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

File size: 2,358 Bytes

const fs = require('fs');
const path = require('path');

const scriptDir = __dirname;
const katexJsPath = path.join(scriptDir, 'assets', 'katex.min.js');
const katexCssPath = path.join(scriptDir, 'assets', 'katex.min.css');

const code = fs.readFileSync(katexJsPath, 'utf8');
const katexCss = fs.readFileSync(katexCssPath, 'utf8');

const m = { exports: {} };
const fn = new Function('module', 'exports', code);
fn(m, m.exports);
const katex = m.exports;

let eqCounter = 0;

function renderMath(text) {
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    // First replace block math $$ ... $$
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        const rawEq = eq.trim();
        try {
            const mathHtml = katex.renderToString(rawEq, {
                displayMode: true,
                output: 'htmlAndMathml',
                throwOnError: false,
                strict: false
            });
            return `<table class="eq-table"><tr><td class="eq-cell-math">${mathHtml}</td><td class="eq-cell-num">(${eqCounter})</td></tr></table>`;
        } catch (e) {
            console.error('KaTeX block error on:', rawEq, e);
            return match;
        }
    });

    // Next replace inline math $ ... $ (excluding escaped \$)
    output = output.replace(/(?<!\\)\$([^\$\n]+?)(?<!\\)\$/g, (match, eq) => {
        const rawEq = eq.trim();
        try {
            return katex.renderToString(rawEq, {
                displayMode: false,
                output: 'htmlAndMathml',
                throwOnError: false,
                strict: false
            });
        } catch (e) {
            console.error('KaTeX inline error on:', rawEq, e);
            return match;
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    // Also replace KaTeX CSS placeholder if present
    if (output.includes('/* __KATEX_CSS__ */')) {
        output = output.replace('/* __KATEX_CSS__ */', katexCss);
    }

    return output;
}

module.exports = { renderMath, katexCss };

if (require.main === module) {
    const inputFile = process.argv[2];
    const outputFile = process.argv[3] || inputFile;
    if (inputFile) {
        const raw = fs.readFileSync(inputFile, 'utf8');
        const rendered = renderMath(raw);
        fs.writeFileSync(outputFile, rendered, 'utf8');
        console.log(`Pre-rendered KaTeX math from ${inputFile} -> ${outputFile}`);
    }
}