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_FORENSICS_AUDIT_V2.md

Premchandyadav369

feat: complete scientific repair, canonical checkpoint training, tokenizer, GQA zero-copy, and release gating

d279df0 13 days ago

preview code

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Q-TensorFormer — Repository Forensics & Scientific Verification Audit (v2)

Timestamp: 2026-10-13T12:00:00Z
Canonical Project Name: Q-TensorFormer
Host Environment: Windows 10, Python 3.11.9, PyTorch 2.10.0+cpu, Transformers 4.41.2, NumPy 2.4.6
Repository URI: https://huggingface.co/Premchan369/Q-TensorFormer
Audit Version: v2 (Post-Repair & Canonical Checkpoint Release)
Historical Audit Preserved: reports/FORENSIC_AUDIT.md (v1 historical findings preserved without overwrite)

1. Traceable Component Classification Matrix

Component	Actual Implementation	Evidence	Status	Required Action / Resolution
Canonical Model	`src/models.py` (`QTensorFormer`, `DenseBaseline`, `PositionalEncoding`)	Full forward/backward passes verified in `tests/test_model.py`, `tests/test_advanced_features.py`, and `scripts/smoke_test.py`. Strict config validation enforced; context-window boundaries and dynamic sinusoidal positional encodings supported.	Verified Working	None. Source of truth established; config validation strictly active.
Configuration	`src/config.py` (`ModelConfig`, `TrainingConfig`, `BudgetConfig`, `validate_model_config`)	`tests/test_scientific_guardrails.py` confirms rejection of incomplete configs without default guessing; YAML serialization roundtrip verified.	Verified Working	None. Canonical research schema validated.
Checkpoint	`src/checkpoint.py`, `checkpoints/qtensorformer_canonical_trained.pt` (SHA-256 `9e5a96913477263d...`), legacy `qtensorformer_small.pt`	Legacy checkpoint confirmed incompatible (`0/113` key overlap, TensorLy container artifact). Newly trained canonical checkpoint loads with 100% key match under `strict=True`, verifies parameter values, records full training provenance and companion `.metadata.json`.	Verified Working	Preserved legacy artifact untouched; canonical trained checkpoint deployed as default.
Tokenizer	`src/tokenizer.py` (`QTensorFormerTokenizer`), `checkpoints/tokenizer/`	`tests/test_tokenizer_and_training.py` verifies deterministic 256-token vocabulary, `<pad>=0, <bos>=1, <eos>=2, <unk>=3`, exact text roundtrip, batch padding + `attention_mask` generation, and model vocabulary alignment checks.	Verified Working	Canonical tokenizer saved to `checkpoints/tokenizer/` and synced with model output heads.
Training Pipeline	`src/training.py` (`LanguageModelTrainer`, `DistillationTrainer`), `scripts/train_and_evaluate.py`, `scripts/distill.py`	Real training executed on `data/qtensorformer_corpus.txt`. Loss decreased from 5.8646 (ppl 352.35) to 3.8086 (ppl 45.09) on QTensorFormer; matched DenseBaseline decreased from 5.7282 to 3.8242. Metrics logged to `results/raw/training_and_quality_validation.json`.	Verified Working	Real training pipeline active and reproducible via deterministic seeds.
Tensor-Train Compression	`src/tensor_layers.py` (`TTLinear`, `TTFeedForward`), `kernels/tt_kernels.py`	`tests/test_tensor_layers.py` and `tests/test_nested_tt.py` verify exact dense matrix reconstruction, instantaneous rank slicing (r=1,2,4,8), and gradient flow. PyTorch native fallback active on CPU; Triton kernels available on CUDA.	Verified Working	Zero-dependency PyTorch fallback active; Triton acceleration ready when GPU present.
Adaptive Rank	`src/resource_allocator.py` (`InformationValueAllocator`, `PIDDualSubgradientController`)	`tests/test_resource_allocator.py` and `tests/test_model.py` verify 100-step entropy stabilization, hysteresis threshold ($\tau=0.15$) suppressing chattering, and Pareto budget enforcement.	Verified Working	Dynamic rank switching verified under closed-loop control.
Adaptive Attention	`src/attention.py` (`MultiHeadAttention`), `modeling_qtensorformer.py`	Zero-copy 5D GQA broadcasting (`num_queries_per_kv > 1`) verified without K/V replication. IEEE-754 NaN bug resolved via boolean `masked_fill`. Full causal + padding mask combinations verified without NaNs.	Verified Working	K/V expansion memory copy eliminated; padding masks fully tested.
KV-Cache Optimization	`src/kv_cache.py` (`AdaptiveKVCache`, `HierarchicalAdaptiveKVCache`, `QuantizedKVTensor`)	`tests/test_kv_cache.py` verifies sink token retention across long horizons, dynamic precision transitions (FP16 $\rightarrow$ INT8 $\rightarrow$ INT4), and metadata tracking.	Verified Working	Quantized INT4/INT8 tensors and sink-preserving eviction fully functional.
Quantum Pathway	`src/quantum_backend.py`, `src/quantum_layers.py`, `src/router.py`	`ClassicalSurrogateUnitary` executes SU($2^N$) Lie group emulator deterministically without external simulator dependencies. Meyer-Wallach entanglement ($Q \in [0, 1]$) and Haar expressibility divergence verified in `tests/test_quantum_backend.py`.	Verified Working (Classical Surrogate) / Partially Working (Physical QPU)	Labeled strictly as `SIMULATED` (classical surrogate); physical QPU bridge stubbed as `HARDWARE_INTERFACE`.
Hugging Face Integration	`configuration_qtensorformer.py`, `modeling_qtensorformer.py`, `checkpoints/hf_canonical_trained/`	`tests/test_hf_integration.py` verifies `AutoConfig`, `AutoModelForCausalLM`, `generate()`, and `save_pretrained`/`from_pretrained` roundtrips without ignoring missing keys.	Verified Working	`keys_to_ignore_at_loading = []` verified. Canonical checkpoint export completed.
Export & Deployment	`qtensorformer_to_gguf.py`, `export_onnx.py`	`tests/test_scientific_guardrails.py` verifies GGUF exporter strictly enforces `strict=True` and rejects mismatched checkpoints. ONNX export verified in `tests/test_frontier_v10.py`.	Verified Working	`strict=False` bypasses removed; exports produce verifiable target artifacts.
Benchmarks	`scripts/run_scientific_benchmark.py`, `scripts/scientific_integrity_checker.py`, `scripts/smoke_test.py`	94 unit tests passing (0 failing, 4 PennyLane-specific skipped). All empirical outputs logged to `results/raw/`, `results/processed/`, and `results/metadata/`. Every metric explicitly classified as `MEASURED`, `SIMULATED`, or `ESTIMATED`.	Verified Working	Automated integrity checker enforces evidence tagging across all reported figures.

