Text Generation
Transformers
Safetensors
PyTorch
ONNX
English
qtensorformer
q-tensorformer
high-throughput-inference
tensor-train
tensor-networks
tensorrt-llm
vllm
continuous-batching
paged-attention
speculative-decoding
zero-svd
adaptive-computation
dynamic-neural-networks
kv-cache
kv-cache-quantization
attention-sinks
lyapunov-stability
control-theory
contextual-bandits
edge-ai
edge-inference
on-device-ai
model-compression
hopper-h100
blackwell-b200
jetson-orin
jetson-thor
grouped-query-attention
rotary-position-embeddings
custom_code
academic-paper
ieee
neurips
vit-ap-university
deepseek
deepseek-r1
reasoning
chain-of-thought
gsm8k
openai-compatible
fastapi
rest-api
sse-streaming
onnx-runtime
embedded-ai
hardware-telemetry
power-efficiency
green-ai
raspberry-pi
smollm2
qwen2.5
llama-3.2
apple-silicon
Instructions to use Premchan369/Q-TensorFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 12,439 Bytes
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"""
scripts/seo_audit.py
Automated Technical SEO, Metadata, and Crawler Quality Gate for Q-TensorFormer.
Validates:
1. All 10 HTML documentation pages in site/ for title, meta description, canonical, OG, Twitter, viewport, charset, and JSON-LD schema validity.
2. XML sitemap structure, URL enumeration, and freshness.
3. robots.txt crawler allowances and sitemap pointer.
4. AI search crawler files (site/llms.txt, site/llms-full.txt, root llms.txt).
5. Internal anchor link integrity across all HTML pages.
6. Hugging Face model card YAML frontmatter and tag integrity.
"""
import sys
import os
import re
import json
import xml.etree.ElementTree as ET
from pathlib import Path
from html.parser import HTMLParser
CANONICAL_BASE = "https://premchan369.github.io/Q-TensorFormer/"
EXPECTED_PAGES = [
"index.html",
"architecture.html",
"methodology.html",
"benchmarks.html",
"edge-inference.html",
"training.html",
"documentation.html",
"research.html",
"limitations.html",
"contributing.html",
]
class HTMLPageAuditor(HTMLParser):
def __init__(self):
super().__init__()
self.title = None
self.in_title = False
self.meta_desc = None
self.canonical = None
self.og_title = None
self.og_desc = None
self.og_url = None
self.viewport = None
self.charset = None
self.jsonld_scripts = []
self.in_jsonld = False
self.current_jsonld = []
self.links = []
def handle_starttag(self, tag, attrs):
attrs_dict = dict(attrs)
if tag == "title":
self.in_title = True
elif tag == "meta":
if attrs_dict.get("charset"):
self.charset = attrs_dict.get("charset")
name = attrs_dict.get("name", "").lower()
prop = attrs_dict.get("property", "").lower()
content = attrs_dict.get("content", "")
if name == "description":
self.meta_desc = content
elif name == "viewport":
self.viewport = content
elif prop == "og:title":
self.og_title = content
elif prop == "og:description":
self.og_desc = content
elif prop == "og:url":
self.og_url = content
elif tag == "link":
rel = attrs_dict.get("rel", "").lower()
if rel == "canonical":
self.canonical = attrs_dict.get("href")
elif tag == "script":
script_type = attrs_dict.get("type", "").lower()
if script_type == "application/ld+json":
self.in_jsonld = True
self.current_jsonld = []
elif tag == "a":
href = attrs_dict.get("href")
if href:
self.links.append(href)
def handle_endtag(self, tag):
if tag == "title":
self.in_title = False
elif tag == "script" and self.in_jsonld:
self.in_jsonld = False
self.jsonld_scripts.append("".join(self.current_jsonld).strip())
def handle_data(self, data):
if self.in_title:
self.title = (self.title or "") + data
elif self.in_jsonld:
self.current_jsonld.append(data)
def run_seo_audit(repo_root: Path) -> bool:
site_dir = repo_root / "site"
print("=" * 80)
print("Q-TensorFormer — Automated Technical SEO & Discoverability Quality Gate")
print(f"Auditing Directory: {site_dir}")
print("=" * 80)
all_passed = True
seen_titles = {}
seen_descriptions = {}
seen_canonicals = set()
# 1. Audit HTML Pages
print("\n[Phase 1] Auditing HTML Metadata, OpenGraph & JSON-LD Schemas...")
