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samuellimabraz/
Qwen3-VL-8B-olora

Name: Qwen3-VL-8B-olora
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Quantum Assistant: Specialization of Multimodal Models for Quantum Computing

License

GitHub

W&B

TensorBoard

The first multimodal Vision-Language Model specialized for quantum computing with Qiskit

Model Description

This model is a fine-tuned version of Qwen3-VL-8B-Instruct specialized for quantum computing tasks using Qiskit 2.0. This model can interpret visual representations of quantum computing: circuit diagrams, Bloch spheres, and measurement histograms.

The model was trained using OLoRA (Orthonormal Low-Rank Adaptation) with rank 16 for 1 epoch on the Quantum Assistant Dataset. OLoRA uses QR decomposition for orthonormal initialization. This model was part of the Phase 1 PEFT comparison experiments and was not evaluated on external benchmarks.

Key Capabilities

Code Generation: Generate complete Qiskit code from natural language descriptions
Function Completion: Complete function bodies from signatures and docstrings
Visual Understanding: Interpret quantum circuit diagrams, Bloch spheres, and histograms
Conceptual Explanations: Answer questions about quantum computing theory
Qiskit 2.0 Compliant: Uses modern APIs (SamplerV2, EstimatorV2, generate_preset_pass_manager)

Training Evaluation

This model was part of the Phase 1 PEFT variant comparison experiments. It was not evaluated on external benchmarks (Qiskit HumanEval, Qiskit HumanEval Hard, or synthetic test set).

Internal Validation Metrics

Metric	Value	Step
Eval Loss	0.742	183 (final)
Eval Token Accuracy	0.794	183 (final)
Train Loss	0.779	183 (final)
Train Token Accuracy	0.793	183 (final)
Training Runtime	1,067 seconds (~17.8 min)	1 epoch

PEFT Comparison Analysis

OLoRA showed significantly worse performance than other PEFT variants in Phase 1 experiments:

Highest validation loss: 0.742 vs 0.622 for rsLoRA (19.3% higher)
Lowest token accuracy: 0.794 vs 0.818 for rsLoRA
QR orthonormal initialization appears unstable for this quantum computing domain
Training and validation metrics nearly identical, suggesting poor learning

Comparison of PEFT variants: (a) validation loss, (b) token accuracy, (c) training time

Conclusion: Orthonormal initialization (OLoRA) was the worst performing PEFT method in this comparison, likely due to instability with the multimodal architecture or domain characteristics.

Training Strategy

The experimental strategy was organized in two phases: PEFT technique selection and hyperparameter optimization.

Phase 1: PEFT Variant Comparison

Five LoRA variants were compared with controlled configuration (r=16, α=32, 1 epoch):

Variant	Eval Loss ↓	Eval Accuracy ↑	Runtime (s)
rsLoRA	0.622	0.818	1,060
DoRA	0.622	0.818	2,307
rsLoRA (frozen aligner)	0.623	0.817	1,057
LoRA (vanilla)	0.646	0.812	1,056
PiSSA	0.657	0.812	1,172
OLoRA	0.742	0.794	1,067

Comparison of PEFT variants: (a) validation loss, (b) token accuracy, (c) training time

Key findings:

rsLoRA and DoRA achieved equivalent performance (Eval Loss 0.622)
DoRA has 2.18× computational overhead (2,307s vs 1,060s) due to magnitude-direction decomposition
rsLoRA selected for optimal performance-efficiency trade-off

Convergence curves of validation loss for PEFT variants

Phase 2: Rank and Epoch Optimization

With rsLoRA selected, the impact of adapter rank and training duration was investigated:

Configuration	Eval Loss ↓	Eval Accuracy ↑	Notes
r=32, 1 epoch	0.607	0.821	Optimal trade-off
r=64, 1 epoch	0.609	0.822	Marginal improvement
r=16, 1 epoch	0.622	0.818	Baseline rsLoRA
r=32, 2 epochs	0.638	0.825	Slight overfitting
r=128, 3 epochs	0.789	0.822	Severe overfitting

Impact of adapter rank on validation loss

Overfitting analysis: (a) r32-2ep configuration, (b) r128-3ep configuration

Conclusions: rsLoRA with r=32 and 1-2 epochs maximizes generalization while avoiding memorization of the synthetic dataset.

