Instructions to use vngrs-ai/VBART-Medium-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vngrs-ai/VBART-Medium-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vngrs-ai/VBART-Medium-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Medium-Base")
model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Medium-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vngrs-ai/VBART-Medium-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vngrs-ai/VBART-Medium-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vngrs-ai/VBART-Medium-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vngrs-ai/VBART-Medium-Base

SGLang

How to use vngrs-ai/VBART-Medium-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vngrs-ai/VBART-Medium-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vngrs-ai/VBART-Medium-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vngrs-ai/VBART-Medium-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vngrs-ai/VBART-Medium-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vngrs-ai/VBART-Medium-Base with Docker Model Runner:
```
docker model run hf.co/vngrs-ai/VBART-Medium-Base
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

VBART Model Card

Model Description

VBART is the first sequence-to-sequence LLM pre-trained on Turkish corpora from scratch on a large scale. It was pre-trained by VNGRS in February 2023.
The model is capable of conditional text generation tasks such as text summarization, paraphrasing, and title generation when fine-tuned. It outperforms its multilingual counterparts, albeit being much smaller than other implementations.

This repository contains pre-trained TensorFlow and Safetensors weights of VBART-Medium-Base.

Developed by: VNGRS-AI
Model type: Transformer encoder-decoder based on mBART architecture
Language(s) (NLP): Turkish
License: CC BY-NC-SA 4.0
Paper: arXiv

Training Details

Training Data

The base model is pre-trained on vngrs-web-corpus. It is curated by cleaning and filtering Turkish parts of OSCAR-2201 and mC4 datasets. These datasets consist of documents of unstructured web crawl data. More information about the dataset can be found on their respective pages. Data is filtered using a set of heuristics and certain rules, explained in the appendix of our paper.

Limitations

This model is the pre-trained base model and is capable of masked language modeling. Its purpose is to serve as the base model to be fine-tuned for downstream tasks.

Training Procedure

Pre-trained for a total of 63B tokens.

Hardware

GPUs: 8 x Nvidia A100-80 GB

Software

TensorFlow

Hyperparameters

Pretraining

Training regime: fp16 mixed precision
Training objective: Span masking (using mask lengths sampled from Poisson distribution λ=3.5, masking 30% of tokens)
Optimizer : Adam optimizer (β1 = 0.9, β2 = 0.98, Ɛ = 1e-6)
Scheduler: Custom scheduler from the original Transformers paper (20,000 warm-up steps)
Dropout: 0.1
Initial Learning rate: 5e-6
Training tokens: 63B

Citation

@article{turker2024vbart,
  title={VBART: The Turkish LLM},
  author={Turker, Meliksah and Ari, Erdi and Han, Aydin},
  journal={arXiv preprint arXiv:2403.01308},
  year={2024}
}