UO

AWD-LSTM + MoS + Partial Shuffled

Language Open Weights
35M
Parameters

About AWD-LSTM + MoS + Partial Shuffled

AWD-LSTM + MoS + Partial Shuffled is an AI model developed by University of Texas at Austin, in the language category, released in 2019, made available as an open-weights model with 0B params.

On this page you'll find AWD-LSTM + MoS + Partial Shuffled's full specifications. Review provider pricing and benchmark scores below, or compare AWD-LSTM + MoS + Partial Shuffled head-to-head with other language models.

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Sized to what AWD-LSTM + MoS + Partial Shuffled actually needs (~8k context at Q4), not to the biggest GPU: Good = cheapest that runs it, Better = best value, Best = most headroom. Speed is a hardware estimate; anything past ~120 tok/s is shown as instant.

Software support

✓ measured · · compatible · — not supported. Informational only — speed is hardware-based.

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Frequently asked questions

What is AWD-LSTM + MoS + Partial Shuffled?

AWD-LSTM + MoS + Partial Shuffled is an AI model developed by University of Texas at Austin, in the language category, released in 2019. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.

Who created AWD-LSTM + MoS + Partial Shuffled?

AWD-LSTM + MoS + Partial Shuffled was developed by University of Texas at Austin and released in 2019.

Is AWD-LSTM + MoS + Partial Shuffled open source or proprietary?

AWD-LSTM + MoS + Partial Shuffled ships as an open-weights model: you can download and self-host the weights, though the licence may place some restrictions on use.

How much does AWD-LSTM + MoS + Partial Shuffled cost?

Pricing for AWD-LSTM + MoS + Partial Shuffled depends on the provider. See the providers table on this page for the latest API rates.

How does AWD-LSTM + MoS + Partial Shuffled perform on benchmarks?

Benchmark scores for AWD-LSTM + MoS + Partial Shuffled are listed on this page as they are published. You can compare it head-to-head with other models on the GenAIList compare tool.

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