for page_name in EXPECTED_PAGES:
page_path = site_dir / page_name
if not page_path.exists():
print(f" [FAIL] Missing expected documentation page: {page_name}")
all_passed = False
continue
with open(page_path, "r", encoding="utf-8") as f:
content = f.read()
auditor = HTMLPageAuditor()
auditor.feed(content)
# Title checks
if not auditor.title:
print(f" [FAIL] {page_name}: Missing <title> tag")
all_passed = False
else:
title = auditor.title.strip()
if title in seen_titles:
print(f" [FAIL] {page_name}: Duplicate title '{title}' (first seen in {seen_titles[title]})")
all_passed = False
else:
seen_titles[title] = page_name
# Meta description checks
if not auditor.meta_desc:
print(f" [FAIL] {page_name}: Missing <meta name='description'>")
all_passed = False
else:
desc = auditor.meta_desc.strip()
if desc in seen_descriptions:
print(f" [FAIL] {page_name}: Duplicate meta description (first seen in {seen_descriptions[desc]})")
all_passed = False
else:
seen_descriptions[desc] = page_name
# Canonical checks
if not auditor.canonical:
print(f" [FAIL] {page_name}: Missing <link rel='canonical'>")
all_passed = False
else:
if auditor.canonical in seen_canonicals:
print(f" [FAIL] {page_name}: Duplicate canonical URL '{auditor.canonical}'")
all_passed = False
seen_canonicals.add(auditor.canonical)
expected_canonical = CANONICAL_BASE if page_name == "index.html" else f"{CANONICAL_BASE}{page_name}"
if auditor.canonical != expected_canonical:
print(f" [FAIL] {page_name}: Canonical mismatch. Expected {expected_canonical}, got {auditor.canonical}")
all_passed = False
# Viewport and Charset
if not auditor.viewport:
print(f" [FAIL] {page_name}: Missing responsive viewport meta tag")
all_passed = False
if not auditor.charset or auditor.charset.lower() != "utf-8":
print(f" [FAIL] {page_name}: Charset is missing or not UTF-8")
all_passed = False
# Open Graph
if not (auditor.og_title and auditor.og_desc and auditor.og_url):
print(f" [FAIL] {page_name}: Incomplete Open Graph metadata (title/desc/url)")
all_passed = False
# JSON-LD Schema
if not auditor.jsonld_scripts:
print(f" [FAIL] {page_name}: Missing JSON-LD structured data")
all_passed = False
else:
for s in auditor.jsonld_scripts:
try:
data = json.loads(s)
if "@context" not in data or "schema.org" not in data["@context"]:
print(f" [FAIL] {page_name}: JSON-LD missing valid schema.org @context")
all_passed = False
if "@type" in data:
pass
elif "@graph" in data and isinstance(data["@graph"], list):
for node in data["@graph"]:
if "@type" not in node:
print(f" [FAIL] {page_name}: JSON-LD node in @graph missing @type")
all_passed = False
else:
print(f" [FAIL] {page_name}: JSON-LD missing @type or valid @graph")
all_passed = False
except json.JSONDecodeError as e:
print(f" [FAIL] {page_name}: Invalid JSON-LD block: {e}")
all_passed = False
# Internal Links Verification
for link in auditor.links:
# strip anchor
target = link.split("#")[0]
if target and not target.startswith("http") and not target.startswith("mailto"):
target_path = (site_dir / target).resolve()
if not target_path.exists():
print(f" [FAIL] {page_name}: Broken internal link to '{link}'")
all_passed = False
print(f" [PASS] {page_name:<20} | Title: {len(auditor.title.strip())}ch | Desc: {len(auditor.meta_desc.strip())}ch | Canonical: OK | Schema: Valid")
# 2. Audit XML Sitemap
print("\n[Phase 2] Auditing XML Sitemap (site/sitemap.xml)...")
