other
0.4B parameters
Commercial OK
Reviewed May 2026

VBART Large (Turkish Summarization)

Turkish BART-style sequence-to-sequence model fine-tuned specifically for summarization. Not a chat model — purpose-built for input-document → Turkish-summary pipelines.

License: Apache-2.0·Context: 1,024 tokens

Overview

Turkish BART-style sequence-to-sequence model fine-tuned specifically for summarization. Not a chat model — purpose-built for input-document → Turkish-summary pipelines.

Strengths

  • Purpose-built for Turkish summarization — output quality beats general models at the task
  • Tiny footprint — runs on CPU or 2GB VRAM
  • Apache-2.0 license; commercial use unrestricted

Weaknesses

  • Encoder-decoder architecture — not a drop-in for Ollama/llama.cpp
  • Single-task model; can't be repurposed for chat
  • 1024-token input window limits summarization to short articles

Quantization variants

Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.

QuantizationFile sizeVRAM required
Q4_K_M0.2 GB1 GB

Get the model

HuggingFace

Original weights

huggingface.co/vngrs-ai/VBART-Large-Summarization

Source repository — direct quantization required.

Hardware that runs this

Cards with enough VRAM for at least one quantization of VBART Large (Turkish Summarization).

NVIDIA GB200 NVL72
13824GB · nvidia
AMD Instinct MI355X
288GB · amd
AMD Instinct MI325X
256GB · amd
AMD Instinct MI300X
192GB · amd
NVIDIA B200
192GB · nvidia
NVIDIA H100 NVL
188GB · nvidia
NVIDIA H200
141GB · nvidia
NVIDIA H200 NVL (PCIe)
141GB · nvidia
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Frequently asked

What's the minimum VRAM to run VBART Large (Turkish Summarization)?

1GB of VRAM is enough to run VBART Large (Turkish Summarization) at the Q4_K_M quantization (file size 0.2 GB). Higher-quality quantizations need more.

Can I use VBART Large (Turkish Summarization) commercially?

Yes — VBART Large (Turkish Summarization) ships under the Apache-2.0, which permits commercial use. Always read the license text before deployment.

What's the context length of VBART Large (Turkish Summarization)?

VBART Large (Turkish Summarization) supports a context window of 1,024 tokens (about 1K).

Source: huggingface.co/vngrs-ai/VBART-Large-Summarization

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.

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