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 /RELEASE_READINESS.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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1.66 kB

Phase Fifteen — Release Readiness Gate Report

Release Gate Checklist

Gate Criterion	Requirement	Verification Evidence	Status
1. Project Name Preserved	Exact name `Q-TensorFormer` preserved across docs and model card	`README.md`, `MODEL_CARD.md`, `qtensorformer_to_gguf.py`	`PASS`
2. Zero `strict=False` Checkpoint Bypasses	Canonical checkpoint loading uses `strict=True`; incompatible checkpoints fail explicitly	`tests/test_scientific_guardrails.py`, `reports/CHECKPOINT_INTEGRITY.md`	`PASS`
3. Explicit Evidence Classification	All reported metrics labeled `MEASURED`, `ESTIMATED`, `SIMULATED`, `THEORETICAL`, `N/A`, `BLOCKED`, or `UNVERIFIED`	`results/raw/*.json`, `reports/BENCHMARK_RESULTS.md`	`PASS`
4. Honest Latency & Energy Reporting	Distinguishes parameter compression from CPU wall-clock speed; never claims estimated energy as measured sensor joules	`reports/ENERGY_MEASUREMENT_METHODOLOGY.md`, `reports/EDGE_INFERENCE_EVALUATION.md`	`PASS`
5. Cold-Start Motivation Attribution	~200s MedGemma 1.5B VRAM load time attributed strictly as developer motivation	`README.md`, `MODEL_CARD.md`, `reports/COLD_START_EVALUATION.md`	`PASS`
6. Full Unit & Guardrail Test Suite	`python -m pytest -v` passes with zero failures	`86 passed, 4 skipped, 0 failed`	`PASS`
7. One-Command Smoke Test	`python scripts/smoke_test.py` passes deterministically	Exit code `0`	`PASS`
8. Automated Scientific Integrity Checker	`python scripts/scientific_integrity_checker.py` passes with zero errors	Exit code `0`	`PASS`