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

Commit History

Nov 5 7 days ago

17:17
feat: SEO and discoverability expansion, viral launch kit, FAQ schema, and trending tags

Premchandyadav369

7d566f9
17:06
feat: real-world production pillars, OpenAI streaming API, and reasoning benchmarks

Premchandyadav369

000c57b

Nov 4 8 days ago

09:16
docs: sync SPACE_README.md short_description

Premchandyadav369

bedaa67
09:15
feat(seo): maximize discoverability, Google Scholar metadata, interactive HF widget, LLM crawler schemas, zero em dashes, and one-click quickstart downloads

Premchandyadav369

5175adb

Nov 2 10 days ago

12:51
feat(systems): add NVIDIA TRT-LLM plugin, vLLM continuous batching adapter, multi-architecture roofline model, test suite, updated academic papers, and systems research dossier

Premchandyadav369

383a1cd

Oct 22 21 days ago

10:22
feat(paper): add real-time cyber-physical deployments, drone uav, ar eyewear, and fleet roi to 6-page paper

Premchandyadav369

1dfc8c4
10:02
feat(space): sync 10-tab app.py with layman guide, fleet roi and sla sandbox

Premchandyadav369

c58e87b
08:04
feat(space): sync hardened app.py with swarm forecaster and core spectrum inspector

Premchandyadav369

05c5629
07:00
feat(ui): sync app.py with predictive future forecaster & cyber-physical digital twin

Premchandyadav369

cb2982d
06:37
docs(paper): exact IEEE layout standards, 6 pages, table-aligned non-overlapping equations & 7 figures

Premchandyadav369

3d24ee3
05:23
docs(paper): camera-ready IEEE paper with pre-rendered KaTeX, edge inference title, Dr. Brahmam 1st author, 2024-2026 citations & expanded hardware benchmarks

Premchandyadav369

114f6bd
05:05
docs(paper): generate and upload camera-ready IEEE 6-page conference PDF

Premchandyadav369

0c22285
04:44
docs: add 6-page IEEE conference manuscript with author Dr. Madala Guru Brahmam and V C Premchand Yadav, native TikZ diagrams, and zero em-dashes

Premchandyadav369

79497ea

Oct 21 22 days ago

14:40
docs: add formal camera-ready IEEE Transactions manuscript (ieee_transactions_qtensorformer.tex)

Premchandyadav369

d7fe943
09:36
docs: add beginner & layman guide with embedded figures and master benchmark tables to README.md

Premchandyadav369

0ae51bd
09:24
feat(neurips-ieee): camera-ready publication artifacts, Theorems 1-4, 50-trial statistical significance, interactive deep-dive tab

Premchandyadav369

6d1f076

Oct 19 24 days ago

03:48
fix(zerogpu): sync spaces first import order with space

Premchandyadav369

59d4d35
03:42
fix(space): sync spaces.GPU ZeroGPU integration and 6.30.0 SDK

Premchandyadav369

de00a85
03:31
fix(app): sync demo module-level instance and queue with space

Premchandyadav369

72bef29
03:26
fix(app): sync error logging with space app.py

Premchandyadav369

02dab15
03:26
fix(hf): add GenerationMixin and safe pad_token_id attribute access

Premchandyadav369

377d9af
03:19
feat: complete 6 frontier research extensions, empirical benchmarks, and 101-test suite

Premchandyadav369

b5cb5a0
03:04
docs: add live Hugging Face Space badge to model README

Premchandyadav369

cbdafb5
02:51
feat(10/10): add pyproject.toml packaging, multi-stage CI workflow, Colab badge, and trained tokenizer in app.py

Premchandyadav369

3a04f57
02:48
docs: elevate README with executive hiring summary, training metrics, and production quickstart

Premchandyadav369

842d390

Oct 13 30 days ago

11:47
feat(seo): complete global SEO architecture, documentation site, llms.txt, and automated quality gate

Premchandyadav369

f00c0c6
11:35
feat: complete scientific repair, canonical checkpoint training, tokenizer, GQA zero-copy, and release gating

Premchandyadav369

d279df0
05:31
Complete 16-phase scientific hardening, strict checkpoint audit, empirical benchmarks, and documentation

Premchandyadav369

7aefd9e
04:13
docs(readme): comprehensive end-to-end documentation overhaul, fix 17-column benchmark table and equation delimiters, remove self-rating table, and enhance SEO

Premchandyadav369

7fd4755

Oct 6

10:06
docs(seo): enrich search discovery tags, interactive inference widget, topic index, MODEL_CARD, CITATION, and CI workflows

Premchandyadav369

a3e28f5
08:20
feat(frontier): achieve 10/10 with Triton kernels, GGUF/Ollama exporter, WebGPU runtime, multimodal vision, technical report, and 81 tests

Premchandyadav369

4e689f6
06:39
docs: add Colab badge, project rating (9.4/10), competitor matrix, ELI5 breakdown, compress tutorial, FAQ, and roadmap

