{"schemaVersion":1,"model":{"id":"ibm-granite/granite-3.1-1b-a400m-base","name":"Granite 3.1 1B-A400M","revision":"408b6e90baab8cf24f4aa9f8e19703ffa0a53b29","url":"https://huggingface.co/ibm-granite/granite-3.1-1b-a400m-base/tree/408b6e90baab8cf24f4aa9f8e19703ffa0a53b29","license":"Apache-2.0","numLayers":24,"numExperts":32,"topK":8,"hiddenSize":1024,"intermediateSize":512,"totalParameters":1334625280,"activeParametersApprox":400000000,"residualMultiplier":0.22,"attentionHeads":16,"keyValueHeads":8,"attentionHeadSize":64,"attentionMultiplier":0.015625,"embeddingMultiplier":12.0,"logitsScaling":6.0,"ropeTheta":1500000.0,"vocabSize":49152,"attentionMaskValue":-3.4028234663852886e+38},"capture":{"timestamp":"2026-10-15T23:58:46.733355+00:00","device":"cpu","dtype":"float32","python":"3.12.12","platform":"macOS-15.5-arm64-arm-64bit","packages":{"Jinja2":"3.1.6","MarkupSafe":"3.0.4","PyYAML":"6.0.3","accelerate":"1.6.0","certifi":"2026.7.22","charset-normalizer":"3.5.2","filelock":"4.2.0","fsspec":"2026.9.0","huggingface-hub":"0.30.2","idna":"3.20","mpmath":"1.3.0","networkx":"3.7","numpy":"2.5.4","packaging":"26.3","psutil":"7.2.2","regex":"2026.9.29","requests":"2.34.2","safetensors":"0.5.3","setuptools":"84.0.0","sympy":"1.13.1","tokenizers":"0.21.4","torch":"2.6.0","tqdm":"4.70.1","transformers":"4.51.3","typing_extensions":"4.16.0","urllib3":"2.8.0"},"script":"scripts/capture_moe_traces.py","scriptSha256":"20cbd407752309fb42e729ae3fc2f621fd1f5e103becc6d736c326e68896f7c5","modelFiles":[{"name":"model-00001-of-00002.safetensors","bytes":1993497352,"sha256":"6933b6375074dc6cc4c0b98c9e126cd5fc899ef0cd80db0a932a232c6393c9cd"},{"name":"model-00002-of-00002.safetensors","bytes":675779512,"sha256":"271870f88e36f28ca87e86e11fa971b0b3e268fb9ac4495d42c66fdd1bc9d060"}],"configSha256":"25af0a4e4e85b781fa22c7cab240cda31714254a9d3d0f2f67e079693e1dd614","attentionImplementation":"eager","seed":0,"threads":4,"notes":["Actual pretrained model execution in eval mode; no simulated routing or random model weights.","Each example is one full forward pass over the supplied prompt, with no generated continuation.","Forward hooks observe actual router logits, dispatch ordering, normalized gates, expert outputs, and MoE outputs.","Attention matrices are the actual post-softmax weights returned by eager attention, shaped [layer][head][query][key]. Eval mode disables attention dropout; future-token entries are exactly zero.","attentionScores and attentionMaskedScores are recomputed from actual observed Q/K projection outputs and the forward pass's actual RoPE and causal mask, using the same operations as Granite's eager attention. Their softmax must exactly match the model's returned attention. Scores include the configured 1/64 scaling; masked future entries are the actual float32 minimum value.","queryPreview/keyPreview/valuePreview contain the first 8 of 64 coordinates for each head and token, with full 64-coordinate norms. Queries and keys are after RoPE; keys and values repeat each of the 8 KV heads across its 2 query heads, matching grouped-query attention.","embeddingPreview is the token embedding lookup multiplied by 12, before the first layer. Granite uses rotary positional embeddings in attention; no learned positional vector is added to these embeddings.","attentionInputPreview is after each layer's input RMSNorm. attentionOutputPreview is after the attention output projection, before multiplication by 0.22 and residual addition.","finalHiddenPreview is after the final RMSNorm, before the LM head; the model divides LM-head logits by 6.","Every example is rerun without hooks; all vocabulary logits at every input position must match exactly.","Granite selects top-8 logits, then softmaxes those 8. routerProbabilities softmaxes all 32 captured logits for comparison; routingWeights are the actual gates used by the model.","Expert IDs and layer indices are zero-based. Selected arrays are ordered by descending router logit.","Previews show the first 8 of 1024 coordinates; norms and reconstruction validation use all 1024 coordinates.","Combined MoE outputs precede the decoder's residual multiplication by 0.22 and residual addition.","Next-token probabilities are from the full 49,152-token vocabulary after final normalization and LM head, without sampling temperature.","nextTokenLogits stores all final-token model logits, already divided by Granite's inherent logitsScaling=6. vocabulary maps every vocabulary ID to tokenizer.decode([ID]) with whitespace preserved. These allow exact full-vocabulary temperature and sampling demonstrations without rerunning the model.","The recorded nextTokenCandidates preserve PyTorch CPU float32 softmax outputs. A 49,152-way float32 reduction differs slightly from stable float64 softmax; each example records its full float32 probability mass and maximum float32-vs-float64 difference. Browser float64 recomputation uses the same real logits and may differ within this measured precision effect.","No semantic expert specialties are inferred. Experts with the same ID in different layers have different parameters.","Serialized numeric arrays are rounded to 9 decimal places. Full-vector reconstruction errors are measured before rounding.","Float32 reduction order can change last bits. All 1024 reconstructed coordinates pass PyTorch assert_close with atol=1e-5 and rtol=2e-6; reconstructionToleranceRatio is the maximum coordinate error divided by its allowed tolerance."]},"examples":[{"id":"language","title":"A familiar fact","category":"Language","prompt":"The capital of France is","tokens":[{"index":0,"id":1318,"text":"The"},{"index":1,"id":18926,"text":" capital"},{"index":2,"id":432,"text":" of"},{"index":3,"id":45600,"text":" France"},{"index":4,"id":438,"text":" 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