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: 1,207 Bytes

"""
Hugging Face Transformers Integration for Q-TensorFormer.

Provides first-class Hugging Face ecosystem compatibility:
  - QTensorFormerConfig (inherits PretrainedConfig)
  - QTensorFormerForCausalLM (inherits PreTrainedModel, GenerationMixin)
  - AutoConfig & AutoModelForCausalLM registration
  - Generation support with past_key_values and Adaptive KV Cache
  - save_pretrained and from_pretrained serialization
"""

import sys
import os

# Re-export authoritative root implementations
from configuration_qtensorformer import QTensorFormerConfig, factorize_dim
from modeling_qtensorformer import (
    QTensorFormerForCausalLM,
    QTensorFormerModel,
    QTensorFormerPreTrainedModel,
    QTensorFormerBlock,
    QTensorFormerAttention,
    QTensorFormerMLP,
    TensorTrainLinear,
    AdaptiveKVCache,
    PIDResourceController,
    QTensorFormerRMSNorm,
)

__all__ = [
    "QTensorFormerConfig",
    "QTensorFormerForCausalLM",
    "QTensorFormerModel",
    "QTensorFormerPreTrainedModel",
    "QTensorFormerBlock",
    "QTensorFormerAttention",
    "QTensorFormerMLP",
    "TensorTrainLinear",
    "AdaptiveKVCache",
    "PIDResourceController",
    "QTensorFormerRMSNorm",
    "factorize_dim",
]