--- license: mit library_name: gguf pipeline_tag: time-series-forecasting language: - en base_model: time-series-foundation-models/Lag-Llama base_model_relation: quantized quantized_by: amaye15 tags: - gguf - time-series - forecasting - zero-shot - autoregressive - llm - rust inference: false --- # lag-llama-rs Pure Rust converter and inference engine for [time-series-foundation-models/Lag-Llama](https://huggingface.co/time-series-foundation-models/Lag-Llama). Pre-converted GGUF files are available at [amaye15/lag-llama-gguf](https://huggingface.co/amaye15/lag-llama-gguf). Produces GGUF v3 files and runs native forecasting — no Python required. ## Build ```bash cargo build --release ``` ## Convert Downloads `lag-llama.ckpt` from HuggingFace and converts it directly via candle's pickle reader — no Python or intermediate extraction step required: ```bash # F16 (recommended) ./target/release/lag-llama-rs convert --model time-series-foundation-models/Lag-Llama --dtype f16 --output gguf/lag_llama-f16.gguf # Q8_0 (smallest) ./target/release/lag-llama-rs convert --dtype q8 --output gguf/lag_llama-q8.gguf # F32 (full precision) ./target/release/lag-llama-rs convert --dtype f32 --output gguf/lag_llama-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/lag-llama-rs inspect-tensors models/model.safetensors ``` ## Infer Run point forecasting from comma-separated context values: ```bash echo '{"context": [1.0, 1.2, 1.5, 1.3, 1.8, 2.0, 1.9, 2.1], "horizon": 96}' \ | ./target/release/lag-llama-rs infer --gguf gguf/lag_llama-f16.gguf ``` Output is JSON in an OpenAI-compatible forecast format: ```json { "id": "forecast-000001932b7a1234", "object": "forecast", "created": 1749686400, "model": "lag-llama", "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/lag-llama-rs infer --gguf gguf/lag_llama-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 lag_llama_rs model = lag_llama_rs.LagLlama("gguf/lag_llama-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 Lag-Llama is a LLaMA-style autoregressive decoder for time series: - **Input**: Lagged values of the target series are appended as covariates alongside the most-recent context, giving the model a multi-scale view of the history - **Backbone**: Standard LLaMA decoder (RoPE, causal self-attention, SwiGLU FFN) - **Output**: Point forecast for each future timestep via autoregressive decoding - **Checkpoint**: Reads `.ckpt` (PyTorch Lightning pickle) directly — no Python required
