--- 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 maturin develop --features python ``` ```python import sundial_rs model = sundial_rs.Sundial("gguf/sundial-f16.gguf") result = model.forecast([1.0, 1.2, 1.5, 1.3, 1.8, 2.0], horizon=96) point = result["choices"][0]["forecast"]["point"] # Batch — one Choice per series result = model.forecast([[1.0, 1.2, 1.5], [2.0, 2.2, 2.5]], horizon=96) ``` `forecast` returns a Python dict in the same OpenAI-compatible format as the CLI. ## Architecture notes Sundial is a flow-matching generative model for time series: - **Encoder**: Patch-based causal transformer (128M params) that encodes the context window into a conditioning vector - **Decoder**: A small flow network trained to map Gaussian noise to the forecast distribution, conditioned on the encoder output - **Sampler**: Euler integration from t=0 (noise) to t=1 (data); Heun's method used for Python reference validation - **RoPE**: Standard rotary positional embeddings applied to all attention heads
