Upload SAE model: SAE-Res-Qwen3.5-35B-A3B-Base-W32K-L0_50

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README.md

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+---
+license: other
+license_name: qwen
+license_link: https://huggingface.co/Qwen/SAE-Res-Qwen3.5-35B-A3B-Base-W32K-L0_50/blob/main/LICENSE
+language:
+- en
+tags:
+- sparse-autoencoder
+- sae
+- mechanistic-interpretability
+- interpretability
+- qwen-scope
+base_model: Qwen/Qwen3.5-35B-A3B
+---
+
+## Qwen-Scope: Decoding Intelligence, Unleashing Potential
+
+![Overview](https://qianwen-res.oss-cn-beijing.aliyuncs.com/qwen-scope/Figures/overview.png)
+
+We are excited to introduce Qwen-Scope, an interpretability module trained on the Qwen3 and Qwen3.5 series models. Specifically, we integrated and trained Sparse Autoencoders (SAEs) within Qwen’s hidden layers. By implementing sparsity constraints, we can automatically extract data features that are highly decoupled, low-redundancy, and significantly more interpretable. Qwen-Scope can be used not only to analyze the internal mechanisms of Qwen’s behavior but also holds immense potential for model optimization. Application scenarios include steerable inference control, evaluation sample distribution analysis and comparison, data classification and synthesis, and model training and optimization.
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base model | [Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B) |
+| SAE width (`d_sae`) | 32768 |
+| Hidden size (`d_model`) | 2048 |
+| Expansion factor | 16× |
+| Top-K | 50 |
+| Hook point | Residual stream |
+| Layers covered | 0 – 39 (40 layers total) |
+| File format | PyTorch `.pt` dict |
+
+## Architecture
+
+This is a **TopK SAE** — at each forward pass, exactly **50** features are kept non-zero.
+
+Each checkpoint file `layer{n}.sae.pt` is a Python `dict` with four tensors:
+
+| Key | Shape | Description |
+|---|---|---|
+| `W_enc` | `(32768, 2048)` | Encoder weight matrix |
+| `W_dec` | `(2048, 32768)` | Decoder weight matrix |
+| `b_enc` | `(32768,)` | Encoder bias |
+| `b_dec` | `(2048,)` | Decoder bias |
+
+## Files
+
+This repository contains one SAE checkpoint per transformer layer (layers 0–39):
+
+```
+layer0.sae.pt
+layer1.sae.pt
+...
+layer39.sae.pt
+```
+
+## Feature Activation Extraction
+
+End-to-end demo: run the base LLM, hook the residual stream at a chosen layer, and extract sparse SAE feature activations.
+For most of the situations, using SAEs trained on base models to explore the internal process of post-training checkpoints is also reasonable.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ── 1. Load base model ────────────────────────────────────────────────────────
+model_name = "Qwen/Qwen3.5-35B-A3B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32)
+model.eval()
+
+# ── 2. Load SAE for a target layer ───────────────────────────────────────────
+LAYER = 0  # choose any layer in 0–39
+sae = torch.load(f"layer{LAYER}.sae.pt", map_location="cpu")
+W_enc = sae["W_enc"]  # (32768, 2048)
+b_enc = sae["b_enc"]  # (32768,)
+
+def get_feature_acts(residual: torch.Tensor) -> torch.Tensor:
+    """residual: (..., 2048) → sparse feature activations (..., 32768)"""
+    pre_acts = residual @ W_enc.T + b_enc
+    topk_vals, topk_idx = pre_acts.topk(50, dim=-1)
+    acts = torch.zeros_like(pre_acts)
+    acts.scatter_(-1, topk_idx, topk_vals)
+    return acts
+
+# ── 3. Hook residual stream after the target transformer layer ────────────────
+captured = {}
+
+def _hook(module, input, output):
+    hidden = output[0] if isinstance(output, tuple) else output
+    captured["residual"] = hidden.detach().cpu()
+
+hook = model.model.layers[LAYER].register_forward_hook(_hook)
+
+# ── 4. Forward pass ───────────────────────────────────────────────────────────
+text = "The capital of France is"
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    model(**inputs)
+hook.remove()
+
+# ── 5. Extract feature activations ───────────────────────────────────────────
+residual = captured["residual"]               # (1, seq_len, 2048)
+feature_acts = get_feature_acts(residual)     # (1, seq_len, 32768)
+
+# Inspect active features for the last token
+last_token_acts = feature_acts[0, -1]         # (32768,)
+active_idx = last_token_acts.nonzero(as_tuple=True)[0]
+print(f"Active features : {active_idx.tolist()}")
+print(f"Feature values  : {last_token_acts[active_idx].tolist()}")
+```
+
+## Gradio Demo
+
+We also provide a gradio demo `app.py`. You can run it locally:
+```
+python app.py \
+    --model Qwen/Qwen3.5-35B-A3B \
+    --model-name-sae-trained-from qwen3.5-35b-a3b-base \
+    --model-name-analyzing-now qwen3.5-35b-a3b \
+    --sae-path Qwen/SAE-Res-Qwen3.5-35B-A3B-Base-W32K-L0_50 \
+    --top-k 50 \
+    --num-layers 40 \
+    --sae-width 32768 \
+    --d-model 2048 \
+    --server-port 7860
+```
+
+## Caution
+It is strictly prohibited to use interpretability tools for non-scientific research purposes to interfere with model capabilities, or to fabricate, generate, and disseminate harmful information that violates public order, good morals, and socialist core values, including pornographic, violent, discriminatory, or incendiary content. Violators will have their authorization automatically terminated and shall bear all legal liabilities arising therefrom. The right of final interpretation of this statement belongs to the project owner.
+
+## Citation
+
+If you use these SAEs in your research, please cite:
+
+```bibtex
+@misc{qwenscope,
+  title  = {{Qwen-Scope}: Turning Sparse Features into Development Tools for Large Language Models},
+  author = {{Qwen Team}},
+  month  = {April},
+  year   = {2026}
+}
+```

app.py

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+"""
+app.py — SAE Feature Explorer for Qwen3 models, whether pretrain (base) or posttrain (thinking/instruct) models.
