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 / results

Commit History

Oct 21 9 days ago

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

Premchandyadav369

6d1f076

Oct 19 11 days ago

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

Premchandyadav369

b5cb5a0

Oct 13 17 days ago

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

Oct 6 24 days ago

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

Premchandyadav369

15a4381

Commit History

Oct 21 9 days ago

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

Oct 19 11 days ago

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

Oct 13 17 days ago

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

Oct 6 24 days ago

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