Time Series Forecasting
Transformers
Safetensors
TimesFM
timebraid
text-generation
time-series
time-series-understanding
forecasting
multimodal
qwen3
custom_code
Instructions to use XinyueWangg/TimeBraid-2.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XinyueWangg/TimeBraid-2.5B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("XinyueWangg/TimeBraid-2.5B", trust_remote_code=True, device_map="auto") - TimesFM
How to use XinyueWangg/TimeBraid-2.5B with TimesFM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download processor_config.json from XinyueWangg/TimeBraid-2.5B: direct link, hf CLI and curl.
- Browser
- Download file 194 Bytes
-
https://huggingface.co/XinyueWangg/TimeBraid-2.5B/resolve/main/processor_config.json
- Command line
-
hf download hf://XinyueWangg/TimeBraid-2.5B/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/XinyueWangg/TimeBraid-2.5B/resolve/main/processor_config.json
194 Bytes
| { | |
| "auto_map": { | |
| "AutoProcessor": "processing_timebraid.TimeBraidProcessor" | |
| }, | |
| "max_spans_per_sample": 64, | |
| "normalization_epsilon": 1e-06, | |
| "processor_class": "TimeBraidProcessor" | |
| } | |