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 / docs

Commit History

Nov 5 9 days ago

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

Premchandyadav369

7d566f9

Oct 13

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

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

Premchandyadav369

4e689f6

Sep 11

12:23
feat(research): Turn Q-TensorFormer into a verified research system with LFS figure assets

Premchandyadav369

0e4850b

Sep 6

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

Premchandyadav369

eaeea8f

Commit History

Nov 5 9 days ago

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

Oct 13

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

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

Sep 11

feat(research): Turn Q-TensorFormer into a verified research system with LFS figure assets

Sep 6

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