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: 6,347 Bytes
7aefd9e b5cb5a0 6d1f076 7aefd9e b5cb5a0 6d1f076 7aefd9e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | #!/usr/bin/env python3
"""
scripts/scientific_integrity_checker.py
Automated Scientific Integrity & Evidence Classification Auditor (Phase 12).
Verifies:
1. All required reports in reports/ and raw benchmark files in results/raw/ exist and are non-empty.
2. Every benchmark block in results/raw/*.json includes a valid scientific_classification label.
3. Production checkpoint loaders (qtensorformer_to_gguf.py, configuration_qtensorformer.py)
do not use strict=False or ignore missing embedding weights.
4. Documentation (README.md, MODEL_CARD.md, reports/*.md) contains no forbidden promotional
claims and has zero broken Markdown tables or unbalanced LaTeX $$ blocks.
"""
import json
import sys
from pathlib import Path
from typing import List
ROOT_DIR = Path(__file__).resolve().parent.parent
REQUIRED_REPORTS = [
"BASELINE_AUDIT.md",
"REPOSITORY_INVENTORY.md",
"EXISTING_FAILURES.md",
"ARCHITECTURE_FAMILY_MAP.md",
"CLAIM_TO_CODE_MAP.md",
"SOURCE_API_AUDIT.md",
"CONFIG_PROVENANCE.md",
"CHECKPOINT_INTEGRITY.md",
"CANONICAL_MODEL_DECISION.md",
"COLD_START_EVALUATION.md",
"EDGE_INFERENCE_EVALUATION.md",
"ENERGY_MEASUREMENT_METHODOLOGY.md",
"BENCHMARK_PROTOCOL.md",
"BASELINE_FAIRNESS.md",
"BENCHMARK_RESULTS.md",
"TEST_COVERAGE.md",
"SCIENTIFIC_INTEGRITY_AUDIT.md",
"RELATED_WORK_AND_NOVELTY.md",
"FINAL_RESEARCH_AUDIT.md",
"RELEASE_READINESS.md",
"KNOWN_LIMITATIONS.md",
"REPRODUCIBILITY_CHECKLIST.md",
"FRONTIER_RESEARCH_BENCHMARKS.md",
"NEURIPS_CAMERA_READY_PAPER.md",
]
REQUIRED_RAW_JSON = [
"checkpoint_integrity.json",
"cold_start_and_edge.json",
"tt_compression_audit.json",
"controller_audit.json",
"kv_cache_audit.json",
"quantum_pathway_audit.json",
"frontier_research_benchmarks.json",
"neurips_statistical_evaluation.json",
]
ALLOWED_LABELS = {
"MEASURED",
"ESTIMATED",
"SIMULATED",
"THEORETICAL",
"PROJECTED",
"N/A",
"BLOCKED",
"UNVERIFIED",
}
FORBIDDEN_CLAIMS = [
"guaranteed energy savings",
"quantum speedup on cpu",
"quantum advantage in nlp",
"zero runtime overhead",
"first ever adaptive transformer",
"asymptotic pareto optimality guaranteed",
]
def check_markdown_tables_and_math(file_path: Path, errors: List[str]):
text = file_path.read_text(encoding="utf-8")
lines = text.splitlines()
# Check $$ display math balance outside fenced code blocks
in_code = False
dd_count = 0
in_table = False
expected_cols = 0
for idx, line in enumerate(lines, start=1):
stripped = line.strip()
if stripped.startswith("```"):
in_code = not in_code
in_table = False
continue
if in_code:
continue
dd_count += line.count("$$")
if stripped.startswith("|") and stripped.endswith("|"):
cols = len([c for c in stripped.split("|")[1:-1]])
if not in_table:
in_table = True
expected_cols = cols
else:
if cols != expected_cols:
errors.append(
f"[{file_path.name}:{idx}] Table column mismatch: expected {expected_cols} columns, got {cols}"
)
else:
in_table = False
if dd_count % 2 != 0:
errors.append(
f"[{file_path.name}] Unbalanced $$ display math delimiters (count={dd_count})"
)
# Check forbidden promotional phrases (ignoring lines that explicitly list them as forbidden)
for idx, line in enumerate(lines, start=1):
low = line.lower()
if "forbidden" in low or "prohibit" in low or "rejects" in low:
continue
for phrase in FORBIDDEN_CLAIMS:
if phrase in low:
errors.append(
f"[{file_path.name}:{idx}] Forbidden promotional phrase detected: '{phrase}'"
)
def main():
errors: List[str] = []
# 1. Check required reports
reports_dir = ROOT_DIR / "reports"
for rname in REQUIRED_REPORTS:
rpath = reports_dir / rname
if not rpath.exists() or rpath.stat().st_size < 100:
errors.append(f"Missing or empty report: reports/{rname}")
else:
check_markdown_tables_and_math(rpath, errors)
# 2. Check required raw JSON files and scientific classifications
raw_dir = ROOT_DIR / "results" / "raw"
for jname in REQUIRED_RAW_JSON:
jpath = raw_dir / jname
if not jpath.exists():
errors.append(f"Missing raw benchmark file: results/raw/{jname}")
continue
data = json.loads(jpath.read_text(encoding="utf-8"))
raw_str = json.dumps(data)
if "scientific_classification" not in raw_str:
errors.append(f"results/raw/{jname} lacks 'scientific_classification' labels")
# 3. Check production source files for strict=False bypasses
gguf_src = (ROOT_DIR / "qtensorformer_to_gguf.py").read_text(encoding="utf-8")
if "strict=False" in gguf_src:
errors.append("qtensorformer_to_gguf.py contains forbidden 'strict=False'")
cfg_src = (ROOT_DIR / "configuration_qtensorformer.py").read_text(encoding="utf-8")
if 'keys_to_ignore_at_loading = ["model.embed_tokens.weight"]' in cfg_src:
errors.append(
"configuration_qtensorformer.py still ignores model.embed_tokens.weight at loading"
)
# 4. Check README.md and MODEL_CARD.md
for doc_name in ["README.md", "MODEL_CARD.md"]:
doc_path = ROOT_DIR / doc_name
if not doc_path.exists():
errors.append(f"Missing {doc_name}")
else:
check_markdown_tables_and_math(doc_path, errors)
print("=" * 70)
print("Q-TensorFormer — Automated Scientific Integrity Audit")
print("=" * 70)
print(f"Reports Verified: {len(REQUIRED_REPORTS)}")
print(f"Raw JSON Verified: {len(REQUIRED_RAW_JSON)}")
print(f"Integrity Errors: {len(errors)}")
if errors:
for e in errors:
print(f" [FAIL] {e}")
sys.exit(1)
else:
print("[PASS] Zero scientific integrity violations, zero broken tables/equations.")
print("=" * 70)
if __name__ == "__main__":
main()
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