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 / CHECKPOINT_COMPATIBILITY.md

Premchandyadav369

fix(config): Align configuration API with canonical ModelConfig fields and document checkpoint provenance

0b7ee79 about 2 months ago

preview code

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1.25 kB

CHECKPOINT COMPATIBILITY: Audit of `qtensorformer_small.pt`

Date: September 17, 2026
Status: CHECKPOINT_INCOMPATIBLE

1. Executive Summary

Evaluation of the binary checkpoint qtensorformer_small.pt under mandatory strict loading (load_state_dict(..., strict=True)) confirmed that the checkpoint cannot be loaded into current Q-TensorFormer models.

2. Incompatibility Matrix

Target Architecture	Strict Load Status	Missing Keys Count	Unexpected Keys Count	Primary Root Cause
`src.models.QTensorFormer`	FAILED	185	113	Architecture mismatch: Checkpoint uses TensorLy factorized FFN and LM head
`q_tensor_former.QTensorFormer`	FAILED	101	113	Namespace & architecture mismatch: Checkpoint uses `blocks.*` and factorized weights
`q_tensor_former_v2.QTensorFormer`	FAILED	107	113	Namespace & architecture mismatch: Checkpoint uses external TensorLy layers

3. Scientific Policy

No keys were renamed arbitrarily to force a synthetic pass.
strict=False was not used to hide weight mismatches.
Track A benchmarks must acknowledge CHECKPOINT_INCOMPATIBLE honestly rather than hallucinating compatibility.

CHECKPOINT COMPATIBILITY: Audit of qtensorformer_small.pt

1. Executive Summary

2. Incompatibility Matrix

3. Scientific Policy

CHECKPOINT COMPATIBILITY: Audit of `qtensorformer_small.pt`