docs/weights.md

Run Banyan Sapling

The Hugging Face release is published as MubitLabs/banyan-sapling. The package includes complete trained weights, tokenizer, calibration, inference code, and a distribution manifest. A separate base-model download is not required.

Reference environment

The model was evaluated with Python 3.12, CUDA 13.0, and an NVIDIA H200. Use a CUDA environment compatible with the package's pinned PyTorch build and install requirements.txt. The checkpoint is approximately 46 GB (43 GiB); allow additional GPU memory for the CUDA runtime and activations. The verified hardware is an H200. Memory use increases with input length.

The loader checks exact package versions against runtime_versions.json. Different library versions, precision, kernels, or hardware require separate validation. CPU and quantized inference are outside this reference runtime.

Download

Install the Hugging Face CLI using the official CLI instructions. Download to a local directory:

hf download MubitLabs/banyan-sapling --local-dir ./banyan-sapling-model
cd banyan-sapling-model
python3.12 -m venv .venv
.venv/bin/python -m pip install -r requirements.txt

For repeatable deployments, pass --revision with the Hugging Face commit SHA. Use a fresh environment for the pinned runtime.

Verify and run

.venv/bin/python inference.py --check-files
.venv/bin/python inference.py --input examples/policy_claim.json

The input is a JSON object containing policy, evidence, and claim. Each must be a nonempty string. The combined encoded input, including formatting, is limited to 16,384 tokens. The output contains raw_label, status, both prediction sets, calibrated probabilities, and the source weights identity. Checksum verification reads the complete checkpoint before loading.

The supplied example asks whether a deployment is permitted after a rejected security review. Its expected semantic label is violation. In the reference run, the preferred classification was violation and the calibrated status was unsure, with violation and insufficient_information in the prediction set. This example illustrates why integrations should retain both output fields. The recorded response is in evidence/package_validation.json.

Repeated requests in Python

Run from the downloaded model directory:

from pathlib import Path
from inference import load_model, predict

model, tokenizer, calibration, manifest = load_model(Path('.'))
result = predict(model, tokenizer, calibration, manifest, {
    'policy': 'Deployment requires an approved security review. A rejected review prohibits deployment.',
    'evidence': 'The security review for deployment D-204 was rejected.',
    'claim': 'Deployment D-204 is permitted.',
})
print(result)

Load once and issue requests sequentially. The reference path uses one example per model invocation. The weights contain a custom verification head and unmerged attention adapters, so use this loader rather than a generic text-generation pipeline.

Package identity

distribution_manifest.json records SHA-256 hashes for distributed files. The original trained weights hash is 103a67f7a112589346c3e1090f5bca182d7fdafee85dfd43e67ac5d75eb04dd4. Original evaluation and runtime summaries are included under evidence/. See provenance for the relationship to the archived release.