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NVIDIA NIM

SeaLLM 7B V2.5 on NVIDIA NIM

SeaLLM 2 · Alibaba

Provisioned

Last refreshed 2026-05-01. Next refresh: weekly.

Why use SeaLLM 7B V2.5 on NVIDIA NIM?

NVIDIA NIM offers SeaLLM 7B V2.5 with competitive pricing. NVIDIA NIM is NVIDIA's deployment platform for GPU-accelerated inference microservices.

Input / 1M
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Output / 1M
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Cache
Not sourced
Batch
Not sourced

Setup recipe

Docs fallback
Install
Use the provider REST API or SDK
Auth
Create a provider API key
Call
model: seallm-7b-v2.5
Model ID
seallm-7b-v2.5

Request example

Curated snippets for this provider are not sourced yet. Use NVIDIA NIM documentation with model ID seallm-7b-v2.5.

Gotchas

No curated gotchas have been sourced for this exact provider/model route yet.

Pricing

TypeRate
GPU Hour Rate$1.00/GPU·hr
GPU Config1xH100

Capabilities

No model capability flags are currently sourced.

About SeaLLM 7B V2.5

SeaLLM-7B-v2.5 is a cutting-edge multilingual large language model tailored for Southeast Asian languages. It surpasses its predecessor, SeaLLM-13B, by providing enhanced performance despite having just half its parameter size. The model excels in various tasks such as text generation and question answering, demonstrating a profound comprehension of language and context. Built on the Gemma-7b architecture, it underwent extensive supervised fine-tuning, contributing to its impressive multilingual proficiency across ten SEA languages. SeaLLM-7B-v2.5 not only outperforms GPT-3.5 on several SEA multilingual benchmarks but also flaunts superior capabilities in reasoning tasks like GSM8K and MATH, particularly in non-Latin languages, making it a versatile tool for diverse applications. Its open-source nature encourages collaborative innovation and further advancement in AI models.

FAQ

Who created SeaLLM 7B V2.5?

SeaLLM 7B V2.5 was created by Alibaba as part of the SeaLLM 2 model family.

Is SeaLLM 7B V2.5 open source?

SeaLLM 7B V2.5's open source status is unknown in the seed data.

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Model Specs

Released2024-07-01
Parameters7B
ArchitectureDecoder Only