+"""
+
+import argparse
+import html as _html
+import json as _json
+import os
+from collections import OrderedDict
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# ─── CLI arguments ────────────────────────────────────────────────────────────
+_parser = argparse.ArgumentParser(description="SAE Feature Explorer")
+_parser.add_argument(
+    '--model',
+    default='Qwen/Qwen3-30B-A3B-Base',
+    help='Path to the base model directory (default: %(default)s)',
+)
+_parser.add_argument(
+    '--model-name-sae-trained-from',
+    default='qwen3-30b-a3b-base',
+    help='The name of model which present representations for SAE training (default: %(default)s)',
+)
+_parser.add_argument(
+    '--model-name-analyzing-now',
+    default='qwen3-30b-a3b',
+    help='The name of model which is used for analyzing now (default: %(default)s)',
+)
+_parser.add_argument(
+    '--sae-path',
+    default='Qwen/SAE-Res-Qwen3-30B-A3B-Base-W128K-L0_100',
+    help='Path to the directory containing layer*.sae.pt files (default: %(default)s)',
+)
+_parser.add_argument(
+    '--top-k',
+    type=int,
+    default=100,
+    help='Number of top features to display (default: %(default)s)',
+)
+_parser.add_argument(
+    '--num-layers',
+    type=int,
+    default=48,
+    help='Number of transformer layers in the model (default: %(default)s)',
+)
+_parser.add_argument(
+    '--sae-width',
+    type=int,
+    default=131_072,
+    help='SAE dictionary width / number of features (default: %(default)s)',
+)
+_parser.add_argument(
+    '--d-model',
+    type=int,
+    default=2048,
+    help='Model hidden dimension (default: %(default)s)',
+)
+_parser.add_argument(
+    '--sae-cache-max',
+    type=int,
+    default=8,
+    help='Maximum number of SAE layers to keep in memory at once (default: %(default)s)',
+)
+_parser.add_argument(
+    '--server-port',
+    type=int,
+    default=7860,
+    help='Port number for server',
+)
+_args = _parser.parse_args()
+
+# ─── Config ──────────────────────────────────────────────────────────────────
+MODEL_PATH                  = _args.model
+MODEL_NAME_SAE_TRAINED_FROM = _args.model_name_sae_trained_from
+MODEL_NAME_ANALYZING_NOW    = _args.model_name_analyzing_now
+SAE_PATH                    = _args.sae_path
+TOP_K                       = _args.top_k
+NUM_LAYERS                  = _args.num_layers
+SAE_WIDTH                   = _args.sae_width
+D_MODEL                     = _args.d_model
+SAE_CACHE_MAX               = _args.sae_cache_max
+PORT                        = _args.server_port
+
+# ─── Generation defaults (from model's generation_config.json) ────────────────
+
+_gen_cfg: dict = {}
+_gen_cfg_path = os.path.join(MODEL_PATH, 'generation_config.json')
+if os.path.exists(_gen_cfg_path):
+    with open(_gen_cfg_path) as _f:
+        _gen_cfg = _json.load(_f)
+    print(f"Loaded generation_config.json from {_gen_cfg_path}")
+else:
+    print(f"No generation_config.json found at {_gen_cfg_path}; using built-in defaults.")
+
+GEN_DO_SAMPLE    = bool(_gen_cfg.get('do_sample',           False))
+GEN_TEMPERATURE  = float(_gen_cfg.get('temperature',        1.0))
+GEN_TOP_P        = float(_gen_cfg.get('top_p',              1.0))
+GEN_TOP_K        = int(_gen_cfg.get('top_k',                1))
+GEN_REP_PENALTY  = float(_gen_cfg.get('repetition_penalty', 1.0))
+STEER_DISPLAY_K  = 10   # top-k candidates shown in the per-token probability panel
+
+# ─── Device resolution ───────────────────────────────────────────────────────
+
+def _resolve_sae_device() -> torch.device:
+    """
+    Pick the device for SAE weights and encoder/decoder computations.
+
+    CUDA_VISIBLE_DEVICES remaps physical GPUs so that the first listed GPU
+    always appears as cuda:0 inside this process.  We simply use cuda:0
+    when any CUDA device is visible; fall back to CPU otherwise.
+    """
+    if not torch.cuda.is_available():
+        print("SAE device: cpu  (no CUDA visible)")
+        return torch.device('cpu')
+    cvd = os.environ.get('CUDA_VISIBLE_DEVICES', '<unset>')
+    device = torch.device('cuda:0')
+    print(f"SAE device: {device} — {torch.cuda.get_device_name(device)}"
+          f"  [CUDA_VISIBLE_DEVICES={cvd}]")
+    return device
+
+SAE_DEVICE = _resolve_sae_device()
+
+# ─── Global singletons ───────────────────────────────────────────────────────
+_model     = None
+_tokenizer = None
+_sae_lru: OrderedDict = OrderedDict()
+_orig_cache: dict | None = None   # cached unsteered generation result
+
+
+def get_model():
+    global _model, _tokenizer
+    if _model is None:
+        print("Loading model…")
+        _model = AutoModelForCausalLM.from_pretrained(
+            MODEL_PATH, device_map='auto', torch_dtype='auto'
+        )
+        _tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+        _model.eval()
+        print("Model ready.")
+    return _model, _tokenizer
+
+
+def get_sae(layer: int) -> dict:
+    if layer in _sae_lru:
+        _sae_lru.move_to_end(layer)
+        return _sae_lru[layer]
+    if len(_sae_lru) >= SAE_CACHE_MAX:
+        _sae_lru.popitem(last=False)
+    path = os.path.join(SAE_PATH, f'layer{layer}.sae.pt')
+    try:
+        sae = torch.load(path, map_location=SAE_DEVICE, weights_only=True)
+    except TypeError:
+        sae = torch.load(path, map_location=SAE_DEVICE)
+    # Pre-convert and transpose encoder weights once on load so compute_sae_features
+    # never repeats the conversion on every call.
+    sae['_W_enc'] = sae['W_enc'].T.to(dtype=torch.float32)  # [d_model, sae_width]
+    sae['_b_enc'] = sae['b_enc'].to(dtype=torch.float32)      # [sae_width]
+    _sae_lru[layer] = sae
+    return sae
+
+
+# ─── Core math ───────────────────────────────────────────────────────────────
+
+def topk_relu(x: torch.Tensor, k: int = TOP_K) -> torch.Tensor:
+    # Scatter top-k ReLU values directly — avoids creating a full-size boolean mask
+    # and an element-wise multiply, saving two [seq, SAE_WIDTH] allocations.
+    relu_x = torch.relu(x)
+    values, indices = torch.topk(relu_x, k, dim=-1)
+    out = torch.zeros_like(relu_x)
+    out.scatter_(-1, indices, values)
+    return out
+
+
+@torch.no_grad()
+def capture_hidden(model, input_ids: torch.Tensor, layer: int) -> torch.Tensor:
+    buf = {}
+    def _hook(module, inp, out):
+        # Qwen3MoE decoder layers return a plain tensor [batch, seq, hidden].
+        # out[0] removes the batch dim → [seq, hidden]; then move to SAE_DEVICE.
+        buf['h'] = out[0].detach().to(SAE_DEVICE, dtype=torch.float32)
+    handle = model.model.layers[layer].register_forward_hook(_hook)
+    model(input_ids)
+    handle.remove()
+    return buf['h']   # [seq_len, d_model]
+
+
+@torch.no_grad()
+def capture_all_hiddens(model, input_ids: torch.Tensor, layers: list) -> dict:
+    """
+    Capture residual-stream hidden states at multiple layers in a single
+    forward pass by registering simultaneous hooks.  Tensors are stored on
+    SAE_DEVICE as float32 so downstream SAE matmuls need no extra transfer.
