--- license: mit library_name: gguf pipeline_tag: time-series-forecasting language: - en base_model: moment-research/MOMENT-1-large base_model_relation: quantized quantized_by: amaye15 tags: - gguf - time-series - forecasting - zero-shot - transformer - masked-encoder - rust inference: false --- # moment-rs Pure Rust converter and inference engine for [moment-research/MOMENT-1-large](https://huggingface.co/moment-research/MOMENT-1-large). Pre-converted GGUF files are available at [amaye15/moment-gguf](https://huggingface.co/amaye15/moment-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/moment-rs convert --model moment-research/MOMENT-1-large --dtype f16 --output gguf/moment-f16.gguf # Q8_0 (smallest) ./target/release/moment-rs convert --dtype q8 --output gguf/moment-q8.gguf # F32 (full precision) ./target/release/moment-rs convert --dtype f32 --output gguf/moment-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 `.safetensors` checkpoint: ```bash ./target/release/moment-rs inspect-tensors models/model.safetensors ``` ## 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/moment-rs infer --gguf gguf/moment-f16.gguf ``` Output is JSON in an OpenAI-compatible forecast format: ```json { "id": "forecast-000001932b7a1234", "object": "forecast", "created": 1749686400, "model": "moment", "choices": [{ "index": 0, "forecast": { "point": [2.1, 2.3, 2.5, "..."], "quantiles": {} }, "finish_reason": "stop" }], "usage": {"context_length": 8, "forecast_length": 96} } ``` **Batch / Multivariate inference** — Moment is channel-independent: each variate is encoded as an independent series. Pass a batch of univariate series to get one `Choice` per series, or use the batch mode to handle multiple variates of a multivariate dataset by submitting each variate as a separate item: ```bash # Two independent series — one Choice each echo '{"context": [[1.0, 1.2, 1.5], [2.0, 2.2, 2.5]], "horizon": 96}' \ | ./target/release/moment-rs infer --gguf gguf/moment-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 moment_rs model = moment_rs.Moment("gguf/moment-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 MOMENT-1-large is a masked patch encoder foundation model: - **Input**: Time series is split into fixed-length patches; forecast-window patches are replaced with a learnable mask token during pretraining - **Backbone**: T5-style bidirectional transformer with relative position biases; ~385M parameters - **Pretraining**: Self-supervised masked patch reconstruction across diverse time series datasets - **Output**: Patch-level representations decoded to point forecasts for each future timestep
