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 /CHECKPOINT_INTEGRITY.md

Premchandyadav369

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

7aefd9e about 1 month ago

preview code

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Phase Two — Checkpoint Integrity & Strict-Load Audit Report

1. Forensic Inspection of `qtensorformer_small.pt`

Data source: results/raw/checkpoint_integrity.json (MEASURED).

File Path: qtensorformer_small.pt
File Size: 5,432,305 bytes (5.18 MB)
SHA-256 Digest: 9a4e61f70ba084e6c4e38b3e7cc8479d1f2ff43c51fff7bcf2ee07191dc3b0db
Git Provenance: Committed in 82fd973c (May 6, 2026) from an external container (/app/qtensorformer).
Top-Level Dictionary Keys: ['model_state_dict', 'config', 'params', 'baseline_params', 'compression_ratio', 'version']
Recorded Metadata:
- version: "1.0.0"
- params: 1,347,747
- baseline_params: 16,146,432
- compression_ratio: 11.9803
Actual State-Dict Structure:
- Contains 113 tensors summing to exactly 1,347,747 elements.
- Sample keys:
  - embeddings.token_embedding.weight ([10000, 128])
  - embeddings.position_embedding.weight ([64, 128])
  - embeddings.quantum_enhancer.qlayer.weights ([2, 4])
  - blocks.0.attention.q_proj.weight ([128, 128])
  - blocks.0.ffn.layer.weight.factors.factor_0 .. factor_5 (TensorLy FactorizedTensor format)
  - blocks.0.ffn.rank_scheduler.net.0.weight

2. Canonical `strict=True` Loading Test Results

Per Phase Two rules, model.load_state_dict(sd, strict=True) was executed against the repository's canonical implementations without altering checkpoint tensors or model dimensions:

Target Model Class	Config Class	Model Params	Model Keys	Checkpoint Keys	Exact Key Overlap	Missing Keys	Unexpected Keys	`strict=True` Result
`src.models.QTensorFormer`	`src.config.ModelConfig`	`1,524,186`	`185`	`113`	`0` (0.0%)	`185`	`113`	`FAIL` (`RuntimeError`)
`modeling_qtensorformer.QTensorFormerForCausalLM`	`QTensorFormerConfig`	`2,767,296`	`52`	`113`	`0` (0.0%)	`52`	`113`	`FAIL` (`RuntimeError`)

Why `qtensorformer_small.pt` Cannot Load Under `strict=True`

Incompatible Tensor Factorization Library: qtensorformer_small.pt stores FFN weights using tensorly / tltorch 6-core factors.factor_0..5 naming on a single fused FFN layer (blocks.i.ffn.layer), whereas src.tensor_layers.TTFeedForward uses two 4-core pure-PyTorch TTLinear projections (blocks.i.tt_ffn.up_proj.cores.0..3 and blocks.i.tt_ffn.down_proj.cores.0..3).
Different Module Hierarchy: qtensorformer_small.pt uses embeddings.token_embedding and blocks.i.ffn.rank_scheduler.net, whereas src.models.QTensorFormer uses embedding.weight, blocks.i.info_engine, and blocks.i.allocator.

3. Canonical Round-Trip `strict=True` Verification

While the legacy external file qtensorformer_small.pt is incompatible, both canonical repository architectures pass strict=True save/load round-trip verification (results/raw/checkpoint_integrity.json and scripts/smoke_test.py):

Canonical Implementation	Serialization Method	Reload Method	`strict=True` Result	Output Numerical Match
`src.models.QTensorFormer`	`torch.save(model.state_dict(), path)`	`model.load_state_dict(sd, strict=True)`	`PASS` (185/185 keys)	Max diff `0.0`
`modeling_qtensorformer.QTensorFormerForCausalLM`	`model.save_pretrained(dir)`	`QTensorFormerForCausalLM.from_pretrained(dir)`	`PASS` (52/52 keys)	Max diff `< 1e-6`

Phase Two — Checkpoint Integrity & Strict-Load Audit Report

1. Forensic Inspection of qtensorformer_small.pt

2. Canonical strict=True Loading Test Results

Why qtensorformer_small.pt Cannot Load Under strict=True

3. Canonical Round-Trip strict=True Verification

1. Forensic Inspection of `qtensorformer_small.pt`

2. Canonical `strict=True` Loading Test Results

Why `qtensorformer_small.pt` Cannot Load Under `strict=True`

3. Canonical Round-Trip `strict=True` Verification