+    """
+    buf = {}
+    handles = []
+    for layer in layers:
+        def make_hook(l):
+            def _hook(module, inp, out):
+                buf[l] = out[0].detach().to(SAE_DEVICE, dtype=torch.float32)
+            return _hook
+        handles.append(model.model.layers[layer].register_forward_hook(make_hook(layer)))
+    model(input_ids)
+    for h in handles:
+        h.remove()
+    return buf  # {layer_idx: Tensor[seq, d_model] on SAE_DEVICE}
+
+
+def compute_sae_features(hidden: torch.Tensor, sae: dict,
+                         raw: bool = False) -> torch.Tensor:
+    # Use pre-converted weights cached on load (avoids .float()/.T on every call)
+    W_enc = sae['_W_enc']   # [d_model, sae_width] float32 on SAE_DEVICE
+    b_enc = sae['_b_enc']   # [sae_width] float32 on SAE_DEVICE
+    pre   = hidden @ W_enc + b_enc            # [seq, sae_width] — pre-activation on SAE_DEVICE
+    if raw:
+        return pre                            # keep negative values intact; caller handles device
+    return topk_relu(pre, TOP_K)              # stays on SAE_DEVICE; caller calls .tolist() as needed
+
+
+# ─── UI helpers ──────────────────────────────────────────────────────────────
+
+
+def parse_positions(s: str):
+    """
+    Parse a position string into 'all' or a sorted list of int indices.
+
+    Supported syntax (comma-separated, combinable):
+      all        → every token position
+      5          → single position
+      3-7        → inclusive range (positions 3, 4, 5, 6, 7)
+      0,2,5-8    → mix of individual positions and ranges
+    """
+    s = s.strip().lower()
+    if s == 'all':
+        return 'all'
+    try:
+        positions: list[int] = []
+        for part in s.split(','):
+            part = part.strip()
+            if not part:
+                continue
+            if '-' in part:
+                lo, hi = part.split('-', 1)
+                positions.extend(range(int(lo.strip()), int(hi.strip()) + 1))
+            else:
+                positions.append(int(part))
+        return sorted(set(positions))
+    except Exception:
+        return 'all'
+
+
+def feature_heatmap_to_html(tokens: list, features: torch.Tensor, top_k: int, skip_first: bool = False) -> str:
+    """
+    Build a 2-D HTML heatmap:
+      rows  = top-k features (ranked by mean activation across all positions)
+      cols  = token positions
+      color = activation value (white → red, normalised per feature row by row max)
+    """
+
+    seq_len, sae_width = features.shape
+    top_k = min(int(top_k), sae_width)
+
+    # ── Optionally exclude the first token ────────────────────────────────────
+    if skip_first and seq_len > 1:
+        features = features[1:]
+        tokens   = tokens[1:]
+        seq_len -= 1
+
+    # ── Select top-k features by mean activation across all positions ─────────
+    mean_per_feat = features.mean(dim=0)                # [sae_width]
+    top_vals, top_idx = torch.topk(mean_per_feat, top_k)
+    feat_acts = features[:, top_idx]                    # [seq_len, top_k]
+
+    # ── Token column headers ──────────────────────────────────────────────────
+    TH_STYLE = (
+        "min-width:38px;max-width:70px;padding:4px 3px;"
+        "text-align:center;font-weight:500;font-size:11px;"
+        "color:#444;border-bottom:2px solid #c7d2e8;"
+        "overflow:hidden;white-space:nowrap;vertical-align:bottom;"
+    )
+    tok_headers = []
+    for i, tok in enumerate(tokens):
+        raw   = tok.strip() or f"[{i}]"
+        short = _html.escape(raw[:6] + "…" if len(raw) > 6 else raw)
+        full  = _html.escape(raw)
+        tok_headers.append(
+            f'<th style="{TH_STYLE}" title="pos {i}: {full}">{short}</th>'
+        )
+
+    # ── Data rows ─────────────────────────────────────────────────────────────
+    FEAT_TD  = (
+        "font-family:ui-monospace,monospace;font-size:11px;"
+        "padding:3px 8px;color:#2563eb;white-space:nowrap;"
+        "border-right:2px solid #c7d2e8;background:#f8faff;"
+        "position:sticky;left:0;z-index:1;"
+    )
+    AVG_TD   = (
+        "font-size:10px;padding:3px 6px;color:#777;white-space:nowrap;"
+        "border-right:1px solid #e4e7ef;text-align:right;"
+    )
+    CELL_BASE = (
+        "border:1px solid rgba(0,0,0,0.05);min-width:38px;height:30px;"
+        "text-align:center;vertical-align:middle;"
+    )
+
+    rows_html = []
+    for fi in range(top_k):
+        feat_i  = int(top_idx[fi])
+        avg_val = float(top_vals[fi])
+        row_acts = feat_acts[:, fi]             # [seq_len]
+        row_max  = float(row_acts.max())
+        norm     = row_max if row_max > 0 else 1.0
+
+        cells = []
+        for pos in range(seq_len):
+            v = float(row_acts[pos])
+            t = max(0.0, min(1.0, v / norm))
+            # white → amber → deep red
+            r = 255
+            g = int(255 * (1 - 0.8 * t))
+            b = int(255 * (1 - t))
+            cells.append(
+                f'<td style="{CELL_BASE}background:rgb({r},{g},{b});"'
+                f' title="feat #{feat_i} | pos {pos} | act={v:.4f}">'
+                f'</td>'
+            )
+
+        rows_html.append(
+            f'<tr>'
+            f'<td style="{FEAT_TD}">#{feat_i}</td>'
+            f'<td style="{AVG_TD}">{avg_val:.3f}</td>'
+            + "".join(cells)
+            + "</tr>"
+        )
+
+    # ── Assemble table ────────────────────────────────────────────────────────
+    header_row = (
+        '<tr>'
+        '<th style="padding:4px 8px;text-align:left;font-size:11px;font-weight:700;'
+        'color:#2563eb;border-bottom:2px solid #c7d2e8;border-right:2px solid #c7d2e8;'
+        'background:#f8faff;position:sticky;left:0;z-index:2;">Feature</th>'
+        '<th style="padding:4px 6px;font-size:11px;font-weight:700;color:#777;'
+        'border-bottom:2px solid #c7d2e8;border-right:1px solid #e4e7ef;">'
+        'Avg&nbsp;act.</th>'
+        + "".join(tok_headers)
+        + "</tr>"
+    )
+
+    legend = (
+        '<div style="display:flex;align-items:center;gap:10px;margin-top:10px;'
+        'font-size:11px;color:#888;">'
+        '<span>0</span>'
+        '<div style="width:140px;height:12px;border-radius:6px;'
+        'background:linear-gradient(to right,#fff,#ff6600,#cc0000);'
+        'border:1px solid #ddd;"></div>'
+        '<span>peak activation (per-feature row-max scale)</span>'
+        '</div>'
+    )
+
+    return (
+        '<div style="overflow-x:auto;max-height:520px;overflow-y:auto;">'
+        '<table style="border-collapse:collapse;width:100%;'
+        'font-family:ui-monospace,monospace;">'
+        f'<thead style="position:sticky;top:0;background:#fff;z-index:3;">'
+        f'{header_row}</thead>'
+        f'<tbody>{"".join(rows_html)}</tbody>'
+        '</table>'
+        '</div>'
+        + legend
+    )
+
+
+def tokens_with_positions_html(tokens: list, positions) -> str:
+    """
+    Render tokenized prompt as coloured token chips.