Premchandyadav369

7a61b0c
06:32
docs: comprehensive README overhaul with layman explanations, end-to-end architecture, real-world applications, master scores, and turnkey HF instructions

Premchandyadav369

0201a86
06:28
feat(hf): turnkey custom model with TensorTrainLinear, AdaptiveKVCache, PID controller, NVML profiler, OpenAI server, and drop-in compressor

Premchandyadav369

15a4381

Sep 17

06:17
fix(config): Align configuration API with canonical ModelConfig fields and document checkpoint provenance

Premchandyadav369

0b7ee79

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
09:38
Enhance README.md with comprehensive LaTeX equations and theoretical CMDP formulation

Premchandyadav369

c5356b0
09:21
Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture

Premchandyadav369

eaeea8f

May 8

09:48
Update README.md

Premchan369

Verified signature
660ec5a
09:45
update

Premchan369

Verified signature
80229e2
07:48
Upload README.md

Premchan369

Verified signature
35682ce
07:17
Upload benchmark_v4.py

Premchan369

Verified signature
e398782
07:09
Upload tests/test_v4.py

Premchan369

Verified signature
e221672
07:08
Upload requirements.txt

Premchan369

Verified signature
c2a5894

Commit History

Nov 5 7 days ago

feat: SEO and discoverability expansion, viral launch kit, FAQ schema, and trending tags

feat: real-world production pillars, OpenAI streaming API, and reasoning benchmarks

Nov 4 8 days ago

docs: sync SPACE_README.md short_description

feat(seo): maximize discoverability, Google Scholar metadata, interactive HF widget, LLM crawler schemas, zero em dashes, and one-click quickstart downloads

Nov 2 10 days ago

feat(systems): add NVIDIA TRT-LLM plugin, vLLM continuous batching adapter, multi-architecture roofline model, test suite, updated academic papers, and systems research dossier

Oct 22 21 days ago

feat(paper): add real-time cyber-physical deployments, drone uav, ar eyewear, and fleet roi to 6-page paper

feat(space): sync 10-tab app.py with layman guide, fleet roi and sla sandbox

feat(space): sync hardened app.py with swarm forecaster and core spectrum inspector

feat(ui): sync app.py with predictive future forecaster & cyber-physical digital twin

docs(paper): exact IEEE layout standards, 6 pages, table-aligned non-overlapping equations & 7 figures

docs(paper): camera-ready IEEE paper with pre-rendered KaTeX, edge inference title, Dr. Brahmam 1st author, 2024-2026 citations & expanded hardware benchmarks

docs(paper): generate and upload camera-ready IEEE 6-page conference PDF

docs: add 6-page IEEE conference manuscript with author Dr. Madala Guru Brahmam and V C Premchand Yadav, native TikZ diagrams, and zero em-dashes

Oct 21 22 days ago

docs: add formal camera-ready IEEE Transactions manuscript (ieee_transactions_qtensorformer.tex)

docs: add beginner & layman guide with embedded figures and master benchmark tables to README.md

feat(neurips-ieee): camera-ready publication artifacts, Theorems 1-4, 50-trial statistical significance, interactive deep-dive tab

Oct 19 24 days ago

fix(zerogpu): sync spaces first import order with space

fix(space): sync spaces.GPU ZeroGPU integration and 6.30.0 SDK

fix(app): sync demo module-level instance and queue with space

fix(app): sync error logging with space app.py

fix(hf): add GenerationMixin and safe pad_token_id attribute access

feat: complete 6 frontier research extensions, empirical benchmarks, and 101-test suite

docs: add live Hugging Face Space badge to model README

feat(10/10): add pyproject.toml packaging, multi-stage CI workflow, Colab badge, and trained tokenizer in app.py

docs: elevate README with executive hiring summary, training metrics, and production quickstart

Oct 13 30 days ago

feat(seo): complete global SEO architecture, documentation site, llms.txt, and automated quality gate

feat: complete scientific repair, canonical checkpoint training, tokenizer, GQA zero-copy, and release gating

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

docs(readme): comprehensive end-to-end documentation overhaul, fix 17-column benchmark table and equation delimiters, remove self-rating table, and enhance SEO

Oct 6

docs(seo): enrich search discovery tags, interactive inference widget, topic index, MODEL_CARD, CITATION, and CI workflows

feat(frontier): achieve 10/10 with Triton kernels, GGUF/Ollama exporter, WebGPU runtime, multimodal vision, technical report, and 81 tests

docs: add Colab badge, project rating (9.4/10), competitor matrix, ELI5 breakdown, compress tutorial, FAQ, and roadmap

docs: comprehensive README overhaul with layman explanations, end-to-end architecture, real-world applications, master scores, and turnkey HF instructions

feat(hf): turnkey custom model with TensorTrainLinear, AdaptiveKVCache, PID controller, NVML profiler, OpenAI server, and drop-in compressor

Sep 17

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

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

Enhance README.md with comprehensive LaTeX equations and theoretical CMDP formulation

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

May 8

Update README.md

update

Upload README.md

Upload benchmark_v4.py

Upload tests/test_v4.py

Upload requirements.txt