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license: mit libraryname: gguf pipelinetag: time-series-forecasting language: en basemodel: moment-research/MOMENT-1-large basemodelrelation: quantized quantizedby: amaye15 tags: gguf time-series forecasting...
Source du modèle
Description de la source
Pure Rust converter and inference engine for moment-research/MOMENT-1-large.
Pre-converted GGUF files are available at amaye15/moment-gguf. Produces GGUF v3 files and runs native forecasting — no Python required.
Sources
1 sourceVérifié 17 juil.
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
cargo build --release
Downloads the model from HuggingFace and writes a GGUF file:
# 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:
./scripts/convert_all.sh
HuggingFace token (optional for public models):
HF_TOKEN=hf_... ./scripts/convert_all.sh
Print all tensor names and shapes from a .safetensors checkpoint:
./target/release/moment-rs inspect-tensors models/model.safetensors
Run forecasting from stdin JSON:
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:
{
"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:
# 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
Install with maturin inside a virtual environment:
python -m venv .venv && source .venv/bin/activate
pip install maturin
maturin develop --features 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.
MOMENT-1-large is a masked patch encoder foundation model:
--- 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],...
Source context: 157 downloads · 0 likes · Pipeline time-series-forecasting · Library gguf · Repo amaye15/moment-gguf
Source context: 625 downloads · 0 likes · Pipeline time-series-forecasting · Library gguf · Repo amaye15/moment-gguf