Add pinned Nanbeige and Qwen architecture comparison

NOTICE

@@ -6,11 +6,14 @@ RecursiveSI/flint, Recursive Superintelligence, Inc.
 https://huggingface.co/RecursiveSI/flint
 Qwen/Qwen3.8-27B, Qwen Team
 https://huggingface.co/Qwen/Qwen3.8-27B
+Nanbeige/Nanbeige4.3-33B, Nanbeige
+https://huggingface.co/Nanbeige/Nanbeige4.3-33B
 The publishers distribute these models under Apache License 2.0:
 https://www.apache.org/licenses/LICENSE-2.0
 
 Configurations record architectural facts. The Qwen configuration describes
-its text decoder; the source model also includes other components.
+its text decoder; the source model also includes other components. The
+Nanbeige configuration is bundled for architecture comparison.
 Bundled measured activation captures are published by alphaBell for reuse
 with this viewer. No model weights are included.
 

architecture/comparison.css

@@ -0,0 +1,122 @@
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architecture/comparison.js

@@ -0,0 +1,123 @@
+'use strict';
+
+const paths = {
+  nanbeige: '../models/nanbeige.json',
+  qwen: '../models/qwen.json'
+};
+const names = { nanbeige: 'Nanbeige4.3-33B', qwen: 'Qwen3.8-27B text decoder' };
+const fmt = value => new Intl.NumberFormat('en-US').format(value);
+const byId = id => document.getElementById(id);
+const configs = {};
+let selected = { model: 'qwen', layer: 3 };
+
+function attentionType(model, layer) {
+  if (model === 'nanbeige') return 'full';
+  return configs.qwen.layer_types[layer] === 'linear_attention' ? 'linear' : 'full';
+}
+
+function specRow(label, left, right) {
+  const tr = document.createElement('tr');
+  for (const [tag, value] of [['th', label], ['td', left], ['td', right]]) {
+    const cell = document.createElement(tag);
+    if (tag === 'th') cell.scope = 'row';
+    cell.textContent = value;
+    tr.append(cell);
+  }
+  return tr;
+}
+
+function renderSpecs() {
+  const n = configs.nanbeige;
+  const q = configs.qwen;
+  const linear = q.layer_types.filter(type => type === 'linear_attention').length;
+  const full = q.layer_types.length - linear;
+  byId('nanbeige-layer-count').textContent = fmt(n.num_hidden_layers);
+  byId('qwen-layer-count').textContent = `${linear} + ${full}`;
+  const rows = [
+    ['Decoder layers', fmt(n.num_hidden_layers), fmt(q.num_hidden_layers)],
+    ['Attention pattern', '52 full', `${linear} recurrent linear / ${full} full`],
+    ['Hidden width', fmt(n.hidden_size), fmt(q.hidden_size)],
+    ['Dense feed-forward width', fmt(n.intermediate_size), fmt(q.intermediate_size)],
+    ['Full-attention query heads', fmt(n.num_attention_heads), fmt(q.num_attention_heads)],
+    ['Full-attention key/value heads', fmt(n.num_key_value_heads), fmt(q.num_key_value_heads)],
+    ['Full-attention head dimension', fmt(n.head_dim), fmt(q.head_dim)],
+    ['Configured maximum positions', fmt(n.max_position_embeddings), fmt(q.max_position_embeddings)]
+  ];
+  byId('spec-body').replaceChildren(...rows.map(row => specRow(...row)));
+}
+
+function fact(label, value) {
+  const group = document.createElement('div');
+  const dt = document.createElement('dt');
+  const dd = document.createElement('dd');
+  dt.textContent = label;
+  dd.textContent = value;
+  group.append(dt, dd);
