Instructions to use Premchan369/Q-TensorFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Premchan369/Q-TensorFormer with Transformers:

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

pipe = pipeline("text-generation", model="Premchan369/Q-TensorFormer", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Premchan369/Q-TensorFormer", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Premchan369/Q-TensorFormer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Premchan369/Q-TensorFormer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Premchan369/Q-TensorFormer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Premchan369/Q-TensorFormer

SGLang

How to use Premchan369/Q-TensorFormer 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 "Premchan369/Q-TensorFormer" \
    --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": "Premchan369/Q-TensorFormer",
		"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 "Premchan369/Q-TensorFormer" \
        --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": "Premchan369/Q-TensorFormer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Premchan369/Q-TensorFormer with Docker Model Runner:
```
docker model run hf.co/Premchan369/Q-TensorFormer
```

Q-TensorFormer / reports /REPOSITORY_INVENTORY.md

Premchandyadav369

Complete 16-phase scientific hardening, strict checkpoint audit, empirical benchmarks, and documentation

7aefd9e 19 days ago

preview code

Raw History Blame Contribute Delete

7.25 kB

Phase Zero — Complete Repository Inventory

1. Root-Level Source, Configuration, and Export Files

Path	Type	Size (Bytes)	Role & Status
`README.md`	Documentation	~56 KB	Primary repository documentation & Hugging Face Hub landing page
`MODEL_CARD.md`	Documentation	~18 KB	Standalone model card with architecture, evidence, and limitations
`config.json`	Configuration	834 B	Hugging Face `QTensorFormerConfig` JSON descriptor
`configuration_qtensorformer.py`	Source (Family 4)	11,708 B	Hugging Face `PretrainedConfig` (`QTensorFormerConfig`)
`modeling_qtensorformer.py`	Source (Family 4)	36,326 B	Hugging Face `PreTrainedModel` (`QTensorFormerForCausalLM`)
`qtensorformer_vision.py`	Source	10,842 B	Experimental multimodal vision-language extension (`QTensorFormerForConditionalGeneration`)
`qtensorformer_to_gguf.py`	Deployment Tool	12,150 B	Pure-Python GGUF v3 binary serializer & Ollama `Modelfile` generator (`strict=True` enforced)
`export_onnx.py`	Deployment Tool	4,120 B	ONNX graph exporter and structural checker for `TTLinear`
`q_tensor_former.py`	Legacy Prototype (Family 1)	26,462 B	Historical single-file v1 prototype (`CFG`, `QTensorFormer`)
`q_tensor_former_v2.py`	Legacy Prototype (Family 2)	43,441 B	Historical single-file v2 prototype (`Config`, `QTensorFormer`)
`qtensorformer_small.pt`	Legacy Checkpoint	5,432,305 B	Historical external TensorLy container checkpoint (incompatible under `strict=True`)
`demo.py`	Script	~12 KB	Interactive demonstration script
`app.py`	UI Script	~14 KB	Gradio telemetry & interactive dashboard
`requirements.txt`	Packaging	145 B	Core Python dependencies
`pyproject.toml` / `setup.py`	Packaging	~2 KB	Package build metadata

2. Core Research Package (`src/` — Family 3)

Path	Classes / Functions	Status
`src/__init__.py`	Package exports	Active
`src/config.py`	`ModelConfig`, `TrainingConfig`, `BudgetConfig`, `ExperimentConfig`, `validate_model_config`	Active, Canonical Research Config
`src/models.py`	`QTensorFormer`, `DenseBaseline`, `PositionalEncoding`, `create_model`	Active, Canonical Research Model
`src/blocks.py`	`HybridBlock`	Active
`src/attention.py`	`MultiHeadAttention` (SDPA, GQA/MQA, RoPE, optional QKSAM fidelity kernel)	Active
`src/tensor_layers.py`	`TTLinear`, `TTFeedForward`, `factorize_dim`	Active
`src/information_state.py`	`TokenInformationState` (8D normalized information vector $z_t$ + LOFO ablation)	Active
`src/resource_allocator.py`	`MarginalValueModel`, `InformationValueAllocator`, `PIDDualSubgradientController`, `ConstrainedDecisionEngine`, `AllocationBudget`	Active
`src/kv_cache.py`	`AdaptiveKVCache`, `HierarchicalAdaptiveKVCache`, `QuantizedKVTensor`, `KVPrecision`	Active
`src/quantum_backend.py`	`QuantumBackend`, `ClassicalSurrogateUnitary`, `BackendType`, `compute_meyer_wallach_entanglement`, `compute_quantum_expressibility`	Active
`src/quantum_layers.py`	`QuantumAngleEmbedding`, `EntanglementMonitor`, `ClassicalQuantumFallback`	Active
`src/router.py`	`QuantumRouter`	Active (fixed deterministic `__init__` layer registration)
`src/scheduler.py`	`RankScheduler`, `BudgetAwareScheduler`	Active
`src/hardware_cost_model.py`	`HardwareCostModel`, `HardwareRooflineAnalyzer`, `KernelBreakdownProfiler`	Active
`src/phase_aware_profiler.py`	`PhaseAwareProfiler`, `PhaseProfileResult`	Active
`src/counterfactual_learning.py`	`CounterfactualDatasetGenerator`, `PolicyBenchmarkEvaluator`	Active
`src/baseline_comparator.py`	`BaselineComparator`	Active
`src/baselines.py`	`StandardTransformer`, `DistilledTransformer`, `PrunedTransformer`	Active
`src/hf_model.py`	Re-exports `QTensorFormerConfig` and `QTensorFormerForCausalLM`	Active
`src/validator.py`	`ScientificClaimValidator`	Active

