--- license: mit library_name: gguf pipeline_tag: time-series-forecasting language: - en base_model: Salesforce/moirai-1.0-R-large base_model_relation: quantized quantized_by: amaye15 tags: - gguf - time-series - forecasting - zero-shot - transformer - universal-forecasting - rust inference: false --- # moirai-rs Pure Rust converter and inference engine for [Salesforce/moirai-1.0-R-large](https://huggingface.co/Salesforce/moirai-1.0-R-large). Pre-converted GGUF files are available at [amaye15/moirai-gguf](https://huggingface.co/amaye15/moirai-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/moirai-rs convert --model Salesforce/moirai-1.0-R-large --dtype f16 --output gguf/moirai-f16.gguf # Q8_0 (smallest) ./target/release/moirai-rs convert --dtype q8 --output gguf/moirai-q8.gguf # F32 (full precision) ./target/release/moirai-rs convert --dtype f32 --output gguf/moirai-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/moirai-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/moirai-rs infer --gguf gguf/moirai-f16.gguf ``` Output is JSON in an OpenAI-compatible forecast format: ```json { "id": "forecast-000001932b7a1234", "object": "forecast", "created": 1749686400, "model": "moirai", "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/moirai-rs infer --gguf gguf/moirai-f16.gguf ``` **Multivariate inference** — pass a 3D context array `[batch][variate][time]` to get a `variates` array in each choice. Each variate is processed independently (channel-independent): ```bash echo '{ "context": [ [[1.0, 1.2, 1.5, 1.3, 1.8, 2.0, 1.9, 2.1], [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]] ], "horizon": 96 }' \ | ./target/release/moirai-rs infer --gguf gguf/moirai-f16.gguf ``` ```json { "choices": [{ "index": 0, "forecast": { "variates": [ {"point": [2.1, 2.3, "..."], "quantiles": {}}, {"point": [1.3, 1.4, "..."], "quantiles": {}} ] }, "finish_reason": "stop" }] } ``` ## 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 moirai_rs model = moirai_rs.Moirai("gguf/moirai-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 Moirai-1.0-R-large is a universal forecasting foundation model: - **Input**: Multi-patch-size projections — patches at sizes 8, 16, 32, 64, and 128 are embedded in parallel, giving the encoder a multi-scale view of the context - **Backbone**: Bidirectional encoder (attends over the full context window); ~311M parameters - **Normalization**: Mean-absolute-mean (absmean) instance normalization - **Output**: Student-t distribution head producing probabilistic point forecasts
