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 / scripts /run_onnx_edge.py

Premchandyadav369

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

000c57b 7 days ago

Raw History Blame Contribute Delete

3.03 kB

#!/usr/bin/env python3
"""
scripts/run_onnx_edge.py
Standalone Edge Embedded Deployment Runner for Q-TensorFormer.
Executes token generation via ONNX Runtime with zero PyTorch runtime dependency:
- Runs dynamic batch and sequence axes on edge CPUs/SoCs (ARM Cortex, Jetson, Raspberry Pi).
- Profiles Inter-Token Latency (ITL) and memory footprints.
"""

import sys
import time
from pathlib import Path
import numpy as np

ROOT_DIR = Path(__file__).resolve().parent.parent
if str(ROOT_DIR) not in sys.path:
    sys.path.insert(0, str(ROOT_DIR))

try:
    import onnxruntime as ort
    HAS_ORT = True
except ImportError:
    HAS_ORT = False

from src.tokenizer import QTensorFormerTokenizer


def run_onnx_inference(onnx_model_path: Path, max_new_tokens: int = 16):
    print("=" * 70)
    print("Q-TensorFormer: Standalone Edge ONNX Runtime Deployment")
    print(f"Target Model: {onnx_model_path}")
    print("=" * 70)

    if not HAS_ORT:
        print("[Error] onnxruntime is not installed. Run: pip install onnxruntime")
        return

    tokenizer = QTensorFormerTokenizer(vocab_size=256)
    prompt = "Edge computing requires adaptive computation"
    token_ids = tokenizer.encode(prompt, add_bos=True)
    print(f"Input Prompt: '{prompt}' ({len(token_ids)} tokens)")

    # Initialize ONNX Runtime Session with optimization
    opts = ort.SessionOptions()
    opts.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
    opts.intra_op_num_threads = 4

    session = ort.InferenceSession(str(onnx_model_path), sess_options=opts, providers=["CPUExecutionProvider"])
    input_name = session.get_inputs()[0].name
    output_name = session.get_outputs()[0].name

    print("\n[+] Starting Autoregressive Generation on Edge ONNX Engine...")
    curr_ids = list(token_ids)
    latencies = []

    for step in range(max_new_tokens):
        t0 = time.perf_counter()
        inp = np.array([curr_ids], dtype=np.int64)
        outputs = session.run([output_name], {input_name: inp})
        logits = outputs[0]  # [1, seq_len, vocab_size]
        next_token = int(np.argmax(logits[0, -1, :]))
        curr_ids.append(next_token)
        lat = (time.perf_counter() - t0) * 1000.0
        latencies.append(lat)

    gen_ids = curr_ids[len(token_ids):]
    generated_text = tokenizer.decode(gen_ids)

    print(f"\n[Generated Output]: {generated_text}")
    print(f"Mean Per-Token Latency: {np.mean(latencies):.2f} ms")
    print(f"P99 Per-Token Latency:  {np.percentile(latencies, 99):.2f} ms")
    print(f"Edge Token Throughput:  {1000.0 / np.mean(latencies):.2f} tokens/second")
    print("=" * 70)


def main():
    onnx_file = ROOT_DIR / "qtensorformer.onnx"
    if not onnx_file.exists():
        print(f"[*] ONNX model not found at {onnx_file}. Triggering automated export...")
        from export_onnx import export_to_onnx
        export_to_onnx(model_path=str(ROOT_DIR), output_path=str(onnx_file), verify=False)

    run_onnx_inference(onnx_file, max_new_tokens=12)


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""
	scripts/run_onnx_edge.py

	Standalone Edge Embedded Deployment Runner for Q-TensorFormer.
	Executes token generation via ONNX Runtime with zero PyTorch runtime dependency:
	- Runs dynamic batch and sequence axes on edge CPUs/SoCs (ARM Cortex, Jetson, Raspberry Pi).
	- Profiles Inter-Token Latency (ITL) and memory footprints.
	"""

	import sys
	import time
	from pathlib import Path
	import numpy as np

	ROOT_DIR = Path(__file__).resolve().parent.parent
	if str(ROOT_DIR) not in sys.path:
	sys.path.insert(0, str(ROOT_DIR))

	try:
	import onnxruntime as ort
	HAS_ORT = True
	except ImportError:
	HAS_ORT = False

	from src.tokenizer import QTensorFormerTokenizer


	def run_onnx_inference(onnx_model_path: Path, max_new_tokens: int = 16):
	print("=" * 70)
	print("Q-TensorFormer: Standalone Edge ONNX Runtime Deployment")
	print(f"Target Model: {onnx_model_path}")
	print("=" * 70)

	if not HAS_ORT:
	print("[Error] onnxruntime is not installed. Run: pip install onnxruntime")
	return

	tokenizer = QTensorFormerTokenizer(vocab_size=256)
	prompt = "Edge computing requires adaptive computation"
	token_ids = tokenizer.encode(prompt, add_bos=True)
	print(f"Input Prompt: '{prompt}' ({len(token_ids)} tokens)")

	# Initialize ONNX Runtime Session with optimization
	opts = ort.SessionOptions()
	opts.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
	opts.intra_op_num_threads = 4

	session = ort.InferenceSession(str(onnx_model_path), sess_options=opts, providers=["CPUExecutionProvider"])
	input_name = session.get_inputs()[0].name
	output_name = session.get_outputs()[0].name

	print("\n[+] Starting Autoregressive Generation on Edge ONNX Engine...")
	curr_ids = list(token_ids)
	latencies = []

	for step in range(max_new_tokens):
	t0 = time.perf_counter()
	inp = np.array([curr_ids], dtype=np.int64)
	outputs = session.run([output_name], {input_name: inp})
	logits = outputs[0] # [1, seq_len, vocab_size]
	next_token = int(np.argmax(logits[0, -1, :]))
	curr_ids.append(next_token)
	lat = (time.perf_counter() - t0) * 1000.0
	latencies.append(lat)

	gen_ids = curr_ids[len(token_ids):]
	generated_text = tokenizer.decode(gen_ids)

	print(f"\n[Generated Output]: {generated_text}")
	print(f"Mean Per-Token Latency: {np.mean(latencies):.2f} ms")
	print(f"P99 Per-Token Latency: {np.percentile(latencies, 99):.2f} ms")
	print(f"Edge Token Throughput: {1000.0 / np.mean(latencies):.2f} tokens/second")
	print("=" * 70)


	def main():
	onnx_file = ROOT_DIR / "qtensorformer.onnx"
	if not onnx_file.exists():
	print(f"[*] ONNX model not found at {onnx_file}. Triggering automated export...")
	from export_onnx import export_to_onnx
	export_to_onnx(model_path=str(ROOT_DIR), output_path=str(onnx_file), verify=False)

	run_onnx_inference(onnx_file, max_new_tokens=12)


	if __name__ == "__main__":
	main()