Model Collection

This model is part of the Quantum Assistant collection. All models are merged versions ready for inference:

Model	Configuration	Description
Qwen3-VL-8B-rslora-r32-2	rsLoRA r=32, 2 epochs	Best overall performance
Qwen3-VL-8B-rslora-r32	rsLoRA r=32, 1 epoch	Best generalization
Qwen3-VL-8B-rslora-r64	rsLoRA r=64, 1 epoch	Higher capacity
Qwen3-VL-8B-rslora-r128	rsLoRA r=128, 1 epoch	Maximum capacity
Qwen3-VL-8B-lora	LoRA r=16, 1 epoch	Vanilla LoRA
Qwen3-VL-8B-dora	DoRA r=16, 1 epoch	Magnitude-direction decomposition
Qwen3-VL-8B-pissa	PiSSA r=16, 1 epoch	SVD initialization
Qwen3-VL-8B-olora	OLoRA r=16, 1 epoch	QR orthonormal initialization
Qwen3-VL-8B-rslora-frozen	rsLoRA r=16, frozen aligner	Ablation study
Qwen3-VL-8B-rslora	rsLoRA r=16, 1 epoch	Baseline rsLoRA

Usage

With vLLM

bash

python -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --model samuellimabraz/Qwen3-VL-8B-olora \
    --gpu-memory-utilization 0.92 \
    --max-model-len 12288 \
    --max-num-seqs 16 \
    --max-num-batched-tokens 49152 \
    --enable-chunked-prefill \
    --enable-prefix-caching

With Transformers

python

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "samuellimabraz/Qwen3-VL-8B-olora",
    torch_dtype="auto",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("samuellimabraz/Qwen3-VL-8B-olora")

messages = [
    {"role": "system", "content": "You are a quantum computing expert assistant specializing in Qiskit."},
    {"role": "user", "content": "Create a function that builds a 3-qubit GHZ state and returns the circuit."}
]

messages_with_image = [
    {"role": "system", "content": "You are a quantum computing expert assistant specializing in Qiskit."},
    {"role": "user", "content": [
        {"type": "image", "image": "path/to/circuit.png"},
        {"type": "text", "text": "Implement the quantum circuit shown in the image."}
    ]}
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt"
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=1024)
output = processor.batch_decode(
    generated_ids[:, inputs.input_ids.shape[1]:], 
    skip_special_tokens=True
)[0]
print(output)

Training Details

Dataset

Training Data: Quantum Assistant Dataset
Train Samples: 5,837 (45.1% multimodal)
Validation Samples: 1,239 (45.2% multimodal)
Task Distribution: 30% function completion, 32% code generation, 38% QA
Categories: 7 quantum computing domains

Training Configuration

Parameter	Value
Base Model	Qwen/Qwen3-VL-8B-Instruct
PEFT Method	OLoRA (QR orthonormal initialization)
Rank (r)	16
Alpha (α)	32
Dropout	0.05
Target Modules	all-linear
Learning Rate	2e-4
LR Scheduler	Cosine
Weight Decay	0.01
Warmup Steps	10
Epochs	1
Batch Size	16
Precision	bfloat16
Framework	ms-swift

Freezing Strategy

Component	Status
Vision Encoder (ViT)	❄️ Frozen
Vision-Language Aligner	🔥 Trainable
Language Model (LLM)	🔥 Trainable

Training Infrastructure

GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition (96GB VRAM)
Training Time: ~17.8 minutes (1 epoch)
Tracking: Weights & Biases | TensorBoard

System Prompt

You are a quantum computing expert assistant specializing in Qiskit.
Provide accurate, clear, and well-structured responses about quantum computing concepts,
algorithms, and code implementation. Use Qiskit 2.0 best practices.

Intended Uses & Limitations

Intended Uses

Educational assistance: Learning quantum computing concepts with Qiskit
Code generation: Creating Qiskit circuits from descriptions or diagrams
Documentation: Understanding quantum circuit visualizations
Research prototyping: Rapid development of quantum algorithms

Limitations

Domain specificity: Optimized for Qiskit 2.0; may generate deprecated APIs for older versions
Dataset size: Trained on 5,837 samples; may underperform on rare edge cases
Category imbalance: Better performance on circuits_and_gates than primitives_and_execution
Hardware specifics: Limited coverage of IBM Quantum hardware-specific optimizations
Execution: Generated code requires verification before running on real quantum hardware

Bias and Risks

Model may perpetuate patterns from training data
Visual understanding limited to common diagram styles in Qiskit documentation
May generate syntactically correct but logically incorrect quantum algorithms
Should not be used for production quantum computing without human review

Citation

If you use this model in your research, please cite:

bibtex

@article{braz2026quantumassistant,
  title     = {Quantum Assistant: Specialization of Multimodal Vision-Language Models for Quantum Computing},
  author    = {Braz, Samuel Lima and Leite, Jo{\~a}o Paulo Reus Rodrigues},
  journal   = {Expert Systems with Applications},
  year      = {2026},
  issn      = {0957-4174},
  doi       = {10.1016/j.eswa.2026.133931},
  url       = {https://doi.org/10.1016/j.eswa.2026.133931},
  publisher = {Elsevier}
}

Related Resources

Paper: doi:10.1016/j.eswa.2026.133931
Dataset: samuellimabraz/quantum-assistant
Model Collection: Quantum Assistant Models
Demo: Quantum Assistant Space
Code: GitHub Repository

Acknowledgments

IBM Quantum and Qiskit team for open-source documentation
Qwen Team for the base model
UNIFEI (Universidade Federal de Itajubá) for academic support
Advisor: Prof. João Paulo Reus Rodrigues Leite

License

This model is released under the Apache 2.0 License.