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 / outputs

Commit History

Sep 15

06:34
docs: Hardening claims, provenance tags, and research-grade documentation alignment

Premchandyadav369

7f86f48
06:18
feat: Harden research system with hierarchical KV, phase profiling, matched budgets, and counterfactual validation

Premchandyadav369

8799640

Sep 11

12:32
feat(benchmarks): Add comprehensive baseline comparison system with 14 architectures, 17 metrics, Pareto frontiers, and provenance tags

Premchandyadav369

7becc06
12:23
feat(research): Turn Q-TensorFormer into a verified research system with LFS figure assets

Premchandyadav369

0e4850b

Sep 6

10:04
docs(metrics): add quantitative percentage improvement breakdown (%) vs baselines and dashboard KPI cards

Premchandyadav369

8cac17f
09:58
feat(benchmarks): add comprehensive 11-model baseline comparison study, interactive dashboard table, and publication-grade docs

Premchandyadav369

a997c06
09:48
Implement top-tier research features: PID Dual Controller, GQA, Roofline Analyzer, Early-Exit, Meyer-Wallach Entanglement, and Interactive Visual Dashboard

Premchandyadav369

0431133
09:21
Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture

Premchandyadav369

eaeea8f

Commit History

Sep 15

docs: Hardening claims, provenance tags, and research-grade documentation alignment

feat: Harden research system with hierarchical KV, phase profiling, matched budgets, and counterfactual validation

Sep 11

feat(benchmarks): Add comprehensive baseline comparison system with 14 architectures, 17 metrics, Pareto frontiers, and provenance tags

feat(research): Turn Q-TensorFormer into a verified research system with LFS figure assets

Sep 6

docs(metrics): add quantitative percentage improvement breakdown (%) vs baselines and dashboard KPI cards

feat(benchmarks): add comprehensive 11-model baseline comparison study, interactive dashboard table, and publication-grade docs

Implement top-tier research features: PID Dual Controller, GQA, Roofline Analyzer, Early-Exit, Meyer-Wallach Entanglement, and Interactive Visual Dashboard

Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture