Card: three-line 'Use it' quickstart via load_lotus

README.md

@@ -36,6 +36,7 @@ baseline by **3.36%** — and its 270M-parameter model is the top system on the
 reasoning benchmark ahead of backbones 25–50× larger.
 
 Paper: [arXiv:2610.18877](https://arxiv.org/abs/2610.18877) · Code: [DA2I2-SLM/Lotus](https://github.com/DA2I2-SLM/Lotus)
+· Phase-1 (pretraining) checkpoints: [hiepnh137/LangTimeFoundationModel](https://huggingface.co/hiepnh137/LangTimeFoundationModel)
 
 ## Training
 
@@ -77,7 +78,27 @@ checkpoints used in the paper (joint five-task and sequential-curriculum variant
 ECG-QA accuracy is computed on the first 5,000 test items (manifest:
 `eval_manifests/ecg_test_first5000.json` in the code repository).
 
-## Quickstart
+## Use it
+
+Everything needed to run the model — including the LOTUS code — is inside this repository:
+
+```python
+from huggingface_hub import snapshot_download
+import sys; sys.path.insert(0, snapshot_download("daidv1112/LOTUS",
+                                                 allow_patterns=["load_lotus.py", "src/*"]))
+from load_lotus import load_lotus
+
+lotus = load_lotus()                     # Qwen3-0.6B checkpoint, GPU + dtype picked automatically
+lotus.ask("Is the time series showing an upward trend?", values)   # any question, your series
+```
+
+`pip install torch transformers peft numpy` (Python ≥ 3.10). `load_lotus()` downloads the
+checkpoint on first use; `ask()` takes any natural-language question and the raw series (it is
+z-normalised inside), with an optional `domain=` context hint. Example output:
+*"True. The series shows an upward trend as it starts at 0.25 and increases to 2.0."*
+
+<details>
+<summary>Low-level quickstart (explicit model construction)</summary>
 
 A checkpoint is a `torch.save` dictionary holding the time-series encoder, projector, next-patch
 decoder and LoRA adapters; the base LLM is loaded from its Hugging Face id. The model class is
@@ -123,6 +144,8 @@ text, _ = model.generate(batch, max_new_tokens=200)
 print(text[0])                                      # rationale, ends with "Answer: walking."
 ```
 
+</details>
+
 The exact prompts for every task are in [`docs/prompts.md`](https://github.com/DA2I2-SLM/Lotus/blob/main/docs/prompts.md),
 the evaluation harnesses in [`evaluation/`](https://github.com/DA2I2-SLM/Lotus/tree/main/evaluation),
 and a runnable tutorial (loading, QA, captioning, forecasting and this checkpoint) in the