+
+    Steered positions (amber/gold) are visually distinct from unsteered ones (grey).
+    positions: 'all'  →  every index is highlighted
+               list   →  only those indices
+    """
+
+    if not tokens:
+        return (
+            '<div style="padding:10px;color:#bbb;font-size:13px;">'
+            'Enter a prompt above to preview token positions.</div>'
+        )
+
+    all_positions = positions if isinstance(positions, list) else []
+    pos_set = (
+        set(range(len(tokens))) if positions == 'all'
+        else {p for p in all_positions if 0 <= p < len(tokens)}
+    )
+    # Positions beyond the prompt — will be steered in the generated text
+    generated_positions = (
+        [] if positions == 'all'
+        else sorted(p for p in all_positions if p >= len(tokens))
+    )
+
+    parts = []
+    for i, tok in enumerate(tokens):
+        steered = i in pos_set
+        txt   = _html.escape(tok)
+        title = _html.escape(repr(tok.strip()), quote=True)
+
+        if steered:
+            bg, border, text_color = "#fef3c7", "2px solid #f59e0b", "#92400e"
+        else:
+            bg, border, text_color = "#f1f5f9", "1px solid #e2e8f0", "#475569"
+
+        parts.append(
+            f'<span style="background:{bg};color:{text_color};'
+            f'padding:3px 7px;margin:2px 1px;border-radius:5px;'
+            f'display:inline-block;border:{border};'
+            f'font-family:ui-monospace,monospace;font-size:12px;" '
+            f'title="pos {i}: {title}">'
+            f'<sub style="opacity:.55;font-size:9px;margin-right:2px">{i}</sub>'
+            f'{txt}</span>'
+        )
+
+    n_steered = len(pos_set)
+    summary = (
+        f'<div style="margin-top:6px;font-size:11px;color:#888;">'
+        f'{len(tokens)}&nbsp;tokens total&nbsp;&nbsp;·&nbsp;&nbsp;'
+        f'<span style="color:#92400e;font-weight:600;">{n_steered}&nbsp;steered</span>'
+        f'&nbsp;<span style="color:#f59e0b;">■</span>'
+        f'</div>'
+    )
+
+    generated_note = ''
+    if generated_positions:
+        gp_str = ', '.join(str(p) for p in generated_positions)
+        generated_note = (
+            f'<div style="margin-top:4px;font-size:11px;padding:4px 8px;'
+            f'background:#eff6ff;border:1px solid #bfdbfe;border-radius:4px;color:#1d4ed8;">'
+            f'Positions {gp_str} are beyond the prompt — they will be steered '
+            f'in the <em>generated</em> text during autoregressive decoding.'
+            f'</div>'
+        )
+
+    return (
+        '<div style="padding:8px 4px;line-height:2.8;">'
+        + ' '.join(parts)
+        + summary
+        + generated_note
+        + '</div>'
+    )
+
+
+def cb_feature_heatmap(state, top_k: int, skip_first: bool):
+    if state is None:
+        return (
+            '<div style="min-height:80px;display:flex;align-items:center;'
+            'justify-content:center;color:#bbb;font-size:13px;">'
+            'Run analysis first to see the feature heatmap.</div>'
+        )
+    tokens, features = state
+    return feature_heatmap_to_html(tokens, features, int(top_k), bool(skip_first))
+
+
+# ─── Gradio callbacks ────────────────────────────────────────────────────────
+
+def cb_analyze(text: str, layer: int):
+    try:
+        model, tokenizer = get_model()
+        input_ids = tokenizer.encode(text, return_tensors='pt').to(
+            next(model.parameters()).device
+        )
+        tokens   = [tokenizer.decode([t]) for t in input_ids[0].tolist()]
+        hidden   = capture_hidden(model, input_ids, int(layer))
+        features = compute_sae_features(hidden, get_sae(int(layer)))
+        return (tokens, features)
+    except Exception as e:
+        raise gr.Error(f"Analysis failed: {e}")
+
+
+
+def _steering_strength_from_mode(mode: str, diff_lookup, layer: int, feat_idx: int,
+                                  custom_val: float = 5.0) -> float:
+    """Map Light/Medium/Strong/Custom to an actual steering strength.
+
+    Looks up the feature-specific diff for (layer, feat_idx) from the
+    Feature Comparison results.  Falls back to the global max across all
+    compared features, then to fixed defaults when no data is available.
+    """
+    if mode == "Custom":
+        return float(custom_val)
+    d = 0.0
+    if diff_lookup and isinstance(diff_lookup, dict):
+        key = (int(layer), int(feat_idx))
+        if key in diff_lookup:
+            d = float(diff_lookup[key])
+        else:
+            d = float(max(diff_lookup.values(), default=0.0))
+    if d <= 0:
+        return {"Light": 5.0, "Medium": 20.0, "Strong": 100.0}.get(mode, 5.0)
+    return {"Light": round(d * 0.5, 2),
+            "Medium": round(d * 2.0, 2),
+            "Strong": round(d * 10.0, 2)}.get(mode, round(d, 2))
+
+
+def cb_generate(prompt, layer, feat_idx, pos_str, steer_mode, compare_diff,
+                steer_output_only, max_tok, greedy, top_k_tok, top_p, rep_penalty, temp,
+                custom_strength=5.0):
+    try:
+        return _cb_generate_inner(prompt, layer, feat_idx, pos_str, steer_mode, compare_diff,
+                                  steer_output_only, max_tok, greedy, top_k_tok, top_p, rep_penalty, temp,
+                                  custom_strength)
+    except gr.Error:
+        raise
+    except Exception as e:
+        raise gr.Error(f"Generation failed: {e}")
+
+
+def cb_update_steer_preview(prompt: str, pos_str: str):
+    """Tokenise the prompt and return an HTML token-position preview."""
