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 / checkpoints /dense_baseline_trained.metadata.json

Premchandyadav369

feat: complete scientific repair, canonical checkpoint training, tokenizer, GQA zero-copy, and release gating

d279df0 about 1 month ago

Raw History Blame Contribute Delete

1.9 kB

{
  "format": "Q-TensorFormer-Canonical-Checkpoint-v1",
  "architecture": "DenseBaseline",
  "created_utc": "2026-10-13T11:27:42.977030+00:00",
  "is_trained": true,
  "weight_status": "TRAINED_CANONICAL_WEIGHTS",
  "model_config": {
    "d_model": 64,
    "n_heads": 4,
    "n_layers": 2,
    "ff_multiplier": 4,
    "max_seq_len": 64,
    "vocab_size": 256,
    "dropout": 0.05,
    "tt_rank": 4,
    "tt_min_rank": 2,
    "use_tensor_ffn": true,
    "n_qubits": 4,
    "n_quantum_layers": 2,
    "quantum_sparsity": 0.3,
    "use_quantum": true,
    "rank_alpha": 2.0,
    "rank_smoothing": 0.9
  },
  "tokenizer_metadata": {
    "tokenizer_class": "QTensorFormerTokenizer",
    "version": "1.0.0",
    "vocab_size": 256,
    "vocab_sha256": "fc7ee274009d3aad8132b5f4de4c09091055439e22aa6b44196cab3ceb1db01e",
    "special_tokens": {
      "pad_token_id": 0,
      "bos_token_id": 1,
      "eos_token_id": 2,
      "unk_token_id": 3
    }
  },
  "training_provenance": {
    "dataset_name": "data/qtensorformer_corpus.txt",
    "dataset_tokens_total": 2905,
    "train_tokens": 2033,
    "val_tokens": 435,
    "test_tokens": 437,
    "epochs": 12,
    "learning_rate": 0.001,
    "initial_test_loss": 5.728164,
    "initial_test_perplexity": 307.4044,
    "final_test_loss": 3.824246,
    "final_test_perplexity": 45.7983,
    "final_test_accuracy": 0.0879,
    "seed": 42
  },
  "state_dict_sha256": "5cf84189f8a3c54c4d331520b6316863f42e9025e4694f591386047eb4070c95",
  "parameter_count": 116480,
  "state_dict_key_count": 29,
  "software": {
    "python": "3.11.9",
    "pytorch": "2.10.0+cpu",
    "platform": "Windows-10-10.0.26200-SP0"
  },
  "checkpoint_path": "C:\\Users\\PREMCHANDYADAV\\.gemini\\antigravity\\scratch\\Q-TensorFormer\\checkpoints\\dense_baseline_trained.pt",
  "file_size_bytes": 493645,
  "file_sha256": "45692966ec4600e2e863a43790401b9bfd04f838347959e28158fcbd53e00098"
}

	{
	"format": "Q-TensorFormer-Canonical-Checkpoint-v1",
	"architecture": "DenseBaseline",
	"created_utc": "2026-10-13T11:27:42.977030+00:00",
	"is_trained": true,
	"weight_status": "TRAINED_CANONICAL_WEIGHTS",
	"model_config": {
	"d_model": 64,
	"n_heads": 4,
	"n_layers": 2,
	"ff_multiplier": 4,
	"max_seq_len": 64,
	"vocab_size": 256,
	"dropout": 0.05,
	"tt_rank": 4,
	"tt_min_rank": 2,
	"use_tensor_ffn": true,
	"n_qubits": 4,
	"n_quantum_layers": 2,
	"quantum_sparsity": 0.3,
	"use_quantum": true,
	"rank_alpha": 2.0,
	"rank_smoothing": 0.9
	},
	"tokenizer_metadata": {
	"tokenizer_class": "QTensorFormerTokenizer",
	"version": "1.0.0",
	"vocab_size": 256,
	"vocab_sha256": "fc7ee274009d3aad8132b5f4de4c09091055439e22aa6b44196cab3ceb1db01e",
	"special_tokens": {
	"pad_token_id": 0,
	"bos_token_id": 1,
	"eos_token_id": 2,
	"unk_token_id": 3
	}
	},
	"training_provenance": {
	"dataset_name": "data/qtensorformer_corpus.txt",
	"dataset_tokens_total": 2905,
	"train_tokens": 2033,
	"val_tokens": 435,
	"test_tokens": 437,
	"epochs": 12,
	"learning_rate": 0.001,
	"initial_test_loss": 5.728164,
	"initial_test_perplexity": 307.4044,
	"final_test_loss": 3.824246,
	"final_test_perplexity": 45.7983,
	"final_test_accuracy": 0.0879,
	"seed": 42
	},
	"state_dict_sha256": "5cf84189f8a3c54c4d331520b6316863f42e9025e4694f591386047eb4070c95",
	"parameter_count": 116480,
	"state_dict_key_count": 29,
	"software": {
	"python": "3.11.9",
	"pytorch": "2.10.0+cpu",
	"platform": "Windows-10-10.0.26200-SP0"
	},
	"checkpoint_path": "C:\\Users\\PREMCHANDYADAV\\.gemini\\antigravity\\scratch\\Q-TensorFormer\\checkpoints\\dense_baseline_trained.pt",
	"file_size_bytes": 493645,
	"file_sha256": "45692966ec4600e2e863a43790401b9bfd04f838347959e28158fcbd53e00098"
	}