GoldWorm v6 β Byte-Level Causal Transformer (2.76M params)
Verified result: full BPC 1.007349 (step 8000, TinyStories-valid, 129,429 fixed outer-val positions). Beats GPT-2 124M BYTE_BPC = 1.142985 (TOKEN_BPC = 9.152823) on the same metric.
Dataset: roneneldan/TinyStories (TinyStories-valid.txt, 19.4 MB, sha256 94e431816c4cce81...).
Protocol: windowed stride-16 ctx-256 over fixed evaluation set; determinism verified (two bit-identical passes, max_abs_diff_per_position_bits = 0.0).
Model facts
- Type:
ByteTransformer - Vocab: 256 (byte-level)
- Config:
vocab_size=256, d_model=192, n_layer=6, n_head=4, ffn_mult=4, ctx=256, tie_embeddings=True, dropout=0.0 - Params: 2,757,504
- Checkpoint:
run/andrew_8000/checkpoint_selected.ptβmodel.safetensors - Selection (inner-val): step 8000,
inner_val_windowed_bpc = 1.051187 - Full-val (outer): step 8000,
full_bpc = 1.007349 - Baselines: unigram 4.437 / bigram 3.317 (add-k 0.01, train-matched)
- Gate: well below frozen v1 reference (2.566)
Files
model.safetensorsβ exported weights (tied embeddings cloned; load withByteTransformer(config))config.jsonβ architecture configeval_results.jsonβ full outer-val resultdemo/app.pyβ Streamlit text-generation demodemo/requirements.txtβtorch,safetensors,streamlit,numpy
Use with Streamlit demo
pip install -r demo/requirements.txt
streamlit run demo/app.py
Prompt is drawn from corpus/corpus.txt outer-val region; generation uses temperature / top-k sampling.
How to load (Python)
import sys, torch
sys.path.insert(0, '.')
from gwlm_v6 import TransformerConfig, ByteTransformer
from safetensors.torch import load_file
cfg = TransformerConfig(vocab_size=256, d_model=192, n_layer=6, n_head=4, ffn_mult=4, ctx=256, tie_embeddings=True, dropout=0.0)
model = ByteTransformer(cfg)
state = load_file("model.safetensors")
model.load_state_dict(state)
model.eval()
Verification
- Chunk shape / parity / replication checks verified per
LLM_PARITY_ROADMAP.md - Independent replication (step 8000) confirmed bit-exact with selection frozen before any outer_val read
- Source repo:
C:/Users/Student/GoldWorm/GoldWorm/goldworm-lm-v0/
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