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liodon-ai/Puro-2B-Base-ONNX
Puro-2B-Base-ONNX is a text generation model from liodon-ai. Use it when you need the model to write or continue text. It is set up for onnx. The card lists the license as other.
ONNX export of thu-pacman/Puro-2B-Base, published by Liodon AI. Exported with optimum (optimum.exporters.onnx.mainexport, task text-generation-with-past, so the graph exposes past-key-value inputs/outputs for KV-cached
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.onnx_data12.4 GB · 86%
From the Hugging Face model README
ONNX export of thu-pacman/Puro-2B-Base, published by Liodon AI.
Exported with optimum (optimum.exporters.onnx.main_export,
task text-generation-with-past, so the graph exposes past-key-value inputs/outputs for KV-cached
autoregressive decoding).
| File | Size | Notes |
|---|---|---|
model.onnx | 8.13 GB | FP32, full precision |
model_fp16.onnx | 4.30 GB | FP16, for GPU execution providers |
model_quantized.onnx | 2.03 GB | Dynamic INT8 (weight-only, no calibration) |
import onnxruntime as ort
from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("liodon-ai/Puro-2B-Base-ONNX")
sess = ort.InferenceSession("model_quantized.onnx", providers=["CPUExecutionProvider"])
# past_key_values.*.key / .value inputs must be supplied (zero-length tensors
# for the first forward pass) -- see optimum's ORTModelForCausalLM for a
# ready-made wrapper that handles KV-cache bookkeeping automatically:
# from optimum.onnxruntime import ORTModelForCausalLM
# model = ORTModelForCausalLM.from_pretrained("liodon-ai/Puro-2B-Base-ONNX", file_name="model_quantized.onnx")
@misc{liodonai_puro_2b_base_onnx,
title = {Puro-2B-Base — ONNX},
author = {{Liodon AI}},
year = {2026},
howpublished = {\url{https://huggingface.co/liodon-ai/Puro-2B-Base-ONNX}},
note = {ONNX export of thu-pacman/Puro-2B-Base}
}
Exported by Liodon AI