+    if not prompt.strip():
+        return (
+            '<div style="padding:10px;color:#bbb;font-size:13px;">'
+            'Enter a prompt above to preview steered positions.</div>'
+        )
+    try:
+        _, tokenizer = get_model()
+        input_ids = tokenizer.encode(prompt)
+        tokens    = [tokenizer.decode([t]) for t in input_ids]
+        positions = parse_positions(pos_str)
+        return tokens_with_positions_html(tokens, positions)
+    except Exception as e:
+        return (
+            f'<div style="padding:10px;color:#dc2626;font-size:13px;">'
+            f'Preview error: {e}</div>'
+        )
+
+
+def _extract_probs(gen_out, input_len: int, tokenizer, display_k: int):
+    """
+    Extract per-step token probabilities from a `return_dict_in_generate=True,
+    output_scores=True` GenerateOutput.
+
+    Returns (text, tokens, chosen_probs, topk_data) where:
+      tokens       : list[str]  — decoded token strings
+      chosen_probs : list[float] — probability of the chosen token (0-1)
+      topk_data    : list[list[[str, float, bool]]] — top-k candidates at each step,
+                     each entry is [token_str, prob, is_chosen]
+    """
+    new_ids     = gen_out.sequences[0][input_len:]
+    new_id_list = new_ids.tolist()
+
+    # Batch-decode chosen tokens and all top-k candidates in two passes
+    # instead of O(n * display_k) individual tokenizer.decode() calls.
+    all_topk_ids: list[list[int]] = []
+    chosen_probs: list[float]     = []
+    topk_vals_list: list          = []
+    chosen_in_top_list: list[bool]= []
+
+    for score_t, tok_id in zip(gen_out.scores, new_id_list):
+        probs              = torch.softmax(score_t[0].float(), dim=-1)
+        chosen_probs.append(float(probs[tok_id]))
+        top_vals, top_ids  = torch.topk(probs, display_k)
+        tid_list           = top_ids.tolist()
+        chosen_in_top      = tok_id in tid_list
+        all_topk_ids.append(tid_list)
+        topk_vals_list.append(top_vals.tolist())
+        chosen_in_top_list.append(chosen_in_top)
+
+    # Single batch_decode call for all chosen tokens
+    tokens: list[str] = tokenizer.batch_decode(
+        [[t] for t in new_id_list], skip_special_tokens=False
+    )
+
+    # Single batch_decode call for all top-k candidate tokens
+    flat_ids      = [tid for ids in all_topk_ids for tid in ids]
+    flat_decoded  = tokenizer.batch_decode(
+        [[t] for t in flat_ids], skip_special_tokens=False
+    )
+
+    topk_data = []
+    flat_idx  = 0
+    for i, (tok_id, ids, vals, chosen_in_top, chosen_prob) in enumerate(
+        zip(new_id_list, all_topk_ids, topk_vals_list, chosen_in_top_list, chosen_probs)
+    ):
+        entry = []
+        for tid, tv in zip(ids, vals):
+            entry.append([flat_decoded[flat_idx], tv, tid == tok_id])
+            flat_idx += 1
+        if not chosen_in_top:
+            entry.append([tokens[i], chosen_prob, True])
+        topk_data.append(entry)
+
+    text = tokenizer.decode(new_ids, skip_special_tokens=True)
+    return text, tokens, chosen_probs, topk_data
+
+
+def probs_to_html(tokens: list, chosen_probs: list, topk_data: list,
+                  panel_id: str, theme: str = 'blue') -> str:
+    """
+    Render per-token generation probabilities as coloured chips.
+    Clicking a chip pins its top-k candidate table in the panel below;
+    clicking the same chip again or another chip toggles/switches the display.
+    Scroll-stable: no hover events that fire on page scroll.
+
+    theme: 'blue' for original output, 'red' for steered output.
+    """
+
+    if not tokens:
+        return ('<div style="padding:10px;color:#bbb;font-size:13px;">'
+                'No tokens generated.</div>')
+
+    # ── Chip colour (white → saturated) based on probability ─────────────────
+    def _colors(prob: float):
+        t = max(0.0, min(1.0, prob))
+        if theme == 'blue':
+            r, g, b = int(255 * (1 - t * 0.85)), int(255 * (1 - t * 0.65)), 255
+            txt = '#1e3a8a' if t < 0.55 else '#ffffff'
+        else:
+            r, g, b = 255, int(255 * (1 - t * 0.82)), int(255 * (1 - t))
+            txt = '#7f1d1d' if t < 0.55 else '#ffffff'
+        return f'rgb({r},{g},{b})', txt
+
+    # ── Pre-build the top-k panel HTML in Python ──────────────────────────────
+    TH = 'padding:2px 8px;font-size:11px;color:#6b7280;border-bottom:1px solid #e4e7ef;'
+
+    def _panel_html(entry: list) -> str:
+        rows = []
+        for rank, (tok_str, prob, is_chosen) in enumerate(entry, 1):
+            bg = 'background:#dbeafe;' if is_chosen else ''
+            fw = 'font-weight:700;'    if is_chosen else ''
+            mk = ' ✓'                  if is_chosen else ''
+            rows.append(
+                f'<tr style="border-bottom:1px solid #f4f6ff;{bg}">'
+                f'<td style="padding:2px 8px;text-align:right;font-size:11px;color:#9ca3af;">{rank}</td>'
+                f'<td style="padding:2px 8px;font-family:monospace;font-size:12px;{fw}">{_html.escape(tok_str)}{mk}</td>'
+                f'<td style="padding:2px 8px;text-align:right;font-family:monospace;font-size:12px;">{prob:.4f}</td>'
+                f'<td style="padding:2px 8px;text-align:right;font-family:monospace;font-size:12px;">{prob * 100:.2f}%</td>'
+                f'</tr>'
+            )
+        return (
+            '<table style="border-collapse:collapse;width:100%;font-size:12px;">'
+            f'<thead style="background:#f8faff;"><tr>'
+            f'<th style="{TH}text-align:right;">Rank</th>'
+            f'<th style="{TH}text-align:left;">Token</th>'
+            f'<th style="{TH}text-align:right;">Prob</th>'
+            f'<th style="{TH}text-align:right;">%</th>'
+            f'</tr></thead>'
+            f'<tbody>{"".join(rows)}</tbody>'
+            '</table>'
+        )
+
+    # ── Inline JS — click to pin, click again to unpin ───────────────────────
+    # Uses data-prob-root to scope sibling chips without global IDs.
+    # Single-quoted JS string literals are safe inside double-quoted HTML attrs.
+    # Non-f-string parts: { } are literal characters (no f-string substitution).
