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 / SECURITY.md

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# Security Policy

## Supported Versions

| Version | Supported          |
| ------- | ------------------ |
| 4.0.x   | :white_check_mark: |
| 3.0.x   | :white_check_mark: |
| < 3.0   | :x:                |

## Reporting a Vulnerability

If you discover a security vulnerability or potential exploit in Q-TensorFormer (including model serialization, GGUF binary decoding, or remote code execution risks):

1. **Do not create a public issue**.
2. Contact the maintainer directly via Hugging Face community discussions or email.
3. Include:
   - A description of the vulnerability.
   - Minimal reproduction code or steps.
   - Potential impact and suggested remediations.

We take model safety, serialization integrity, and dependencies seriously and will acknowledge reports within 48 hours.

	# Security Policy

	## Supported Versions

	\| Version \| Supported \|
	\| ------- \| ------------------ \|
	\| 4.0.x \| :white_check_mark: \|
	\| 3.0.x \| :white_check_mark: \|
	\| < 3.0 \| :x: \|

	## Reporting a Vulnerability

	If you discover a security vulnerability or potential exploit in Q-TensorFormer (including model serialization, GGUF binary decoding, or remote code execution risks):

	1. Do not create a public issue.
	2. Contact the maintainer directly via Hugging Face community discussions or email.
	3. Include:
	- A description of the vulnerability.
	- Minimal reproduction code or steps.
	- Potential impact and suggested remediations.

	We take model safety, serialization integrity, and dependencies seriously and will acknowledge reports within 48 hours.