WestQuantStudio/WQT50M

text-generation model by WestQuantStudio

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WestQuant Transformer 50M — Quantum Representation Scheduler WQT50M is a 50M-parameter Transformer that ranks quantum transformations. Trained on real Qiskit transpilation outputs — not synthetic data. It predicts which transformation is most promising given a circuit, backend, and optimization objective. WQT50M is a proof-of-concept that a 50M-parameter Transformer can learn structured quantum optimization preferences from real compiler outputs. Researchers building better compiler-optimization models can use this as a baseline. The same QAOA circuit gets different best actions depending on the objective: WQT50M predicts costs in the real magnitude range (not a narrow band):…

WestQuantStudio/WQT50M on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. WQT50M
  2. What It Does — 3 Tested Examples
  3. Example 1: 62% Gate Reduction (Random Circuit, Fidelity-Focused)
  4. Example 2: Objective-Aware Scheduling (QAOA, Linear Backend)
  5. Example 3: Calibrated Value Prediction
  6. Quick Start
  7. What WQT50M Predicts
  8. Model
  9. Validation Results
  10. 450 tests: 15 circuits × 5 objectives × 6 backends
  11. By objective
  12. By circuit type

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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

Model overview

WestQuantStudio/WQT50M is a text-generation model repository published by WestQuantStudio on Hugging Face. The source reports the transformers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
text-generation
Library
transformers
Recent downloads (30 days)
0
Cumulative likes
1
Hugging Face trending score
1
Reported safetensors parameters
49,533,952
Architecture
LlamaForCausalLM
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-09-29T06:05:53.000Z
Last modified
2026-09-29T06:06:18.000Z

Compatibility and lineage

Reported languages: en

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Model card and usage instructions

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Source: Hugging Face Hub. Metadata observed 2026-09-29T06:38:10.429Z. Daily imports are snapshots, not real-time monitoring.

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