+    JS_CLICK = (
+        "var root=this.closest('[data-prob-root]');"
+        "if(!root)return;"
+        "var p=root.querySelector('[data-topk-panel]');"
+        "if(!p)return;"
+        "var sel=this.dataset.selected==='1';"
+        "root.querySelectorAll('[data-chip]').forEach(function(e){"
+        "e.dataset.selected='0';e.style.outline='';});"
+        "if(sel){"
+        "p.innerHTML='';p.style.display='none';"
+        "}else{"
+        "this.dataset.selected='1';"
+        "this.style.outline='2px solid #94a3b8';"
+        "this.style.outlineOffset='-1px';"
+        "p.innerHTML=this.getAttribute('data-panel');"
+        "p.style.display='block';"
+        "}"
+    )
+
+    def _tok_disp(s: str) -> str:
+        return s.replace('\n', '↵').replace('\r', '↵').replace('\t', '→')
+
+    # ── Build chips ───────────────────────────────────────────────────────────
+    chips = []
+    for tok, prob, entry in zip(tokens, chosen_probs, topk_data):
+        bg, txt = _colors(prob)
+        panel_attr = _html.escape(_panel_html(entry), quote=True)
+        chips.append(
+            f'<span data-chip data-selected="0" '
+            f'style="background:{bg};color:{txt};padding:3px 8px 2px;margin:1px;'
+            f'border-radius:5px;display:inline-block;cursor:pointer;white-space:nowrap;'
+            f'font-family:ui-monospace,monospace;font-size:12px;" '
+            f'data-panel="{panel_attr}" '
+            f'onclick="{JS_CLICK}">'
+            f'{_html.escape(_tok_disp(tok))}'
+            f'<sub style="opacity:.75;font-size:9px;margin-left:3px;">{prob * 100:.1f}%</sub>'
+            f'</span>'
+        )
+
+    return (
+        '<div data-prob-root style="padding:2px;">'
+        '<div style="font-size:11px;color:#888;margin-bottom:6px;font-style:italic;">'
+        'Click a token to pin its top-k candidates &nbsp;·&nbsp; click again to dismiss.</div>'
+        '<div style="padding:4px;line-height:2.8;">'
+        + ''.join(chips)
+        + '</div>'
+        + '<div data-topk-panel style="display:none;margin-top:8px;padding:4px;'
+          'background:#f8faff;border:1px solid #e4e7ef;border-radius:6px;'
+          'max-height:220px;overflow-y:auto;"></div>'
+        + '</div>'
+    )
+
+
+def _cb_generate_inner(prompt, layer, feat_idx, pos_str, steer_mode, compare_diff,
+                       steer_output_only, max_tok, greedy, top_k_tok, top_p, rep_penalty, temp,
+                       custom_strength=5.0):
+    global _orig_cache
+    model, tokenizer = get_model()
+    layer    = int(layer)
+    feat_idx = int(feat_idx)
+    if not (0 <= feat_idx < SAE_WIDTH):
+        raise gr.Error(f"Feature index must be in [0, {SAE_WIDTH - 1}].")
+    strength  = _steering_strength_from_mode(steer_mode, compare_diff, layer, feat_idx, float(custom_strength))
+    positions = parse_positions(pos_str)
+
+    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(
+        next(model.parameters()).device
+    )
+
+    # Build generation kwargs shared by both calls
+    gen_kwargs: dict = dict(max_new_tokens=int(max_tok),
+                            return_dict_in_generate=True, output_scores=True)
+    if greedy:
+        gen_kwargs['do_sample'] = False
+    else:
+        gen_kwargs['do_sample']              = True
+        gen_kwargs['temperature']            = float(temp)
+        gen_kwargs['top_k']                  = int(top_k_tok)
+        gen_kwargs['top_p']                  = float(top_p)
+        gen_kwargs['repetition_penalty']     = float(rep_penalty)
+
+    prompt_len = input_ids.shape[1]
+
+    # ── Original generation (cached) ─────────────────────────────────────────
+    # The unsteered output depends only on the prompt and decoding parameters,
+    # not on any steering inputs.  Reuse the last result when those are unchanged.
+    if greedy:
+        orig_key = (prompt, int(max_tok), True)
+    else:
+        orig_key = (prompt, int(max_tok), False,
+                    int(top_k_tok), float(top_p), float(rep_penalty), float(temp))
+
+    if _orig_cache is not None and _orig_cache['key'] == orig_key:
+        orig_text       = _orig_cache['text']
+        orig_probs_html = _orig_cache['probs_html']
+    else:
+        with torch.no_grad():
+            orig_out = model.generate(input_ids, **gen_kwargs)
+        orig_text, orig_toks, orig_probs, orig_topk = _extract_probs(
+            orig_out, prompt_len, tokenizer, STEER_DISPLAY_K
+        )
+        orig_probs_html = probs_to_html(orig_toks, orig_probs, orig_topk,
+                                        'topk-panel-orig', theme='blue')
+        _orig_cache = dict(key=orig_key, text=orig_text, probs_html=orig_probs_html)
+
+    sae          = get_sae(layer)
+    steering_vec = sae['W_dec'][:, feat_idx].float()   # [d_model]
+    pos_set      = None if positions == 'all' else set(positions)
+    counter      = [0]
+
+    def _steer_hook(module, inp, out):
+        # out: plain tensor [batch, seq, hidden] for Qwen3MoE
+        h           = out.clone()
+        sv          = steering_vec.to(device=h.device, dtype=h.dtype)  # one fused transfer
+        cur_counter = counter[0]
+        counter[0] += 1
+        if cur_counter == 0:
+            # Prefill: apply position-based steering to the prompt
+            if positions == 'all':
+                h = h + strength * sv
+            else:
+                for p in positions:
+                    if 0 <= p < h.shape[1]:
+                        h[:, p, :] = h[:, p, :] + strength * sv
+        else:
+            # Decode step (KV-cache): h is [batch, 1, hidden]
+            # Steer if: output-only mode is on, positions='all', or this position is listed
+            cur_seq_pos = prompt_len + cur_counter - 1
+            if steer_output_only or positions == 'all' or cur_seq_pos in pos_set:
+                h[:, 0, :] = h[:, 0, :] + strength * sv
+        return h
+
+    handle = model.model.layers[layer].register_forward_hook(_steer_hook)
+    with torch.no_grad():
+        steer_out = model.generate(input_ids, **gen_kwargs)
+    handle.remove()
+    steer_text, steer_toks, steer_probs, steer_topk = _extract_probs(
+        steer_out, prompt_len, tokenizer, STEER_DISPLAY_K
+    )
+
+    steer_probs_html = probs_to_html(steer_toks, steer_probs, steer_topk,
+                                     'topk-panel-steer', theme='red')
+
+    return orig_text, steer_text, orig_probs_html, steer_probs_html
+
+
+
+# ─── Feature Comparison helpers ──────────────────────────────────────────────
+
+def compare_to_html(records: list, text1: str, text2: str,
+                    tokens1: list = None, tokens2: list = None) -> tuple:
+    """
+    Render comparison results as two HTML strings:
+      - tok_display_html: token rows for the left panel (data-tok-display root)
+      - feature_table_html: feature table for the right panel
+
+    Returns (tok_display_html, feature_table_html).
