Instructions to use merileijona/quantumgpt-124m-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use merileijona/quantumgpt-124m-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="merileijona/quantumgpt-124m-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("merileijona/quantumgpt-124m-v3")
model = AutoModelForCausalLM.from_pretrained("merileijona/quantumgpt-124m-v3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use merileijona/quantumgpt-124m-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "merileijona/quantumgpt-124m-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "merileijona/quantumgpt-124m-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/merileijona/quantumgpt-124m-v3

SGLang

How to use merileijona/quantumgpt-124m-v3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "merileijona/quantumgpt-124m-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "merileijona/quantumgpt-124m-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "merileijona/quantumgpt-124m-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "merileijona/quantumgpt-124m-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use merileijona/quantumgpt-124m-v3 with Docker Model Runner:
```
docker model run hf.co/merileijona/quantumgpt-124m-v3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

QuantumGPT-124M-v3 (ctx512)

Third-generation QuantumGPT: a GPT-2 small (124M) model fine-tuned to generate OpenQASM 2.0 circuits from natural-language descriptions, with the context window extended from 256 to 512 tokens via position-embedding extension.

QSS-Core v0.1: 97.70 / 97.28 (two-seed replication) — up from 91.61 (v2) and 75.00 (v1 baseline) on the same 120-sample stratified development benchmark.

Model details


Architecture	GPT-2 small (12 layers, 12 heads, 768 embd), tied embeddings
Context window	512 tokens (extended from 256; positions 0–255 preserved, 256–511 initialized as continuation of learned embedding table)
Parameters	~124M
Lineage	`quantumgpt-124m-v2` → +dataset/format mutations (QEXP-100006) → +ctx512 extension & full-context training (this model)
Training data	merileijona/quantum-circuits-21k (validated OpenQASM 2.0 description→circuit pairs) with format-anchor oversampling ×2, long-circuit oversampling ×2, deterministic paraphrase augmentation
Tokenizer	GPT-2 BPE
Prompt format	`<

Benchmark results (QSemBench-dev-v0.1, n=120 stratified, greedy decode)

Scoring: qiskit qasm2 parse/compile + Aer statevector fidelity ≥ 0.99 vs reference circuit, entanglement check where reference entangles, prompt-perturbation robustness probes. Full methodology in the technical report below.

Metric	v1 baseline	v3 (seed 42)	v3 (seed 43)
qsyntax	95.00	99.17	99.17
qcompile	95.00	99.17	99.17
qsemantic	76.67	97.50	96.67
qexecute	76.67	97.50	96.67
qgeneralize	75.26	96.91	96.91
qrobust	90.22	97.44	98.28
qefficiency	100.00	100.00	100.00
QSS-Core v0.1	77.91*	97.70	97.28

*v1 baseline reported under pre-cap-lift conditions; see report for details.

Capabilities (qualitative evaluation)

Strong on: Bell/GHZ-style entanglement, teleportation structure, Deutsch-Jozsa architecture, three-qubit error-correction encoding, mid-circuit measurement with classical control (if(c==1)), parameterized rotations, novel state preparations adjacent to training distribution (e.g., plausible W-state synthesis with correct arccos(1/√3) angles).

Weak on: negation constraints ("WITHOUT any entangling gates" ignored), compound multi-part instructions (partial completion), physics-level self-checking (no-cloning request produces a circuit instead of refusal), casual/typo-heavy phrasing degrades semantic accuracy.

Limitations

Development benchmark shares its generator with the training corpus; truly held-out evaluation requires newly generated circuits.
Trained exclusively on synthetic Grok-generated circuits validated by qiskit parse checks.
No alignment/refusal behavior: any prompt returns some QASM program, meaningful or not.
512-token context is still limiting for large-scale algorithms (Shor-9 encoding sits at the edge).
English prompts only.

Intended use

Research on small-scale code-generation models for quantum computing, education, and as a base for further scaling experiments. Not intended for production quantum-circuit synthesis without human verification of every generated circuit.

Reproducibility

Training and evaluation harness: deterministic orchestrator with sealed experiment packets. Key artifacts: champion lineage QEXP-100006 → QEXP-200042/200043; replication spread between seeds: 0.41 QSS points. See technical report in this repository for full experiment history.

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for merileijona/quantumgpt-124m-v3

Quantizations

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Dataset used to train merileijona/quantumgpt-124m-v3

Collection including merileijona/quantumgpt-124m-v3

QuantumGPT — Quantum Circuit Generation

Collection

Three GPT-style models trained from scratch on two OpenQASM 2 datasets. • 6 items • Updated 5 days ago