3. Custom Kernel & Acceleration Modules (`kernels/`)

Path	Classes / Functions	Status
`kernels/tt_kernels.py`	`FusedTTContraction`, `DynamicINT4Quantizer`, `HAS_TRITON`	Active (PyTorch fallback on CPU; Triton JIT when CUDA + Triton available)

4. Verification & Test Suite (`tests/`)

Test File	Scope	Status
`tests/test_advanced_features.py`	GQA, Early Exit, Roofline, Kernel Breakdown, Marginal Value	Passing
`tests/test_baseline_comparator.py`	Multi-baseline evaluation & Pareto domination	Passing
`tests/test_frontier_v10.py`	GGUF v3 binary spec, Vision-Language forward, Triton/PyTorch fallback, ONNX export	Passing
`tests/test_hf_integration.py`	`AutoConfig`, `AutoModelForCausalLM`, `.generate()`, `save_pretrained`/`from_pretrained`	Passing
`tests/test_information_state.py`	8D `TokenInformationState` bounds, normalization, ablation	Passing
`tests/test_kv_cache.py`	`AdaptiveKVCache` FP16/INT8/INT4 quantization, sink retention, eviction	Passing
`tests/test_model.py`	Model forward/backward shapes and presets	Passing
`tests/test_nested_tt.py`	Nested TT core slicing and rank consistency	Passing
`tests/test_quantum_backend.py`	`QuantumBackend`, `ClassicalSurrogateUnitary`, Meyer-Wallach, Expressibility	Passing
`tests/test_quantum_layers.py`	Quantum embeddings & classical fallback (3 PennyLane-specific tests skipped when `pennylane` absent)	Passing / Skipped
`tests/test_research_hardening.py`	Counterfactual learning, hierarchical KV, phase-aware profiler	Passing
`tests/test_resource_allocator.py`	`InformationValueAllocator`, hysteresis, PID dual controller	Passing
`tests/test_scientific_guardrails.py`	`strict=True` checkpoint enforcement, GGUF strict check, config validation, router init	Passing
`tests/test_tensor_layers.py`	`TTLinear` & `TTFeedForward` shapes, reconstruction, compression	Passing
`tests/test_v4.py`	End-to-end integration tests	Passing

5. Benchmark Scripts, Raw Results, and Forensic Reports

Scripts (scripts/):
- scripts/run_scientific_benchmark.py — End-to-end reproducible benchmark suite (RUN-20261013-QTF-HARDENING)
- scripts/scientific_integrity_checker.py — Automated scientific integrity & evidence-label auditor
- scripts/smoke_test.py — Fast deterministic one-command smoke test
- scripts/generate_figures.py & scripts/run_experiments.py — Historical figure and experiment generators
Empirical Results (results/):
- results/metadata/run_metadata.json
- results/raw/checkpoint_integrity.json
- results/raw/cold_start_and_edge.json
- results/raw/tt_compression_audit.json
- results/raw/controller_audit.json
- results/raw/kv_cache_audit.json
- results/raw/quantum_pathway_audit.json
- results/processed/benchmark_summary.json
- results/processed/benchmark_summary.csv
Reports (reports/):
- Full 22-document forensic, architectural, checkpoint, benchmark, edge/cold-start, energy, novelty, and release-readiness report suite.

Phase Zero — Complete Repository Inventory

1. Root-Level Source, Configuration, and Export Files

2. Core Research Package (src/ — Family 3)

3. Custom Kernel & Acceleration Modules (kernels/)

4. Verification & Test Suite (tests/)

5. Benchmark Scripts, Raw Results, and Forensic Reports

2. Core Research Package (`src/` — Family 3)

3. Custom Kernel & Acceleration Modules (`kernels/`)

4. Verification & Test Suite (`tests/`)