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 / reports /CHECKPOINT_INTEGRITY.md

Premchandyadav369

Complete 16-phase scientific hardening, strict checkpoint audit, empirical benchmarks, and documentation

7aefd9e about 1 month ago

preview code

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# Phase Two — Checkpoint Integrity & Strict-Load Audit Report

## 1. Forensic Inspection of `qtensorformer_small.pt`
Data source: `results/raw/checkpoint_integrity.json` (`MEASURED`).

- **File Path:** `qtensorformer_small.pt`
- **File Size:** `5,432,305` bytes (`5.18 MB`)
- **SHA-256 Digest:** `9a4e61f70ba084e6c4e38b3e7cc8479d1f2ff43c51fff7bcf2ee07191dc3b0db`
- **Git Provenance:** Committed in `82fd973c` (`May 6, 2026`) from an external container (`/app/qtensorformer`).
- **Top-Level Dictionary Keys:** `['model_state_dict', 'config', 'params', 'baseline_params', 'compression_ratio', 'version']`
- **Recorded Metadata:**
  - `version`: `"1.0.0"`
  - `params`: `1,347,747`
  - `baseline_params`: `16,146,432`
  - `compression_ratio`: `11.9803`
- **Actual State-Dict Structure:**
  - Contains `113` tensors summing to exactly `1,347,747` elements.
  - Sample keys:
    - `embeddings.token_embedding.weight` (`[10000, 128]`)
    - `embeddings.position_embedding.weight` (`[64, 128]`)
    - `embeddings.quantum_enhancer.qlayer.weights` (`[2, 4]`)
    - `blocks.0.attention.q_proj.weight` (`[128, 128]`)
    - `blocks.0.ffn.layer.weight.factors.factor_0` .. `factor_5` (TensorLy `FactorizedTensor` format)
    - `blocks.0.ffn.rank_scheduler.net.0.weight`

---

## 2. Canonical `strict=True` Loading Test Results

Per Phase Two rules, `model.load_state_dict(sd, strict=True)` was executed against the repository's canonical implementations without altering checkpoint tensors or model dimensions:

| Target Model Class | Config Class | Model Params | Model Keys | Checkpoint Keys | Exact Key Overlap | Missing Keys | Unexpected Keys | `strict=True` Result |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| `src.models.QTensorFormer` | `src.config.ModelConfig` | `1,524,186` | `185` | `113` | **`0` (0.0%)** | `185` | `113` | **`FAIL`** (`RuntimeError`) |
| `modeling_qtensorformer.QTensorFormerForCausalLM` | `QTensorFormerConfig` | `2,767,296` | `52` | `113` | **`0` (0.0%)** | `52` | `113` | **`FAIL`** (`RuntimeError`) |

### Why `qtensorformer_small.pt` Cannot Load Under `strict=True`
1. **Incompatible Tensor Factorization Library:** `qtensorformer_small.pt` stores FFN weights using `tensorly` / `tltorch` 6-core `factors.factor_0..5` naming on a single fused FFN layer (`blocks.i.ffn.layer`), whereas `src.tensor_layers.TTFeedForward` uses two 4-core pure-PyTorch `TTLinear` projections (`blocks.i.tt_ffn.up_proj.cores.0..3` and `blocks.i.tt_ffn.down_proj.cores.0..3`).
2. **Different Module Hierarchy:** `qtensorformer_small.pt` uses `embeddings.token_embedding` and `blocks.i.ffn.rank_scheduler.net`, whereas `src.models.QTensorFormer` uses `embedding.weight`, `blocks.i.info_engine`, and `blocks.i.allocator`.

---

## 3. Canonical Round-Trip `strict=True` Verification

While the legacy external file `qtensorformer_small.pt` is incompatible, both canonical repository architectures pass `strict=True` save/load round-trip verification (`results/raw/checkpoint_integrity.json` and `scripts/smoke_test.py`):

| Canonical Implementation | Serialization Method | Reload Method | `strict=True` Result | Output Numerical Match |
| :--- | :--- | :--- | :--- | :--- |
| `src.models.QTensorFormer` | `torch.save(model.state_dict(), path)` | `model.load_state_dict(sd, strict=True)` | **`PASS` (185/185 keys)** | Max diff `0.0` |
| `modeling_qtensorformer.QTensorFormerForCausalLM` | `model.save_pretrained(dir)` | `QTensorFormerForCausalLM.from_pretrained(dir)` | **`PASS` (52/52 keys)** | Max diff `< 1e-6` |

	# Phase Two — Checkpoint Integrity & Strict-Load Audit Report

	## 1. Forensic Inspection of `qtensorformer_small.pt`

	Data source: `results/raw/checkpoint_integrity.json` (`MEASURED`).

