Time Series Forecasting
Transformers
Safetensors
t5
text2text-generation
TSFM
Finance
Financial Forecasting
FinText
text-generation-inference
Instructions to use FinText/Chronos_Tiny_2017_Global with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FinText/Chronos_Tiny_2017_Global with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FinText/Chronos_Tiny_2017_Global") model = AutoModelForSeq2SeqLM.from_pretrained("FinText/Chronos_Tiny_2017_Global", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from FinText/Chronos_Tiny_2017_Global: direct link, hf CLI and curl.
- Browser
- Download file 662 Bytes
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https://huggingface.co/FinText/Chronos_Tiny_2017_Global/resolve/main/README.md
- Command line
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hf download hf://FinText/Chronos_Tiny_2017_Global/README.md
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curl -L -o README.md https://huggingface.co/FinText/Chronos_Tiny_2017_Global/resolve/main/README.md
662 Bytes
metadata
pipeline_tag: time-series-forecasting
license: apache-2.0
tags:
- TSFM
- Finance
- Financial Forecasting
- FinText
library_name: transformers
Chronos-Tiny (TSFM) โ Global (2017)
This is the Time Series Foundation Model (TSFM), pre-trained on global financial time series data up to the year 2017 using the Chronos architecture (Tiny size). The dataset spans from 1990โ2017 and includes all global excess return data.
๐ Related Links