+  return group;
+}
+
+function renderSelection() {
+  const { model, layer } = selected;
+  const config = configs[model];
+  const type = attentionType(model, layer);
+  const label = type === 'linear' ? 'Recurrent linear attention' : 'Full attention';
+  document.querySelectorAll('.layer-grid button').forEach(button => {
+    button.setAttribute('aria-pressed', String(button.dataset.model === model && Number(button.dataset.layer) === layer));
+  });
+  byId('inspect-title').textContent = `${names[model]} · layer ${layer + 1}`;
+  byId('inspect-description').textContent = type === 'linear'
+    ? 'This configured layer uses gated recurrent linear attention. Its head counts describe the linear key and value branches.'
+    : 'This configured layer uses grouped-query full attention. The feed-forward path is dense.';
+  const rows = [
+    fact('Attention', label),
+    fact('Hidden width', fmt(config.hidden_size)),
+    fact('Feed-forward width', fmt(config.intermediate_size)),
+    fact(type === 'linear' ? 'Linear key heads' : 'Query heads', fmt(type === 'linear' ? config.linear_num_key_heads : config.num_attention_heads)),
+    fact(type === 'linear' ? 'Linear value heads' : 'Key/value heads', fmt(type === 'linear' ? config.linear_num_value_heads : config.num_key_value_heads)),
+    fact(type === 'linear' ? 'Linear value dimension' : 'Head dimension', fmt(type === 'linear' ? config.linear_value_head_dim : config.head_dim))
+  ];
+  byId('inspect-facts').replaceChildren(...rows);
+}
+
+function renderMap(model) {
+  const config = configs[model];
+  const grid = byId(`${model}-grid`);
+  const buttons = Array.from({ length: config.num_hidden_layers }, (_, layer) => {
+    const type = attentionType(model, layer);
+    const button = document.createElement('button');
+    button.type = 'button';
+    button.className = `layer ${type}`;
+    button.dataset.model = model;
+    button.dataset.layer = layer;
+    button.textContent = String(layer + 1).padStart(2, '0');
+    button.setAttribute('aria-label', `${names[model]}, layer ${layer + 1}, ${type === 'linear' ? 'recurrent linear' : 'full'} attention`);
+    button.addEventListener('click', () => {
+      selected = { model, layer };
+      renderSelection();
+    });
+    return button;
+  });
+  grid.replaceChildren(...buttons);
+}
+
+async function init() {
+  try {
+    const entries = await Promise.all(Object.entries(paths).map(async ([model, path]) => {
+      const response = await fetch(path);
+      if (!response.ok) throw new Error(`${model}: HTTP ${response.status}`);
+      return [model, await response.json()];
+    }));
+    for (const [model, config] of entries) configs[model] = config;
+    if (configs.nanbeige.num_hidden_layers !== 52 || configs.qwen.layer_types.length !== configs.qwen.num_hidden_layers) throw new Error('Unexpected decoder configuration');
+    renderSpecs();
+    renderMap('nanbeige');
+    renderMap('qwen');
+    renderSelection();
+  } catch (error) {
+    byId('inspect-title').textContent = 'The comparison could not open';
+    byId('inspect-description').textContent = 'Reload this page to try the model configurations again.';
+    console.error('Architecture comparison failed', error);
+  }
+}
+
+init();

