Llmlingua 2 Xlm Roberta Large Meetingbank
Developed by Microsoft, Llmlingua 2 Xlm Roberta Large Meetingbank is a chat model exceling at text generation, particularly in tasks such as question answering and summarization over compressed meeting transcripts. Its notable technical trait is a context window of 514 tokens, allowing it to process and understand lengthy inputs.
Input
Output
Context
1K
Max Output
-
Parameters
558.9M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Llmlingua 2 Xlm Roberta Large Meetingbank
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Llmlingua 2 Xlm Roberta Large Meetingbank
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llmlingua 2 Xlm Roberta Large Meetingbank advertises a context window of 514 tokens. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Llmlingua 2 Xlm Roberta Large Meetingbank is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Llmlingua 2 Xlm Roberta Large Meetingbank accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Llmlingua 2 Xlm Roberta Large Meetingbank is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Llmlingua 2 Xlm Roberta Large Meetingbank is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.9 GB at INT8 for the weights and a default context, from the catalog's 558.9M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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