Instructions to use amazon/chronos-bolt-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use amazon/chronos-bolt-base with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-bolt-base", device_map="cuda") # Load historical data context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv") # Generate predictions pred_df = pipeline.predict_df( context_df, prediction_length=36, # Number of steps to forecast quantile_levels=[0.1, 0.5, 0.9], # Quantiles for probabilistic forecast id_column="item_id", # Column identifying different time series timestamp_column="Month", # Column with datetime information target="#Passengers", # Column(s) with time series values to predict ) - Notebooks
- Google Colab
- Kaggle
π Documentation Enhancement Suggestion
π Documentation Enhancement Suggestion
This observation was generated by Crovia β the AI transparency observation layer.
Crovia does not accuse or judge. It observes publicly available information and suggests improvements.
π Quick Stats
| Metric | Value |
|---|---|
| Source | huggingface |
| Downloads | 2150299 |
| Likes | 75 |
| Last Updated | 2026-02-09 |
π» Ready-to-Use Code
from transformers import AutoModel, AutoTokenizer
model_id = "amazon/chronos-bolt-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id)
# Example usage
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)
π Citation
If you use this model, please cite:
@misc {amazon_chronos_bolt_base_2026,
author = {amazon},
title = {amazon/chronos-bolt-base},
year = {2026},
url = {https://huggingface.co/amazon/chronos-bolt-base},
note = {Accessed via CROVIA transparency registry}
}
βοΈ EU AI Act Compliance Checklist
- Training data disclosed
- License clearly stated
- Intended use documented
- Model limitations documented
- Evaluation metrics provided
- Bias/fairness analysis
π Training Data Transparency
Training Data Status: β οΈ Partial
A training section exists but specific datasets are not enumerated.
Recommendation: Add explicit dataset names and versions for full transparency.
Enhancement generated by CROVIA Β· Package ID: ebaca1911393
Generated at: 2026-02-09T19:31:47.028691Z
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Crovia does not accuse or judge. It observes publicly available information and suggests documentation improvements.
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