architecture/index.html

@@ -0,0 +1,57 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <meta name="theme-color" content="#f7f8f3">
+  <meta name="description" content="Compare the published decoder configurations of Nanbeige4.3-33B and Qwen3.8-27B, layer by layer.">
+  <title>Nanbeige and Qwen architecture | alphaBell</title>
+  <link rel="icon" href="../favicon.svg">
+  <link rel="stylesheet" href="comparison.css">
+  <script src="comparison.js" defer></script>
+</head>
+<body>
+  <header class="topbar">
+    <a class="brand" href="../index.html" aria-label="Model Observatory home"><span class="mark">α</span><span>alphaBell <span class="slash">/</span> observatory</span></a>
+    <a class="back" href="../index.html">Measured captures <span aria-hidden="true">↗</span></a>
+  </header>
+  <main>
+    <section class="intro" aria-labelledby="page-title">
+      <p class="eyebrow">Architecture comparison <span>01 / 02</span></p>
+      <h1 id="page-title">Two dense decoders.<br><em>Different paths through a token.</em></h1>
+      <p class="lead">Nanbeige repeats full attention in 52 layers. Qwen alternates recurrent linear attention with full attention across 64 text-decoder layers. Explore the published configuration, one layer at a time.</p>
+      <div class="scope"><span class="scope-dot" aria-hidden="true"></span><strong>Configuration view</strong><span>Layer types and dimensions come from pinned model configs. Nanbeige weights, activations and routing were not measured here.</span></div>
+    </section>
+
+    <section class="overview" aria-labelledby="overview-title">
+      <div class="section-heading"><p class="eyebrow">01 / At a glance</p><h2 id="overview-title">The decoder shapes</h2></div>
+      <div class="overview-grid">
+        <article class="model-card nanbeige-card">
+          <div class="model-head"><span class="model-letter">N</span><div><p class="model-family">Llama-style decoder</p><h3>Nanbeige4.3-33B</h3></div></div>
+          <p class="model-summary">A uniform stack: every configured layer uses full attention and a dense feed-forward network.</p>
+          <div class="hero-number"><strong id="nanbeige-layer-count">52</strong><span>full-attention layers</span></div>
+        </article>
+        <article class="model-card qwen-card">
+          <div class="model-head"><span class="model-letter">Q</span><div><p class="model-family">Hybrid text decoder</p><h3>Qwen3.8-27B</h3></div></div>
+          <p class="model-summary">A repeating pattern: three recurrent linear-attention layers, then one full-attention layer.</p>
+          <div class="hero-number"><strong id="qwen-layer-count">48 + 16</strong><span>linear + full layers</span></div>
+        </article>
+      </div>
+      <div class="spec-wrap"><table class="spec-table"><caption>Published decoder configuration</caption><thead><tr><th scope="col">Configuration</th><th scope="col">Nanbeige</th><th scope="col">Qwen text decoder</th></tr></thead><tbody id="spec-body"></tbody></table></div>
+    </section>
+
+    <section class="layers" aria-labelledby="layers-title">
+      <div class="section-heading"><p class="eyebrow">02 / Layer map</p><h2 id="layers-title">Follow the stack</h2><p>Each square is one configured decoder layer. Select a layer to inspect its attention and feed-forward dimensions.</p></div>
+      <div class="legend" aria-label="Layer colors"><span><i class="swatch full"></i>Full attention</span><span><i class="swatch linear"></i>Recurrent linear attention</span></div>
+      <div class="map-pair">
+        <div class="map-card"><div class="map-title"><h3>Nanbeige</h3><span>52 layers</span></div><div class="layer-grid" id="nanbeige-grid" role="group" aria-label="Nanbeige layers"></div></div>
+        <div class="map-card"><div class="map-title"><h3>Qwen text decoder</h3><span>64 layers</span></div><div class="layer-grid" id="qwen-grid" role="group" aria-label="Qwen text decoder layers"></div></div>
+      </div>
+      <div class="inspect" id="inspect" aria-live="polite"><p class="eyebrow">Selected layer</p><h3 id="inspect-title">Loading configuration</h3><p id="inspect-description"></p><dl id="inspect-facts"></dl></div>
+    </section>
+
+    <section class="source" aria-labelledby="source-title"><div class="section-heading"><p class="eyebrow">Pinned sources</p><h2 id="source-title">Inspect the configs</h2></div><p>Qwen values refer to the <code>text_config</code> decoder inside its released model configuration. Context values are configured maximum positions, not measured usable context.</p><div class="source-links"><a href="https://huggingface.co/Nanbeige/Nanbeige4.3-33B/blob/c1093c501cc6460427c0ac634db7223d0450c0ee/config.json" target="_blank" rel="noopener noreferrer">Nanbeige config <span>c1093c5 ↗</span></a><a href="https://huggingface.co/Qwen/Qwen3.8-27B/blob/1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0/config.json" target="_blank" rel="noopener noreferrer">Qwen config <span>1d4bf0f ↗</span></a></div></section>
+  </main>
+  <footer><span>alphaBell / Model Observatory</span><a href="../index.html">Explore measured Flint and Qwen captures ↗</a></footer>
+</body>
+</html>

