Refine README.md as plain-text overview

README.md

@@ -8,83 +8,27 @@ pinned: false
 short_description: Time-series foundation models as a service.
 ---
 
-<div align="center">
-
 # TSFM.ai
 
-**Time-series foundation models as a service.**
-
-One API. 49+ pretrained models. Zero fine-tuning required.
-
-[tsfm.ai](https://tsfm.ai) · [docs](https://tsfm.ai/docs) · [API reference](https://tsfm.ai/docs/api) · [pricing](https://tsfm.ai/pricing)
-
-</div>
-
----
-
-## What we do
-
-TSFM.ai hosts the largest catalog of **pretrained time-series foundation models** behind a single, consistent inference API. Bring a history — numbers, timestamps, optional covariates — and get a calibrated forecast with quantile bands, without training or fine-tuning.
-
-We serve every major open-weights forecaster:
-
-- **Chronos / Chronos-Bolt / Chronos-2** (Amazon)
-- **TimesFM 2.0 / 2.5** (Google)
-- **Moirai 1.x / 2.0 / Moirai-MoE** (Salesforce)
-- **Granite TTM / PatchTST / FlowState** (IBM Research)
-- **TiRex** (NX-AI)
-- **Toto** (Datadog)
-- **TimeMoE** (Maple / Tsinghua)
-- **MOMENT** (AutonLab, CMU)
-- **Sundial / Timer / TimeMixer++** (THUML)
-- **Lag-Llama**, **TEMPO**, **Kairos**, **YingLong**, **Kronos**, **TSPulse** and more.
-
-If the model is on the Hub and has weights, we're probably already serving it. If not, open an issue on the repo and we'll prioritize it.
-
-## Why time-series deserves its own provider
-
-General-purpose LLM inference stacks are a bad fit for forecasting. Time-series models have narrow context windows, variable history lengths, quantile outputs, exogenous covariates, and probabilistic sampling — none of which map cleanly onto OpenAI-style APIs. We built TSFM.ai specifically for this surface: our request/response schema carries `past_values`, `past_timestamps`, `past_covariates`, `future_covariates`, `static_covariates`, `quantiles`, and `num_samples` as first-class fields. Batch forecasting, ensemble routing, and MCP tooling all compose cleanly on top.
-
-## Get started
-
-```bash
-curl -X POST https://api.tsfm.ai/v1/forecast \
-  -H "Authorization: Bearer $TSFM_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "amazon/chronos-2",
-    "inputs": [{"target": [10, 12, 11, 13, 14, 15, 14, 16, 18, 17]}],
-    "parameters": {"prediction_length": 24, "quantiles": [0.1, 0.5, 0.9]}
-  }'
-```
-
-```python
-from tsfm import Tsfm
-
-client = Tsfm()
-forecast = client.forecast(
-    model="amazon/chronos-2",
-    inputs=[{"target": [10, 12, 11, 13, 14, 15, 14, 16, 18, 17]}],
-    parameters={"prediction_length": 24, "quantiles": [0.1, 0.5, 0.9]},
-)
-print(forecast.predictions[0].mean)
-```
+**Time-series foundation models as a service.** One API. 49+ pretrained forecasters. Zero fine-tuning required.
 
-## Benchmarks
+The Organization Card you see at [huggingface.co/TSFM-ai](https://huggingface.co/TSFM-ai) is rendered from [`index.html`](./index.html) in this Space — edit that file to change the card. This README.md is kept as a plain-text overview for anyone browsing the Files tab.
 
-We publish continuously-updated leaderboards scoring every hosted model across **GIFT-Eval** and the **Impermanent** long-horizon benchmark suite:
+## What we host
 
-- [GIFT-Eval leaderboard](https://tsfm.ai/benchmarks/gift-eval)
-- [Impermanent leaderboard](https://tsfm.ai/benchmarks/impermanent)
+Every major open-weights time-series foundation model, served behind one consistent inference API. The canonical catalog lives in the [Collection](https://huggingface.co/collections/TSFM-ai/time-series-foundation-models-served-by-tsfmai-69e2baca51579e4b126dbf20) — 49 models across Chronos / Chronos-Bolt / Chronos-2 (Amazon), TimesFM 2.0 and 2.5 (Google), Moirai 1.x / 2.0 / Moirai-MoE (Salesforce), Granite TTM / PatchTST / FlowState (IBM Research), TiRex (NX-AI), Toto (Datadog), TimeMoE, MOMENT, Sundial, Timer, Lag-Llama, TEMPO, Kairos, YingLong, Kronos, TSPulse, and more.
 
-## Learn more
+## Links
 
-- [Blog](https://tsfm.ai/blog) — model deep-dives, benchmark analyses, architectural notes.
-- [Use cases](https://tsfm.ai/use-cases) — demand planning, energy load, telemetry, financial time series.
-- [Integrations](https://tsfm.ai/integrations) — Python SDK, Node SDK, CLI, MCP tools.
+- Website: <https://tsfm.ai>
+- Docs: <https://tsfm.ai/docs>
+- API reference: <https://tsfm.ai/docs/api>
+- Pricing: <https://tsfm.ai/pricing>
+- GIFT-Eval leaderboard: <https://tsfm.ai/benchmarks/gift-eval>
+- Impermanent leaderboard: <https://tsfm.ai/benchmarks/impermanent>
+- Blog: <https://tsfm.ai/blog>
 
 ## Contact
 
-- **General:** [hello@tsfm.ai](mailto:hello@tsfm.ai)
-- **Enterprise:** [sales@tsfm.ai](mailto:sales@tsfm.ai)
-- **Website:** [tsfm.ai](https://tsfm.ai)
+- General: <hello@tsfm.ai>
+- Enterprise: <sales@tsfm.ai>