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 / GENERATION_VALIDATION_REPORT.md

Premchandyadav369

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

0b7ee79 about 2 months ago

preview code

Raw History Blame Contribute Delete

2.39 kB

GENERATION VALIDATION REPORT: Autoregressive Decoding Audit

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

1. Generation Interface Verification

The repository provides generation interfaces at two levels:

Model-Level Method: QTensorFormer.generate() in src/models.py (with AdaptiveKVCache).
Hugging Face Integration: QTensorFormerForCausalLM in src/hf_model.py (inherits transformers.GenerationMixin).
Pure PyTorch Greedy Loop: Evaluated via standard causal next-token prediction argmax(logits[:, -1, :]) directly on the model's forward() output contract.

2. Deterministic Reproducibility Audit

Deterministic greedy decoding was executed twice from identical initial input prompt [10, 25, 30] with seed=42:

Test Run Comparison

src.models.QTensorFormer:
- Run 1 Tokens: [10, 25, 30, 5278, 5278, 5278, 5278, 5278]
- Run 2 Tokens: [10, 25, 30, 5278, 5278, 5278, 5278, 5278]
- Match: 100% Identical (Zero divergence)
q_tensor_former.QTensorFormer:
- Run 1 Tokens: [10, 25, 30, 2856, 1538, 2498, 2498, 2498]
- Run 2 Tokens: [10, 25, 30, 2856, 1538, 2498, 2498, 2498]
- Match: 100% Identical (Zero divergence)

3. Per-Token Sequence Growth & Dimension Trace

Step	Current Sequence Length ($T$)	Logits Shape	Selected Next Token ID	Next Sequence Length ($T+1$)	Invariant Check
Prompt	3	N/A	N/A	3	Initial prompt
1	3	`[1, 3, 10000]`	$5278$	4	VALID
2	4	`[1, 4, 10000]`	$5278$	5	VALID
3	5	`[1, 5, 10000]`	$5278$	6	VALID
4	6	`[1, 6, 10000]`	$5278$	7	VALID
5	7	`[1, 7, 10000]`	$5278$	8	VALID

4. Verification Checklist

Input IDs semantics: Int64 tensor indexing within $[0, V-1]$.
Logits semantics: Real-valued unnormalized log-probabilities over vocabulary.
Next-token selection: Greedy argmax along vocabulary dimension.
Sequence growth: Monotonic single-token concatenation.
Determinism: Repeated execution with identical prompt and seed yields identical sequences.
Numerical stability: No NaN or Inf encountered during unrolling.

FINAL STATUS: GENERATION_PASS