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 / FUNCTIONAL_VALIDATION_REPORT.md

Premchandyadav369

fix(config): Align configuration API with canonical ModelConfig fields and document checkpoint provenance

0b7ee79 about 1 month ago

preview code

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2.24 kB

FUNCTIONAL VALIDATION REPORT: Deterministic Runtime Verification

Date: September 17, 2026
Auditor: Senior ML Systems Engineer & Research Software Auditor
Final Status: FUNCTIONAL_PASS

1. Tokenizer Status

TOKENIZER_STATUS = NOT_FOUND
TRACK_A_QUALITY_STATUS = UNAVAILABLE

Finding: No pretrained tokenizer vocabulary files (tokenizer.json, vocab.json, spm.model) exist in the repository.
Protocol: Per research software audit requirements, no external tokenizer was fabricated or substituted. All inputs for functional and latency testing are valid integer token tensors within the vocabulary bounds: $x \in {0, \dots, V-1}$ where $V = 10,000$.

2. Checkpoint Loading Gate Status

Target: qtensorformer_small.pt
Result: CHECKPOINT_INCOMPATIBLE (Proven in CHECKPOINT_INTEGRITY_REPORT.md).
Policy: Functional validation is executed using the canonical architectural definition initialized deterministically, avoiding random hyperparameter guessing or silent weight substitution.

3. Shape & Finite Output Verification

Deterministic forward passes were executed across varying batch and sequence dimensions on CPU:

Input Batch ($B$)	Sequence ($T$)	Integer Token Range	QTF Output Shape	Dense Output Shape	Output Dtype	Finite Check (NaN/Inf)	Status
1	1	$[0, 10000)$	`[1, 1, 10000]`	`[1, 1, 10000]`	`torch.float32`	ALL FINITE (0 NaN, 0 Inf)	PASS
1	8	$[0, 10000)$	`[1, 8, 10000]`	`[1, 8, 10000]`	`torch.float32`	ALL FINITE (0 NaN, 0 Inf)	PASS
1	32	$[0, 10000)$	`[1, 32, 10000]`	`[1, 32, 10000]`	`torch.float32`	ALL FINITE (0 NaN, 0 Inf)	PASS

4. Architectural Invariants Verified

Batch Dimension: Preserved across all forward layers without reshaping artifacts.
Sequence Dimension: Output sequence dimension matches input sequence dimension exactly.
Vocabulary Dimension: Logits projection maps to vocab_size = 10000.
Numerical Stability: Outputs contain zero NaN or Inf values across all test configurations.

FINAL STATUS: FUNCTIONAL_PASS