index.html

@@ -3,7 +3,7 @@
 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>Flint &amp; Qwen Model Observatory | alphaBell</title><meta name="description" content="Explore Flint expert routing and Qwen recurrent memory with real Q8 tensor captures. Replay every token, inspect every block and import your own trace."><meta property="og:title" content="Model Observatory by alphaBell"><meta property="og:description" content="Two architectures. Real tensor captures. Every token."><meta property="og:image" content="https://huggingface.co/spaces/alphabell/Model-Observatory/resolve/main/cover.png"><meta name="theme-color" content="#176b59"><link rel="icon" href="favicon.svg"><link rel="stylesheet" href="style.css"></head>
 <body>
 <header class="models-header"><div class="header-start"><a class="brand" href="./"><span class="brand-icon">α</span><span>alphaBell<span class="brand-divider">/</span><b>observatory</b></span></a><label class="model-control"><span>MODEL</span><select id="modelSelect" aria-label="Model" disabled><option>Recursive Flint</option></select></label></div><div class="header-end"><span class="engine-status" id="engineStatus"><i></i>Loading capture</span><button class="icon-button" id="aboutButton" title="How to read the model" aria-label="How to read the model">?</button></div></header>
-<section class="overview"><div><div class="eyebrow" id="modelEyebrow">MODEL OBSERVATORY</div><h1>Inside Flint &amp; Qwen.</h1><a class="experiment-entry" href="experiments/index.html">Try the five experiments ↗</a></div><div class="stats" id="modelStats"></div></section>
+<section class="overview"><div><div class="eyebrow" id="modelEyebrow">MODEL OBSERVATORY</div><h1>Inside Flint &amp; Qwen.</h1><a class="experiment-entry" href="experiments/index.html">Try the five experiments ↗</a><a class="experiment-entry architecture-entry" href="architecture/index.html">Compare Nanbeige and Qwen architecture ↗</a></div><div class="stats" id="modelStats"></div></section>
 <main>
 <aside class="input-panel">
  <section class="prompt-section"><div class="section-heading"><h2>01 <span>Measured capture</span></h2><span class="small-tag" id="quantization">Q8_0</span></div><p id="traceOrigin" class="trace-origin">Opening example capture…</p><label class="sr-only" for="prompt" id="promptLabel">Captured prompt</label><textarea id="prompt" readonly spellcheck="false">Explain why the sky is blue in two sentences.</textarea><p class="capture-settings" id="captureSettings"></p><button id="runButton" class="primary" disabled>Loading capture…</button><p id="runNote" class="run-note" role="status">Structure is ready while the capture loads.</p><details class="capture-help"><summary>Use your own capture</summary><p>Import a compatible Flint or Qwen trace JSON. It stays in your browser. This public viewer replays measured captures; new prompts need a separate capture engine.</p><a href="https://huggingface.co/spaces/alphabell/Model-Observatory/blob/main/TRACE-FORMAT.md" target="_blank" rel="noreferrer">Trace format ↗</a></details></section>

models/nanbeige.json

@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 166100,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 166101,
+  "ffn_hidden_size": 22016,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 22016,
+  "kv_channels": 128,
+  "max_position_embeddings": 524288,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 56,
+  "num_hidden_layers": 52,
+  "num_key_value_heads": 8,
+  "num_query_groups": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 120000000,
+  "rotary_base": 120000000,
+  "seq_length": 524288,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.1",
+  "use_cache": false,
+  "vocab_size": 166912
+}

sources.json

@@ -7,6 +7,11 @@
     "Qwen/Qwen3.8-27B": {
       "sha": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
       "config": "https://huggingface.co/Qwen/Qwen3.8-27B/resolve/1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0/config.json"
+    },
+    "Nanbeige/Nanbeige4.3-33B": {
+      "sha": "c1093c501cc6460427c0ac634db7223d0450c0ee",
+      "config": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B/resolve/c1093c501cc6460427c0ac634db7223d0450c0ee/config.json",
+      "bundled_config_sha256": "ee6db71f6de1bc2d78c0adc543e339c44e9340c1e3f474285f3144d2d3cd3df9"
     }
   },
   "capture_engine": {
@@ -16,6 +21,7 @@
   "checks": {
     "flint_matched_configuration_fields": 38,
     "qwen_matched_configuration_fields": 23,
+    "nanbeige_matched_configuration_fields": 33,
     "differences": 0
   }
 }

style.css

@@ -145,3 +145,5 @@ button.expert-chip{border:0}
 @media(min-width:981px){main{height:max(760px,calc(100vh - 325px));grid-template-rows:minmax(0,1fr)}.input-panel,.model-panel{min-height:0}.model-map{min-height:0}}
 