	- File Path: `qtensorformer_small.pt`
	- File Size: `5,432,305` bytes (`5.18 MB`)
	- SHA-256 Digest: `9a4e61f70ba084e6c4e38b3e7cc8479d1f2ff43c51fff7bcf2ee07191dc3b0db`
	- Git Provenance: Committed in `82fd973c` (`May 6, 2026`) from an external container (`/app/qtensorformer`).
	- Top-Level Dictionary Keys: `['model_state_dict', 'config', 'params', 'baseline_params', 'compression_ratio', 'version']`
	- Recorded Metadata:
	- `version`: `"1.0.0"`
	- `params`: `1,347,747`
	- `baseline_params`: `16,146,432`
	- `compression_ratio`: `11.9803`
	- Actual State-Dict Structure:
	- Contains `113` tensors summing to exactly `1,347,747` elements.
	- Sample keys:
	- `embeddings.token_embedding.weight` (`[10000, 128]`)
	- `embeddings.position_embedding.weight` (`[64, 128]`)
	- `embeddings.quantum_enhancer.qlayer.weights` (`[2, 4]`)
	- `blocks.0.attention.q_proj.weight` (`[128, 128]`)
	- `blocks.0.ffn.layer.weight.factors.factor_0` .. `factor_5` (TensorLy `FactorizedTensor` format)
	- `blocks.0.ffn.rank_scheduler.net.0.weight`

	---

	## 2. Canonical `strict=True` Loading Test Results

	Per Phase Two rules, `model.load_state_dict(sd, strict=True)` was executed against the repository's canonical implementations without altering checkpoint tensors or model dimensions:

	\| Target Model Class \| Config Class \| Model Params \| Model Keys \| Checkpoint Keys \| Exact Key Overlap \| Missing Keys \| Unexpected Keys \| `strict=True` Result \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| `src.models.QTensorFormer` \| `src.config.ModelConfig` \| `1,524,186` \| `185` \| `113` \| `0` (0.0%) \| `185` \| `113` \| `FAIL` (`RuntimeError`) \|
	\| `modeling_qtensorformer.QTensorFormerForCausalLM` \| `QTensorFormerConfig` \| `2,767,296` \| `52` \| `113` \| `0` (0.0%) \| `52` \| `113` \| `FAIL` (`RuntimeError`) \|

	### Why `qtensorformer_small.pt` Cannot Load Under `strict=True`
	1. Incompatible Tensor Factorization Library: `qtensorformer_small.pt` stores FFN weights using `tensorly` / `tltorch` 6-core `factors.factor_0..5` naming on a single fused FFN layer (`blocks.i.ffn.layer`), whereas `src.tensor_layers.TTFeedForward` uses two 4-core pure-PyTorch `TTLinear` projections (`blocks.i.tt_ffn.up_proj.cores.0..3` and `blocks.i.tt_ffn.down_proj.cores.0..3`).
	2. Different Module Hierarchy: `qtensorformer_small.pt` uses `embeddings.token_embedding` and `blocks.i.ffn.rank_scheduler.net`, whereas `src.models.QTensorFormer` uses `embedding.weight`, `blocks.i.info_engine`, and `blocks.i.allocator`.

	---

	## 3. Canonical Round-Trip `strict=True` Verification

	While the legacy external file `qtensorformer_small.pt` is incompatible, both canonical repository architectures pass `strict=True` save/load round-trip verification (`results/raw/checkpoint_integrity.json` and `scripts/smoke_test.py`):

	\| Canonical Implementation \| Serialization Method \| Reload Method \| `strict=True` Result \| Output Numerical Match \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| `src.models.QTensorFormer` \| `torch.save(model.state_dict(), path)` \| `model.load_state_dict(sd, strict=True)` \| `PASS` (185/185 keys) \| Max diff `0.0` \|
	\| `modeling_qtensorformer.QTensorFormerForCausalLM` \| `model.save_pretrained(dir)` \| `QTensorFormerForCausalLM.from_pretrained(dir)` \| `PASS` (52/52 keys) \| Max diff `< 1e-6` \|