SmolLM2-360M-Instruct-ONNX

SmolLM2-360M-Instruct — ONNX

ONNX export of HuggingFaceTB/SmolLM2-360M-Instruct, 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).

Files

Quick Start

import onnxruntime as ort
from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("liodon-ai/SmolLM2-360M-Instruct-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/SmolLM2-360M-Instruct-ONNX", file_name="model_quantized.onnx")

Source

• Model: HuggingFaceTB/SmolLM2-360M-Instruct
• License: other

Citation

@misc{liodonai_smollm2_360m_instruct_onnx,
  title        = {SmolLM2-360M-Instruct — ONNX},
  author       = {{Liodon AI}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/liodon-ai/SmolLM2-360M-Instruct-ONNX}},
  note         = {ONNX export of HuggingFaceTB/SmolLM2-360M-Instruct}
}

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Exported by Liodon AI