 .experiment-entry{display:inline-block;margin-top:9px;font:11px system-ui,sans-serif;color:#176b59;text-underline-offset:4px}
+.architecture-entry{margin-left:15px}
+@media(max-width:650px){.architecture-entry{margin-left:0;display:block}}

tests/architecture.test.cjs

@@ -0,0 +1,66 @@
+'use strict';
+const test = require('node:test');
+const assert = require('node:assert/strict');
+const fs = require('node:fs');
+const path = require('node:path');
+const crypto = require('node:crypto');
+const root = path.resolve(__dirname, '..');
+const read = file => fs.readFileSync(path.join(root, file), 'utf8');
+const n = JSON.parse(read('models/nanbeige.json'));
+const q = JSON.parse(read('models/qwen.json'));
+const sources = JSON.parse(read('sources.json'));
+
+test('the pinned Nanbeige configuration supports the displayed comparison', () => {
+  assert.deepEqual(n.architectures, ['LlamaForCausalLM']);
+  assert.equal(n.num_hidden_layers, 52);
+  assert.equal(n.hidden_size, 7168);
+  assert.equal(n.intermediate_size, 22016);
+  assert.equal(n.num_attention_heads, 56);
+  assert.equal(n.num_key_value_heads, 8);
+  assert.equal(n.head_dim, 128);
+  assert.equal(n.max_position_embeddings, 524288);
+  assert.equal(n.num_experts, undefined);
+});
+
+test('the pinned Qwen text decoder has three linear layers for each full layer', () => {
+  assert.equal(q.model_type, 'qwen3_5_text');
+  assert.equal(q.layer_types.length, q.num_hidden_layers);
+  assert.equal(q.num_hidden_layers, 64);
+  for (let i = 0; i < q.layer_types.length; i++) {
+    assert.equal(q.layer_types[i], i % 4 === 3 ? 'full_attention' : 'linear_attention');
+  }
+  assert.equal(q.linear_num_key_heads, 16);
+  assert.equal(q.linear_num_value_heads, 48);
+  assert.equal(q.num_experts, 0);
+});
+
+test('the architecture route is linked and ships its local assets', () => {
+  const home = read('index.html');
+  const html = read('architecture/index.html');
+  assert.match(home, /href="architecture\/index\.html"/);
+  for (const file of ['architecture/comparison.css', 'architecture/comparison.js', 'models/nanbeige.json', 'models/qwen.json']) {
+    assert.ok(fs.statSync(path.join(root, file)).size > 0, file);
+  }
+  assert.match(html, /Nanbeige.*?config/s);
+  assert.match(html, /c1093c501cc6460427c0ac634db7223d0450c0ee/);
+  assert.match(html, /1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0/);
+  assert.match(html, /Nanbeige weights, activations and routing were not measured here/);
+  for (const text of [html, read('architecture/comparison.js'), read('architecture/comparison.css')]) {
+    assert.doesNotMatch(text, /\u2014|&mdash;|&#8212;|&#x2014;/i);
+  }
+});
+
+test('Nanbeige attribution and source pin match the bundled configuration', () => {
+  const source = sources.configurations['Nanbeige/Nanbeige4.3-33B'];
+  const notice = read('NOTICE');
+  const html = read('architecture/index.html');
+  assert.equal(source.sha, 'c1093c501cc6460427c0ac634db7223d0450c0ee');
+  assert.equal(source.config, `https://huggingface.co/Nanbeige/Nanbeige4.3-33B/resolve/${source.sha}/config.json`);
+  assert.equal(crypto.createHash('sha256').update(read('models/nanbeige.json')).digest('hex'), source.bundled_config_sha256);
+  assert.equal(Object.keys(n).length, 33);
+  assert.equal(sources.checks.nanbeige_matched_configuration_fields, Object.keys(n).length);
+  assert.match(html, new RegExp(source.sha));
+  assert.match(notice, /Nanbeige\/Nanbeige4\.3-33B, Nanbeige/);
+  assert.match(notice, /Apache License 2\.0/);
+  assert.match(notice, /https:\/\/www\.apache\.org\/licenses\/LICENSE-2\.0/);
+});