HU
Harvard University
Language model · United States ·Mar 2019

UniRep

Language Open Weights
18.2M
Parameters

About UniRep

UniRep is an AI model developed by Harvard University, in the language category, released in 2019, made available as an open-weights model with 0B params.

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

→ More models by Harvard University → All language models → AI benchmarks leaderboard

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Sized to what UniRep 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.

Best
Intel Arc B580 12GB
Consumer GPU · ~$249
~120 tok/s
Q4 · fits full context · up to 8k ctx
Runs with: ExLlamaV2, KoboldCpp, LM Studio
See full estimate →

Software support

·ExLlamaV2 ·KoboldCpp ·LM Studio ·MLX ·Ollama ·SGLang ·TensorRT-LLM ·llama.cpp ·vLLM ?KTransformers ?text-generation-webui

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

Consumer GPU
120+ tok/s
33 options →
Apple
120+ tok/s
18 options →
Datacenter GPU
120+ tok/s
13 options →

Community

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Frequently asked questions

What is UniRep? ▶

UniRep is an AI model developed by Harvard University, in the language category, released in 2019. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.

Who created UniRep? ▶

UniRep was developed by Harvard University and released in 2019.

Is UniRep open source or proprietary? ▶

UniRep 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 UniRep cost? ▶

Pricing for UniRep depends on the provider. See the providers table on this page for the latest API rates.

How does UniRep perform on benchmarks? ▶

Benchmark scores for UniRep 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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