2. Summary of Resolved Blockers

Checkpoint Blocker: Legacy qtensorformer_small.pt had 0/113 key overlap under strict=True due to an incompatible external TensorLy structure. A genuine canonical checkpoint (checkpoints/qtensorformer_canonical_trained.pt) has been trained and saved with complete provenance, SHA-256 fingerprinting, and strict loading verification.
Attention Mask NaN Bug: Replaced vulnerable mask * float("-inf") multiplications in src/attention.py and src/baselines.py with boolean masked_fill, guarding against all-masked padding query rows and preventing NaN generation in both forward and backward passes.
GQA Memory Copy Blocker: Eliminated repeat_interleave and .expand().reshape() memory copies in src/attention.py and modeling_qtensorformer.py. GQA now broadcasts key and value heads directly via 5D zero-copy views.
Tokenizer Missing Infrastructure: Implemented QTensorFormerTokenizer in src/tokenizer.py supporting deterministic vocabulary building, explicit special tokens (<pad>=0, <bos>=1, <eos>=2, <unk>=3), batch encoding with attention masks, model alignment validation, and JSON artifact persistence.
Training and Evaluation Blocker: Implemented LanguageModelTrainer, DistillationTrainer, and evaluate_model in src/training.py and src/metrics.py, making both canonical LM training and distillation fully functional.