WALDO 1B initial experimental reference checkpoint
Experimental reference weights — Apache License 2.0.
This is a raw causal base model, provided for reproducing WALDO training and
evaluation experiments. The frozen behavioral qualification did not pass.
It is not instruction-tuned, and character/topic drift and factual errors remain.
See evaluation/ for the complete assessment and limitations.
Exact artifact
- Managed model:
general-base-1b-a-tuned-01 - Native run:
5652ba3bce3a7f6c - Selected checkpoint within stage: 271073
- Selected artifact: 4138071392 bytes, SHA-256
f3159fa6b8be2acd8efbc0d9daebeba59dcaf2f1f9ed47ff925f656a187028cb - Held-out FP32-inference loss: 2.195388929
- Held-out bits per byte: 0.799569705
- Stage completed: 295716 steps / 4845010944 target tokens
- Selected snapshot token position within stage: 4441260032
The continued model starts from the initial model's selected trained weights, with a new AdamW optimizer and a lower peak LR (1e-4 versus 4e-4). It replays the same deterministic data stream; extra exposure is not unique new data. The native BOMs retain the exact architecture, 32K byte-BPE tokenizer, corpus mixture, held-out selection, runtime and training controls. No qualification tolerances were relaxed.
Download from Hugging Face
Download the complete repository, retaining its native/ directory structure:
hf download whiskeywhiskey/waldo-1b-initial-reference --local-dir ./waldo-1b-initial-reference
cd waldo-1b-initial-reference
Then use the WALDO commands below. The companion continued checkpoint is at waldo-1b-continued-reference.
Loading and format
This is an inference-only managed snapshot, not a complete native archive
or a Transformers/GGUF conversion. It retains exact metadata and current
published artifacts; intermediate checkpoint/optimizer payloads are omitted
and enumerated in PACKAGE.json. The continued snapshot also retains its
required parent initializer under base/. It cannot restore every historical
checkpoint. The separate full-backup staging tree retains all source files.
Use a WALDO CLI with the original native PyTorch model/tokenizer support and a compatible PyTorch environment. After downloading the repository:
waldo model summary "$PWD/native/general-base-1b-a-tuned-01"
CUDA_VISIBLE_DEVICES=0 waldo model chat "$PWD/native/general-base-1b-a-tuned-01" --raw --temperature 0 --seed 42 --max-tokens 64 "A map helps people"
The same snapshot is smoke-tested with CUDA hidden during preparation. It
does not require access to the original absolute training-data paths for
inference. Standard Hugging Face/GGUF export is currently unsupported for
this trained 32K tokenizer; do not assume AutoModel.from_pretrained works.
Provenance, attribution and license
See PACKAGE.json, the immutable native model/run BOMs, the compose retained
in the managed metadata, and ATTRIBUTION.md. Training-data declarations
include CC-BY-SA-4.0, CDLA-Sharing-1.0 and ODC-BY-1.0 and remain preserved.
The operator explicitly chose Apache-2.0 for these weights to match WALDO's
project license. See LICENSE, NOTICE and LICENSING.md. This is an independent
experimental release, not an official OpenWALDO model.
The native records retain historical host/runtime paths for reproducibility. Git commit signing and native model/export signing are separate; this package does not claim a signed WALDO release or complete regulatory disclosure.