sitemap_path = site_dir / "sitemap.xml"
if not sitemap_path.exists():
print(" [FAIL] site/sitemap.xml does not exist")
all_passed = False
else:
try:
tree = ET.parse(sitemap_path)
root = tree.getroot()
ns = {"sm": "http://www.sitemaps.org/schemas/sitemap/0.9"}
urls = [elem.text.strip() for elem in root.findall(".//sm:loc", ns)]
print(f" Found {len(urls)} URLs in sitemap:")
for expected_page in EXPECTED_PAGES:
expected_url = CANONICAL_BASE if expected_page == "index.html" else f"{CANONICAL_BASE}{expected_page}"
if expected_url not in urls:
print(f" [FAIL] Missing sitemap entry for {expected_url}")
all_passed = False
else:
print(f" [OK] {expected_url}")
except ET.ParseError as e:
print(f" [FAIL] XML sitemap parsing failed: {e}")
all_passed = False
# 3. Audit robots.txt
print("\n[Phase 3] Auditing robots.txt...")
for r_path, label in [(site_dir / "robots.txt", "site/robots.txt"), (repo_root / "robots.txt", "root robots.txt")]:
if not r_path.exists():
print(f" [FAIL] {label} does not exist")
all_passed = False
else:
content = r_path.read_text(encoding="utf-8")
if "Sitemap:" not in content:
print(f" [FAIL] {label} missing Sitemap directive")
all_passed = False
else:
print(f" [PASS] {label}: Sitemap directive verified")
# 4. Audit AI Search Crawler Files (llms.txt)
print("\n[Phase 4] Auditing LLM Search Crawler Files (llms.txt & llms-full.txt)...")
for f_path, label in [
(site_dir / "llms.txt", "site/llms.txt"),
(site_dir / "llms-full.txt", "site/llms-full.txt"),
(repo_root / "llms.txt", "root llms.txt"),
]:
if not f_path.exists():
print(f" [FAIL] {label} does not exist")
all_passed = False
else:
sz = f_path.stat().st_size
if sz < 200:
print(f" [FAIL] {label} size is too small ({sz} bytes)")
all_passed = False
else:
print(f" [PASS] {label}: Exists and populated ({sz:,} bytes)")
# 5. Audit Hugging Face Model Card YAML frontmatter
print("\n[Phase 5] Auditing Hugging Face Model Card & README Metadata...")
for md_file in [repo_root / "README.md", repo_root / "MODEL_CARD.md"]:
if not md_file.exists():
print(f" [FAIL] {md_file.name} does not exist")
all_passed = False
continue
text = md_file.read_text(encoding="utf-8")
if not text.startswith("---"):
print(f" [FAIL] {md_file.name} missing YAML frontmatter opening '---'")
all_passed = False
else:
match = re.search(r"^---\n(.*?)\n---", text, re.DOTALL)
if not match:
print(f" [FAIL] {md_file.name} missing YAML frontmatter closing '---'")
all_passed = False
else:
frontmatter = match.group(1)
for req_key in ["tags:", "pipeline_tag:", "license:"]:
if req_key not in frontmatter:
print(f" [FAIL] {md_file.name} YAML frontmatter missing '{req_key}'")
all_passed = False
print(f" [PASS] {md_file.name}: Valid YAML frontmatter with required keys")
print("\n" + "=" * 80)
if all_passed:
print("SEO QUALITY GATE RESULT: [PASSED] ALL 10 PAGES & METADATA VERIFIED")
print("=" * 80)
return True
else:
print("SEO QUALITY GATE RESULT: [FAILED] REPAIR REPORTED DEFECTS")
print("=" * 80)
return False
if __name__ == "__main__":
root = Path(__file__).resolve().parent.parent
success = run_seo_audit(root)
sys.exit(0 if success else 1)
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