+    """
+
+    _TOK_PLACEHOLDER = (
+        '<div style="min-height:60px;display:flex;align-items:center;'
+        'justify-content:center;color:#bbb;font-size:13px;padding:8px;">'
+        'Run Compare to see token activations here.</div>'
+    )
+
+    if not records:
+        return (
+            _TOK_PLACEHOLDER,
+            '<div style="min-height:80px;display:flex;align-items:center;'
+            'justify-content:center;color:#bbb;font-size:13px;">'
+            'No results — try a wider layer range or larger Top-K.</div>',
+        )
+
+    # ── Token display blocks ──────────────────────────────────────────────────
+    TOK_SPAN = (
+        "display:inline-block;padding:3px 7px;margin:2px 1px;"
+        "border-radius:5px;font-family:ui-monospace,monospace;font-size:12px;"
+        "background:#eef2ff;color:#374151;cursor:default;"
+        "transition:background .1s;border:1px solid rgba(0,0,0,0.06);"
+    )
+
+    def render_tok_row(tokens, seq_id):
+        parts = []
+        for i, tok in enumerate(tokens):
+            txt   = _html.escape(tok)
+            title = _html.escape(repr(tok.strip()), quote=True)
+            parts.append(
+                f'<span data-seq={seq_id} data-pos={i} style="{TOK_SPAN}" '
+                f'title="pos {i}: {title}">'
+                f'<sub style="opacity:.5;font-size:9px;margin-right:2px">{i}</sub>'
+                f'{txt}</span>'
+            )
+        return " ".join(parts)
+
+    # Build token display HTML for the left panel
+    if tokens1 and tokens2:
+        tok_inner = (
+            '<div style="margin-bottom:10px;color:#1e293b;">'
+            '<div style="font-size:11px;font-weight:700;color:#2563eb;'
+            'text-transform:uppercase;letter-spacing:.5px;margin-bottom:5px;">'
+            f'Example 1 &nbsp;<span style="font-weight:400;color:#888;">'
+            f'({len(tokens1)} tokens)</span></div>'
+            '<div style="line-height:2.8;padding:8px 10px;background:#fafbff;'
+            'border-radius:8px;border:1px solid #e4e7ef;overflow-x:auto;">'
+            + render_tok_row(tokens1, 1)
+            + '</div></div>'
+            '<div style="margin-bottom:8px;color:#1e293b;">'
+            '<div style="font-size:11px;font-weight:700;color:#dc2626;'
+            'text-transform:uppercase;letter-spacing:.5px;margin-bottom:5px;">'
+            f'Example 2 &nbsp;<span style="font-weight:400;color:#888;">'
+            f'({len(tokens2)} tokens)</span></div>'
+            '<div style="line-height:2.8;padding:8px 10px;background:#fafbff;'
+            'border-radius:8px;border:1px solid #e4e7ef;overflow-x:auto;">'
+            + render_tok_row(tokens2, 2)
+            + '</div></div>'
+            '<div style="font-size:11px;color:#888;font-style:italic;">'
+            'Hover a feature row on the right to highlight activations.</div>'
+        )
+    else:
+        tok_inner = _TOK_PLACEHOLDER
+
+    # Wrap with data-tok-display so the JS hover handler can find it across columns
+    tok_display_html = f'<div data-tok-display style="padding:2px;">{tok_inner}</div>'
+
+    # ── Per-layer max for bar-width normalization ─────────────────────────────
+    _layer_max: dict = {}
+    for _d, _l, *_ in records:
+        if _d > _layer_max.get(_l, 0.0):
+            _layer_max[_l] = _d
+
+    # ── Inline JS snippets for hover-highlight ────────────────────────────────
+    # Uses document.querySelector('[data-tok-display]') so the handler works
+    # across Gradio columns (token panel on left, feature table on right).
+    _JS_ENTER = (
+        "var d=document.querySelector('[data-tok-display]');"
+        "if(!d)return;"
+        "var a1=JSON.parse(this.getAttribute('data-acts1'));"
+        "var a2=JSON.parse(this.getAttribute('data-acts2'));"
+        "if(!a1||!a2)return;"
+        "var pk=Math.max.apply(null,a1.map(Math.abs).concat(a2.map(Math.abs)))||0.0001;"
+        "function c1(v){var t=Math.abs(v)/pk;"
+        "return 'rgb('+Math.round(255*(1-t))+','+Math.round(255*(1-.6*t))+',255)'}"
+        "function c2(v){var t=Math.abs(v)/pk;"
+        "return 'rgb(255,'+Math.round(255*(1-.8*t))+','+Math.round(255*(1-t))+')'}"
+        "d.querySelectorAll('[data-seq]').forEach(function(e){"
+        "var s=e.dataset.seq,p=parseInt(e.dataset.pos,10);"
+        "if(s==='1'&&p<a1.length)e.style.background=c1(a1[p]);"
+        "else if(s==='2'&&p<a2.length)e.style.background=c2(a2[p]);});"
+        "this.style.outline='2px solid #94a3b8';"
+        "this.style.outlineOffset='-1px';"
+    )
+    _JS_LEAVE = (
+        "var d=document.querySelector('[data-tok-display]');"
+        "if(!d)return;"
+        "d.querySelectorAll('[data-seq]').forEach(function(e){e.style.background='';});"
+        "this.style.outline='';"
+    )
+
+    TR_BASE = "border-bottom:1px solid #f0f4ff;"
+    TH = (
+        "padding:6px 10px;font-size:11px;font-weight:700;text-transform:uppercase;"
+        "letter-spacing:.5px;white-space:nowrap;"
+    )
+
+    rows_html = []
+    current_layer = None
+    layer_rank = 0
+    for _rank, record in enumerate(records, 1):
+        diff_val, layer, feat_idx, act1, act2 = record[:5]
+        acts1_pos = record[5] if len(record) > 5 else None
+        acts2_pos = record[6] if len(record) > 6 else None
+
+        # Insert a layer-group header row whenever the layer changes
+        if layer != current_layer:
+            current_layer = layer
+            layer_rank = 0
+            rows_html.append(
+                f'<tr style="background:#eef2ff;border-top:2px solid #c7d2e8;">'
+                f'<td colspan="6" style="padding:4px 12px;font-size:11px;font-weight:700;'
+                f'color:#2563eb;letter-spacing:.5px;">Layer {layer}</td>'
+                f'</tr>'
+            )
+        layer_rank += 1
+
+        bar_w = max(2, int(120 * diff_val / (_layer_max.get(layer) or 1.0)))
+        if act1 >= act2:
+            bar_color = "#2563eb"
+            dir_label = "Ex&nbsp;1&nbsp;▲"
+            dir_color = "#2563eb"
+            row_bg    = "background:#f5f8ff;"
