--- license: mit library_name: gguf pipeline_tag: time-series-forecasting language: - en base_model: ibm-granite/granite-timeseries-ttm-r2 base_model_relation: quantized quantized_by: amaye15 tags: - gguf - time-series - forecasting - zero-shot - mlp-mixer - rust inference: false --- # ttm-rs Pure Rust converter and inference engine for [ibm-granite/granite-timeseries-ttm-r2](https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2). Pre-converted GGUF files are available at [amaye15/ttm-gguf](https://huggingface.co/amaye15/ttm-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/ttm-rs convert --model ibm-granite/granite-timeseries-ttm-r2 --dtype f16 --output gguf/ttm-f16.gguf # Q8_0 (smallest) ./target/release/ttm-rs convert --dtype q8 --output gguf/ttm-q8.gguf # F32 (full precision) ./target/release/ttm-rs convert --dtype f32 --output gguf/ttm-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 file: ```bash ./target/release/ttm-rs inspect-tensors models/model.safetensors ``` ## Infer Run forecasting from comma-separated context values. The context must be at least `patch_length` steps (defined in `config.json`): ```bash echo '{"context": [1.0, 1.2, 1.5, 1.3, 1.8, 2.0, 1.9, 2.1], "horizon": 96}' \ | ./target/release/ttm-rs infer \ --gguf gguf/ttm-f16.gguf \ --config models/config.json ``` Output is JSON in an OpenAI-compatible forecast format: ```json { "id": "forecast-000001932b7a1234", "object": "forecast", "created": 1749686400, "model": "ttm", "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/ttm-rs infer \ --gguf gguf/ttm-f16.gguf \ --config models/config.json ``` ## 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 ttm_rs model = ttm_rs.Ttm("gguf/ttm-f16.gguf", "models/config.json") 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 TTM (Tiny Time-Mixer) is a compact encoder-decoder model: - **Encoder**: Multi-layer mixer blocks operating across the patch dimension; adaptive patching levels allow different temporal resolutions simultaneously - **Decoder**: Lightweight projection from encoder representations to the forecast horizon - **Scale**: ~1M parameters — orders of magnitude smaller than transformer-based foundation models, competitive on short-horizon benchmarks - **Config**: `context_length`, `prediction_length`, `patch_length`, `patch_stride`, `d_model`, `num_layers`, `decoder_num_layers`, and `adaptive_patching_levels` are loaded from `config.json`
