Sundial GGUF

--- license: apache-2.0 library_name: gguf pipeline_tag: time-series-forecasting language: - en base_model: thuml/sundial-base-128m base_model_relation: quantized quantized_by: amaye15 tags: - gguf - time-series - forecasting - zero-shot - flow-matching - transformer - rust inference: false --- # Sundial (THUML) — GGUF GGUF conversion of [THUML's Sundial](https://huggingface.co/thuml/sundial-base-128m) — a 128M parameter flow-matching generative model for time series. Converted and run with [**zsfm**](https://github.com/amaye15/zsfm-rs), a Rust workspace that ports zero-shot forecasting and tabular foundation models to GGUF + [candle](https://github.com/huggingface/candle). No PyTorch, no Python runtime required to run inference. | F32 | F16 | Q8_0 | |---|---|---| | `sundial-f32.gguf` | `sundial-f16.gguf` | `sundial-q8.gguf` | F16 is generally the best size/accuracy trade-off; Q8_0 is smallest. This repo's default recommendation matches the upstream conversion default: **F16**. **Context must be at least 32 timesteps** (Sundial pads short contexts to a full patch, but 32 gives a meaningful example) — a shorter context fails with `context too short`. The examples below use a 32-value context. ## Use it ### Python (`pip install zsfm`) ```bash pip install zsfm huggingface_hub ``` ```python import zsfm from huggingface_hub import hf_hub_download gguf_path = hf_hub_download("amaye15/sundial-gguf", "sundial-f16.gguf") model = zsfm.SundialModel(gguf_path) context = [0.85, 0.93, 1.01, 1.09, 1.17, 1.25, 1.33, 1.06, 1.14, 1.22, 1.3, 1.38, 1.46, 1.54, 1.27, 1.35, 1.43, 1.51, 1.59, 1.67, 1.75, 1.48, 1.56, 1.64, 1.72, 1.8, 1.88, 1.96, 1.69, 1.77, 1.85, 1.93] point = model.forecast(context, horizon=64) # -> List[float], the (deterministic) decoded point forecast ``` Sundial is generative (flow-matching): this Rust port's ODE solver starts from an all-zero state rather than sampled noise, so `forecast()` is deterministic — a single decoded path, not a distributional sample or an ensemble mean. ### Rust / CLI (`cargo install zsfm`) ```bash cargo install zsfm --locked ``` ```bash # downloads the original weights and converts to GGUF locally # (produces the same bytes as sundial-f16.gguf in this repo): zsfm sundial convert --dtype f16 -o gguf/sundial-f16.gguf echo '{"context": [0.85, 0.93, 1.01, 1.09, 1.17, 1.25, 1.33, 1.06, 1.14, 1.22, 1.3, 1.38, 1...

--- license: mit library_name: gguf pipeline_tag: time-series-forecasting language: - en base_model: thuml/sundial-base-128m base_model_relation: quantized quantized_by: amaye15 tags: - gguf - time-series - forecasting - zero-shot - flow-matching - transformer - rust inference: false --- # sundial-rs Pure Rust converter and inference engine for [thuml/sundial-base-128m](https://huggingface.co/thuml/sundial-base-128m). Pre-converted GGUF files are available at [amaye15/sundial-gguf](https://huggingface.co/amaye15/sundial-gguf). Produces GGUF v3 files and runs native forecasting — no Python required. ## Build ```bash cargo build --release ``` ## Convert Downloads the model from HuggingFace and writes a GGUF file: ```bash # F16 (recommended) ./target/release/sundial-rs convert --model thuml/sundial-base-128m --dtype f16 --output gguf/sundial-f16.gguf # Q8_0 (smallest) ./target/release/sundial-rs convert --dtype q8 --output gguf/sundial-q8.gguf # F32 (full precision) ./target/release/sundial-rs convert --dtype f32 --output gguf/sundial-f32.gguf ``` To convert all dtypes at once: ```bash ./scripts/convert_all.sh ``` HuggingFace token (optional for public models): ```bash HF_TOKEN=hf_... ./scripts/convert_all.sh ``` ## Inspect tensors Print all tensor names and shapes from a GGUF file: ```bash ./target/release/sundial-rs inspect-tensors gguf/sundial-f16.gguf ``` ## Infer Run forecasting from stdin JSON: ```bash echo '{"context": [1.0, 1.2, 1.5, 1.3, 1.8, 2.0, 1.9, 2.1], "horizon": 96}' \ | ./target/release/sundial-rs infer --gguf gguf/sundial-f16.gguf ``` Output is JSON in an OpenAI-compatible forecast format: ```json { "id": "forecast-000001932b7a1234", "object": "forecast", "created": 1749686400, "model": "sundial", "choices": [{ "index": 0, "forecast": { "point": [2.1, 2.3, 2.5, "..."], "quantiles": {} }, "finish_reason": "stop" }], "usage": {"context_length": 8, "forecast_length": 96} } ``` **Batch inference** — pass multiple series as a nested array to get one `Choice` per series: ```bash echo '{"context": [[1.0, 1.2, 1.5], [2.0, 2.2, 2.5]], "horizon": 96}' \ | ./target/release/sundial-rs infer --gguf gguf/sundial-f16.gguf ``` ## Python bindings Install with [maturin](https://github.com/PyO3/maturin) inside a virtual environment: ```bash python -m venv .venv && source .venv/bin/activate pip install maturin...

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