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  <title>Documentation, Setup &amp; API Reference - Q-TensorFormer</title>
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    <h1>Documentation &amp; API Reference</h1>
    <p class="lead">
      Q-TensorFormer provides two first-class API interfaces: the <strong>Research API</strong> in <code>src/</code> for closed-loop dynamic systems experiments, and the <strong>Hugging Face API</strong> for standard <code>AutoModelForCausalLM</code> pipelines.
    </p>

    <section>
      <h2>1. Installation</h2>
      <p>Clone the repository and install dependencies:</p>
      <pre><code>git clone https://huggingface.co/Premchan369/Q-TensorFormer
cd Q-TensorFormer
pip install -r requirements.txt</code></pre>
      <p>Dependencies: <code>torch >= 2.0.0</code>, <code>transformers >= 4.35.0</code>, <code>numpy >= 1.24.0</code>, <code>pytest >= 7.0.0</code>.</p>
    </section>

    <section>
      <h2>2. PyTorch Research API (src/)</h2>
      <p>Load the verified canonical checkpoint and perform inference:</p>
      <pre><code>import torch
from src.checkpoint import load_canonical_checkpoint
from src.tokenizer import QTensorFormerTokenizer

# 1. Load canonical tokenizer
tokenizer = QTensorFormerTokenizer.load("checkpoints/tokenizer/tokenizer.json")

# 2. Load canonical checkpoint with strict verification
model, info = load_canonical_checkpoint(
    "checkpoints/qtensorformer_canonical_trained.pt",
    strict=True,
    require_trained=True,
    tokenizer=tokenizer
)

# 3. Tokenize input text
prompt = "Transformer attention"
input_ids = torch.tensor([tokenizer.encode(prompt, add_bos=True)])

# 4. Generate with KV cache and deterministic greedy decoding
output_ids = model.generate(
    input_ids,
    max_new_tokens=15,
    do_sample=False,
    use_cache=True,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id
)

print("Decoded Output:", tokenizer.decode(output_ids[0]))</code></pre>
    </section>

    <section>
      <h2>3. Hugging Face Transformers API</h2>
      <p>Use Q-TensorFormer with the standard Hugging Face pipeline:</p>
      <pre><code>from transformers import AutoConfig, AutoModelForCausalLM

# Load model from the canonical checkpoint directory
model_path = "checkpoints/hf_canonical_trained"
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)

# Generate using Hugging Face .generate()
import torch
prompt_ids = torch.tensor([[1, 25, 42, 10]])
generated = model.generate(prompt_ids, max_new_tokens=10, do_sample=False)
print("Generated Token IDs:", generated)</code></pre>
    </section>

    <section>
      <h2>4. Deployment &amp; Export Tools</h2>
      <div class="grid">
        <div class="card">
          <h3>GGUF v3 Binary Export</h3>
          <p>Export the model to Ollama / llama.cpp compatible GGUF format:</p>
          <pre><code>python qtensorformer_to_gguf.py \
  --checkpoint checkpoints/qtensorformer_canonical_trained.pt \
  --output qtensorformer.gguf \
  --strict</code></pre>
        </div>
        <div class="card">
          <h3>ONNX Graph Export</h3>
          <p>Export Tensor-Train linear layers to ONNX for edge runtime acceleration:</p>
          <pre><code>python export_onnx.py \
  --output qtensorformer.onnx \
  --batch_size 1 \
  --seq_len 32</code></pre>
        </div>
      </div>
    </section>

    <section>
      <h2>5. Running Unit Tests &amp; Smoke Tests</h2>
      <p>Verify that your environment matches our benchmark findings:</p>
      <pre><code># Run full pytest suite (94 passing tests)
pytest -v

# Run deterministic smoke test
python scripts/smoke_test.py</code></pre>
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