Instructions to use quantispect/QuantiSpect-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Ising Decoding
How to use quantispect/QuantiSpect-V1 with Ising Decoding:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| Factory module for creating models. | |
| Provides ModelFactory for instantiating pre-decoder models from config. | |
| """ | |
| class ModelFactory: | |
| def create_model(cfg): | |
| if cfg.code == "surface": | |
| return ModelFactory._create_surface_model(cfg) | |
| else: | |
| raise ValueError("Invalid model name") | |
| def _create_surface_model(cfg): | |
| if cfg.model.version == "predecoder_memory_v1": | |
| from model.predecoder import PreDecoderModelMemory_v1 | |
| model = PreDecoderModelMemory_v1(cfg) | |
| return model | |
| elif cfg.model.version == "predecoder_sd_litenet_v1": | |
| from model.predecoder_sd_litenet_v1 import PredecoderSDLiteNetV1 | |
| model = PredecoderSDLiteNetV1( | |
| input_channels=getattr(cfg.model, "input_channels", 4), | |
| out_channels=getattr(cfg.model, "out_channels", 4), | |
| hidden_dim=getattr(cfg.model, "hidden_dim", 64), | |
| bottleneck_dim=getattr(cfg.model, "bottleneck_dim", 16), | |
| dropout_p=getattr(cfg.model, "dropout_p", 0.05), | |
| ) | |
| return model | |
| elif cfg.model.version == "predecoder_fasthyper_rf13_v1": | |
| from model.predecoder_fasthyper_rf13_v1 import PredecoderFastHyperRF13V1 | |
| model = PredecoderFastHyperRF13V1( | |
| input_channels=getattr(cfg.model, "input_channels", 4), | |
| out_channels=getattr(cfg.model, "out_channels", 4), | |
| hidden_dim=getattr(cfg.model, "hidden_dim", 96), | |
| mid_dim=getattr(cfg.model, "mid_dim", 144), | |
| mix_groups=getattr(cfg.model, "mix_groups", 6), | |
| num_blocks=getattr(cfg.model, "num_blocks", 5), | |
| stem_kernel_size=getattr(cfg.model, "stem_kernel_size", 3), | |
| dropout_p=getattr(cfg.model, "dropout_p", 0.02), | |
| gate_reduction=getattr(cfg.model, "gate_reduction", 4), | |
| ) | |
| return model | |
| else: | |
| raise ValueError(f"Invalid model version: {cfg.model.version}") | |