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
File size: 910 Bytes
0acbed8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | """TimeBraid model core."""
from .loading import (
LoadedTimeBraid,
ValidatedTimeBraidCheckpointSource,
load_timebraid_checkpoint,
load_timebraid_tokenizer,
validate_timebraid_canonical_artifact,
validate_timebraid_checkpoint_source,
validate_timebraid_model,
)
from .mot import TimeBraidGenerateOutput
from .timebraid import (
TimeBraid,
TimeBraidConfig,
TimeBraidLossBreakdown,
TimeBraidOutput,
register_timebraid_auto_classes,
)
__all__ = [
"TimeBraid",
"TimeBraidConfig",
"TimeBraidGenerateOutput",
"TimeBraidLossBreakdown",
"TimeBraidOutput",
"LoadedTimeBraid",
"ValidatedTimeBraidCheckpointSource",
"register_timebraid_auto_classes",
"load_timebraid_checkpoint",
"load_timebraid_tokenizer",
"validate_timebraid_canonical_artifact",
"validate_timebraid_model",
"validate_timebraid_checkpoint_source",
]
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