ani spustit na vlastním hardwaru v jakékoli velikosti. Je dostupný pouze prostřednictvím svého poskytovatele a žádná grafická karta to nezmění..
Záznam.
Plná specifikace
Všechno zaznamenané k tomuto modelu. Většina z toho popisuje, jak byl trénován, nikoli jak běží — užitečný kontext pro posouzení, kolik práce do něj bylo vloženo, a jak se srovnává s modely postavenými na jinou škálu.
Původ
Kdo vybudoval tento model, kde a kdy byl publikován.
- Organizace
- University College London (UCL)
- Typ organizace
- Academia
- Země
- United Kingdom of Great Britain and Northern Ireland
- Publikováno
- 27 June 2012
- Autoři
- Andriy Mnih, Yee Whye Teh
Co to dělá
Problémové oblasti, pro které byl model vyvinut. Model může obsahovat několik z každého.
- Doména
- Language
- Úkol
- Language modeling
Velikost
Jak velký je model a na kolik dat byl vytrénován. Parametry jsou číslo, které rozhoduje o tom, zda se vejde na danou grafickou kartu.
- Parametry
- 2M
- Tréninková data
- 929,000 tokens
- Epochy
- 45
Vocabulary size (V) = 10,000 words Context size (c) = 2 words Feature dimension (d) = 100 Plugging this into the parameter count: Context word features: 10,000 x 100 = 1,000,000 Target word features: 10,000 x 100 = 1,000,000 Context weights: 2 x 100 x 100 = 20,000 Total parameters: 1,000,000 + 1,000,000 + 20,000 = 2,020,000 So for the LBL model configuration used in the experiments in this paper, with a 10K word vocabulary, 2-word context, and 100D feature vectors, the total number of parameter…
The paper does not explicitly state the number of epochs used for training the LBL models. However, it provides some relevant information: In Section 4, it mentions that the learning rates were adapted at the end of each epoch based on the validation set perplexity. In Section 4.1, it states: "NCE took about twice as many epochs as ML to converge." For maximum likelihood (ML) training, the training time is reported as 21 hours. So we can deduce: ML training took 21 hours and NCE took twice …
Výpočet trénování
Aritmetika prováděná k trénování modelu, měřená v operacích s plovoucí desetinnou čárkou. Je to měřítko toho, kolik stál trénink, nikoli jak rychle hotový model na vás odpovídá.
- Výpočet trénování
- 5 × 10¹⁴ FLOP
- Jak bylo založeno
- Operation counting
6 FLOP / parameter / token * 2000000 parameters ['Likely' confidence] * 929000 tokens * 45 epochs ["Likely" confidence] = 5.0166e+14 FLOP
Dostupnost
Zda můžete získat model a spustit ho na svém vlastním hardwaru, což rozhoduje o tom, zda se na tuto stránku vztahují jakákoliv čísla grafických karet.
- Váhy
- Closed — provider access only
- Přístup k modelu
- Unreleased
- Tréninkový kód
- Unreleased
Jak je klasifikováno
Labely, které se na zdrojovém datovém souboru používají při sledování významných modelů, a jak moc si je jistý touto položkou.
- Záznam důvěry
- Likely
- Citace
- 835
- Benchmarková data
- LBL
Zdroje
Odkud tento záznam pochází a kdy byl naposled zkontrolován.
- Odkaz
- A Fast and Simple Algorithm for Training Neural Probabilistic Language Models
- Naposledy aktualizováno
- 28 November 2025
Co čísla znamenají
O tomto modelu
LBL was published by University College London (UCL), in United Kingdom of Great Britain and Northern Ireland, in June 2012. academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Jak byla trénována
The training run consumed about 5 × 10¹⁴ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 929,000 tokens.
Odpovědi
LBL — Časté dotazy
What GPU do I need to run LBL?
None. LBL 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 LBL open source?
No. LBL has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does LBL have?
LBL has 2M parameters. Vocabulary size (V) = 10,000 words Context size (c) = 2 words Feature dimension (d) = 100 Plugging this into the parameter count: Context word features: 10,000 x 100 = 1,000,000 Target word features: 10,000 x 100 = 1,000,000 Context weights: 2 x 100 x 100 = 20,000 Total parameters: 1,000,000 + 1,000,000 + 20,000 = 2,020,000 So for the LBL model configuration used in the experiments in this paper, with a 10K word vocabulary, 2-word context, and 100D feature vectors, the total number of parameters is approximately 2 million. 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 LBL?
LBL was published by University College London (UCL), based in United Kingdom of Great Britain and Northern Ireland, categorised as academia.
When was LBL released?
LBL was published in June 2012. 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 LBL used for?
LBL 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.
How much compute was used to train LBL?
Around 5 × 10¹⁴ FLOP. 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.
Druhý směr
Když se na to podíváte z druhé strany?
Tato stránka začíná modelem. Pokud již vlastníte kartu a chcete vědět, co vše bude schopna spustit, Začněte raději od hardwaru.