{
 "version": "v2",
 "input": {
  "shape": "[T, 512]",
  "dtype": "float32",
  "what": "EmbeddingGemma 2 audio soft tokens (embed_audio output), 25 per second"
 },
 "context": "bidirectional within the window",
 "architecture": {
  "name": "a",
  "kind": "transformer encoder from scratch",
  "layers": 6,
  "d_model": 512,
  "heads": 8,
  "ffn": 2048,
  "activation": "gelu",
  "norm": "pre-LayerNorm",
  "positions": "rotary (base 10000)",
  "input": "LayerNorm -> Linear 512->512",
  "output": "LayerNorm -> Linear 512->768",
  "params": 19573762
 },
 "output": {
  "vector": "[T, 768] L2-normalized",
  "blank_logit": "[T]"
 },
 "logits": "scale * cos(vector, table[w]) for w in vocab; blank from blank_logit",
 "scale": 216.3509979248047,
 "vocab": "vocab.txt",
 "table": {
  "file": "word_table.npy",
  "dtype": "float16",
  "shape": "[200000, 768]",
  "prompt": "task: search result | query: ",
  "normalized": true
 },
 "decode": "greedy argmax over N+1, collapse repeats, drop blank",
 "window": {
  "seconds": 30,
  "left_overlap_tokens": 96,
  "right_extra_tokens": 1,
  "note": "encoder is causal; the 83 tokens at the start of a window and its last token differ from a continuous run (fixtures/long-speckled-band: soft_tokens_w30 vs soft_tokens_continuous)"
 },
 "weights": {
  "file": "reader.pt",
  "format": "torch state_dict, float32; training matmuls in TF32 (A100), evaluation in fp32",
  "code": "train/model.py Reader(arch, n_vocab, k, layers)",
  "keys": "body.* (reader body), blank.weight [1, 768], blank.bias [1], log_scale (scale = exp(log_scale))"
 },
 "n_vocab": 200000,
 "wer": {
  "dev-clean": {
   "wer": 0.03754,
   "utterances": 2703,
   "ref_words": 54402
  },
  "dev-other": {
   "wer": 0.10263,
   "utterances": 2864,
   "ref_words": 50948
  },
  "test-clean": {
   "wer": 0.04207,
   "utterances": 2620,
   "ref_words": 52576
  },
  "test-other": {
   "wer": 0.10422,
   "utterances": 2939,
   "ref_words": 52343
  },
  "fixtures": {
   "wer": 0.03697,
   "utterances": 20,
   "ref_words": 541
  },
  "decode": "greedy, no language model"
 }
}