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 /EXISTING_FAILURES.md

Premchandyadav369

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

7aefd9e 13 days ago

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Phase Zero — Existing Failures, Defects, and Skipped Tests

1. Historical & Pre-Hardening Failures Recorded

1.1 Legacy Checkpoint Incompatibility (`qtensorformer_small.pt`)

Symptom: Attempting to load qtensorformer_small.pt into src.models.QTensorFormer or modeling_qtensorformer.QTensorFormerForCausalLM with strict=True raises RuntimeError: Error(s) in loading state_dict.
Root Cause: qtensorformer_small.pt was exported from an external TensorLy container (/app/qtensorformer, version 1.0.0) with state-dict keys blocks.{0,1,2}.ffn.layer.weight.factors.factor_{0..5} and embeddings.quantum_enhancer.qlayer.weights. None of the repository's pure-PyTorch TT classes (src.tensor_layers.TTLinear or modeling_qtensorformer.TensorTrainLinear) use TensorLy FactorizedTensor naming (0/113 keys match).
Resolution: Per Phase Two non-negotiable rules, strict=False is prohibited and checkpoint tensors are never altered to fake compatibility. qtensorformer_small.pt is quarantined as a legacy external artifact (FAIL_STRICT_LOAD), and all benchmarks run on verified canonical models (src.models.QTensorFormer and modeling_qtensorformer.QTensorFormerForCausalLM) with strict=True round-trip verification.

1.2 Historical Config Attribute Mismatch (`backend_type`, `d_model`, `n_heads`, `n_layers`, `dropout`)

Symptom: An earlier external notebook attempted to instantiate src.models.QTensorFormer using a raw dictionary or mismatched Hugging Face config class that lacked src.config.ModelConfig attributes.
Root Cause: Mixing Family 4 (QTensorFormerConfig) and Family 3 (ModelConfig) without property bridges or validation.
Resolution:
- src.config.validate_model_config() explicitly validates required attributes (d_model, n_heads, n_layers, ff_multiplier, max_seq_len, vocab_size) and raises ConfigurationValidationError without guessing defaults.
- QTensorFormerConfig provides read-only property accessors (d_model, n_layers, n_heads, n_kv_heads, max_seq_len) and constructor keyword mapping so both config families remain unambiguous and tested.

2. Source Defects Discovered and Fixed During This Audit

Defect ID	File & Location	Description of Defect	Fix Implemented	Regression Test Added
DEF-01	`qtensorformer_to_gguf.py` (lines 216–231)	Fell back to `qtensorformer_small.pt` when `pytorch_model.bin` was absent and called `model.load_state_dict(state_dict, strict=False)`, silently ignoring all 113 unexpected keys and exporting untrained random weights as if they were loaded from the checkpoint.	Removed automatic fallback to `qtensorformer_small.pt`; changed checkpoint loading to enforce `strict=True` (`strict_load: bool = True`) and raise `RuntimeError` on mismatch; added explicit `[EXPORT_MODE: INITIALIZED_ARCHITECTURE]` logging when exporting an uninitialized model directory.	`tests/test_scientific_guardrails.py::test_gguf_exporter_rejects_incompatible_checkpoint_under_strict`
DEF-02	`configuration_qtensorformer.py` (line 119)	Set `keys_to_ignore_at_loading = ["model.embed_tokens.weight"]`, which could allow Hugging Face `from_pretrained` to silently ignore a missing token embedding matrix.	Set `keys_to_ignore_at_loading = []` so all model parameters are strictly required during loading.	`tests/test_scientific_guardrails.py::test_hf_config_does_not_ignore_missing_weights`
DEF-03	`src/router.py` (lines 91–93)	`QuantumRouter.forward()` lazily created `self._q_out_proj = nn.Linear(self.q_input_dim, d_model).to(x.device)` on the first forward call, meaning parameters were missing from `router.parameters()` right after `__init__()`.	Registered `self._q_out_proj = nn.Linear(q_input_dim, d_model)` deterministically inside `QuantumRouter.__init__()`.	`tests/test_scientific_guardrails.py::test_quantum_router_deterministic_init_registration`

3. Skipped Tests & Environment Blockers

Item	Status	Reason
`tests/test_quantum_layers.py` (3 PennyLane circuit tests)	`SKIPPED`	Optional package `pennylane` is not installed in the default CPU environment; `ClassicalSurrogateUnitary` and `ClassicalQuantumFallback` are active and tested.
`tests/test_tensor_layers.py::test_tltorch_comparison`	`SKIPPED`	Optional package `tltorch` is not installed; native PyTorch `TTLinear.to_matrix()` reconstruction is verified to `< 1.3e-6` max absolute error.
Physical GPU / CUDA Kernel Execution	`BLOCKED / N/A`	Host has no NVIDIA GPU (`torch.cuda.is_available() == False`). Triton kernel path falls back cleanly to PyTorch tensor contraction.
Hardware Power Sensor (NVML / RAPL / Wall Meter)	`UNVERIFIED / N/A`	Host does not expose NVML or unprivileged RAPL counters; energy figures are explicitly labeled `ESTIMATED (LEVEL 1/2/3)` and never reported as measured sensor joules.