| --- |
| base_model: amazon/chronos-2 |
| base_model_relation: adapter |
| library_name: peft |
| pipeline_tag: time-series-forecasting |
| tags: |
| - lora |
| - chronos |
| - time-series |
| - forecasting |
| - multimodal |
| - coco |
| --- |
| |
| # CoCo: Chronos2 LoRA adapter |
|
|
| The multivariate time-series forecasting component of **TSFM Meets LLM: Context as Covariates (CoCo)**, shared by SWARM-Lab. This repository contains the adapter from the paper's submitted supplementary package, not the base model weights. |
|
|
| This adapter teaches Chronos2 to use an LLM's forecast and confidence covariates together with numerical history. Its companion LLM adapter is [SWARM-Lab/CoCo-Qwen3-4B-LoRA](https://huggingface.co/SWARM-Lab/CoCo-Qwen3-4B-LoRA). |
|
|
| ## Checkpoint |
|
|
| - Base model: `amazon/chronos-2`. |
| - PEFT LoRA rank: 8; alpha: 16; dropout: 0. |
| - Target modules: `self_attention.q`, `self_attention.k`, `self_attention.v`, `self_attention.o`, `output_patch_embedding.output_layer`. |
| - Adapter file: 4,854,584 bytes. |
| - Adapter configuration records PEFT 0.18.1. |
|
|
| ## Loading the component |
|
|
| ```python |
| import torch |
| from chronos.chronos2 import Chronos2Model, Chronos2Pipeline |
| from peft import PeftModel |
| |
| base = Chronos2Model.from_pretrained( |
| "amazon/chronos-2", torch_dtype=torch.bfloat16, device_map="cuda" |
| ) |
| model = PeftModel.from_pretrained(base, "SWARM-Lab/CoCo-Chronos2-LoRA") |
| model = model.merge_and_unload() |
| model.eval() |
| pipeline = Chronos2Pipeline(model) |
| ``` |
|
|
| Merging happens in memory; this repository stores only the LoRA adapter. This follows the submitted loader, but the remote-loading example has not yet been GPU-tested as part of the release. |
|
|
| Use the paper's covariate construction rather than supplying arbitrary text embeddings: the forecast channel is shifted, numerical history is normalized using history-only statistics, and raw rollout variance is divided by squared history standard deviation (with the implementation's numerical stabilizer). The submitted implementation uses history noise standard deviation 0.05. A unified public CoCo inference package is being prepared. |
|
|
| The submitted environment pins `chronos-forecasting==2.1.0`, `peft==0.18.1`, and `torch==2.9.0`. |
|
|
| ## Training and intended use |
|
|
| The paper describes second-stage LoRA adaptation on pre-generated LLM forecast and confidence covariates. Intended use is research on text-aided time-series forecasting. The confidence covariate is a rollout-variance signal, not a calibrated probability of correctness. |
|
|
| ## Base-model license |
|
|
| The base [Chronos2 checkpoint](https://huggingface.co/amazon/chronos-2) is distributed under Apache-2.0 by its publisher. |
|
|