+        else:
+            bar_color = "#dc2626"
+            dir_label = "Ex&nbsp;2&nbsp;▲"
+            dir_color = "#dc2626"
+            row_bg    = "background:#fff5f5;"
+
+        # Embed per-position activation arrays for the hover handler
+        if acts1_pos is not None and acts2_pos is not None:
+            a1_json = _json.dumps(acts1_pos)
+            a2_json = _json.dumps(acts2_pos)
+            tr_open = (
+                f"<tr style='{TR_BASE}{row_bg}cursor:pointer;'"
+                f" data-acts1='{a1_json}'"
+                f" data-acts2='{a2_json}'"
+                f' onmouseenter="{_JS_ENTER}"'
+                f' onmouseleave="{_JS_LEAVE}">'
+            )
+        else:
+            tr_open = f'<tr style="{TR_BASE}{row_bg}">'
+
+        rows_html.append(
+            tr_open
+            + f'<td style="padding:5px 10px;text-align:center;color:#9ca3af;font-size:11px;">{layer_rank}</td>'
+            + f'<td style="padding:5px 10px;font-family:monospace;color:#2563eb;">#{feat_idx}</td>'
+            + f'<td style="padding:5px 8px;text-align:right;font-family:monospace;color:#374151;">{act1:.1%}</td>'
+            + f'<td style="padding:5px 8px;text-align:right;font-family:monospace;color:#374151;">{act2:.1%}</td>'
+            + f'<td style="padding:5px 10px;">'
+            +  f'  <div style="display:flex;align-items:center;gap:6px;">'
+            +  f'    <div style="width:{bar_w}px;height:10px;background:{bar_color};'
+            +  f'         border-radius:3px;flex-shrink:0;"></div>'
+            +  f'    <span style="font-family:monospace;font-size:12px;color:#374151;">{diff_val:.1%}</span>'
+            +  f'  </div>'
+            + f'</td>'
+            + f'<td style="padding:5px 10px;font-size:11px;font-weight:700;color:{dir_color};">'
+            + f'{dir_label}</td>'
+            + '</tr>'
+        )
+
+    ex1_short = _html.escape(text1[:50] + "…" if len(text1) > 50 else text1)
+    ex2_short = _html.escape(text2[:50] + "…" if len(text2) > 50 else text2)
+
+    legend = (
+        '<div style="display:flex;flex-wrap:wrap;gap:16px;margin-top:12px;'
+        'font-size:11px;color:#6b7280;">'
+        f'<span><span style="color:#2563eb;font-weight:700;">■ Ex 1</span>'
+        f' "{ex1_short}"</span>'
+        f'<span><span style="color:#dc2626;font-weight:700;">■ Ex 2</span>'
+        f' "{ex2_short}"</span>'
+        '</div>'
+    )
+
+    table_inner = (
+        '<div style="overflow-x:auto;max-height:560px;overflow-y:auto;color:#1e293b;">'
+        '<table style="border-collapse:collapse;width:100%;color:#1e293b;'
+        'font-family:ui-monospace,monospace;font-size:13px;">'
+        '<thead style="background:#f8faff;color:#1e293b;border-bottom:2px solid #c7d2e8;'
+        'position:sticky;top:0;z-index:2;">'
+        '<tr>'
+        f'<th style="{TH}color:#9ca3af;">Rank</th>'
+        f'<th style="{TH}color:#2563eb;">Feature</th>'
+        f'<th style="{TH}color:#2563eb;text-align:right;">Rate&nbsp;Ex&nbsp;1</th>'
+        f'<th style="{TH}color:#dc2626;text-align:right;">Rate&nbsp;Ex&nbsp;2</th>'
+        f'<th style="{TH}color:#6b7280;">|Rate diff|</th>'
+        f'<th style="{TH}color:#6b7280;">Higher</th>'
+        '</tr>'
+        '</thead>'
+        f'<tbody>{"".join(rows_html)}</tbody>'
+        '</table>'
+        '</div>'
+    )
+
+    feature_table_html = (
+        '<div style="padding:2px;">'
+        + table_inner
+        + legend
+        + '</div>'
+    )
+
+    return tok_display_html, feature_table_html
+
+
+def cb_compare(text1: str, text2: str, layer_from: int, layer_to: int,
+               top_k: int, skip_first: bool,
+               remove_common_toks: bool, remove_prefix: bool,
+               raw_acts: bool = False):
+    try:
+        if not text1.strip() or not text2.strip():
+            raise gr.Error("Both examples must be non-empty.")
+
+        model, tokenizer = get_model()
+        layer_from = int(layer_from)
+        layer_to   = int(layer_to)
+        top_k      = int(top_k)
+        if layer_from > layer_to:
+            layer_from, layer_to = layer_to, layer_from
+        layers = list(range(layer_from, layer_to + 1))
+
+        # ── Tokenise ─────────────────────────────────────────────────────────
+        model_dev = next(model.parameters()).device
+        ids1 = tokenizer.encode(text1, return_tensors='pt').to(model_dev)
+        ids2 = tokenizer.encode(text2, return_tensors='pt').to(model_dev)
+        toks1 = ids1[0].tolist()
+        toks2 = ids2[0].tolist()
+
+        # ── Build per-sequence keep-index lists ───────────────────────────────
+        prefix_len = 0
+        if remove_prefix:
+            for a, b in zip(toks1, toks2):
+                if a == b:
+                    prefix_len += 1
+                else:
+                    break
+
+        common_tok_ids: set = set()
+        if remove_common_toks:
+            common_tok_ids = set(toks1) & set(toks2)
+
+        def _build_keep(toks: list) -> list:
+            return [
+                i for i, t in enumerate(toks)
+                if not (skip_first and i == 0)
+                and i >= prefix_len
+                and t not in common_tok_ids
+            ]
+
+        keep1 = _build_keep(toks1)
+        keep2 = _build_keep(toks2)
+
+        # ── Capture hidden states for all layers in two forward passes ────────
+        hiddens1 = capture_all_hiddens(model, ids1, layers)
+        hiddens2 = capture_all_hiddens(model, ids2, layers)
+
+        # Decoded token strings for the HTML token display
+        tokens1_str = [tokenizer.decode([t]) for t in toks1]
+        tokens2_str = [tokenizer.decode([t]) for t in toks2]
+
+        # ── Per-layer feature activation-rate difference ──────────────────────
+        # Activation rate = fraction of kept positions where the feature fires
+        # (activation > 0).  Ranking by |rate1 − rate2| highlights features
+        # that are selectively active in one example but not the other.
+        # Load one SAE at a time to avoid OOM (each SAE is ~1-2 GB on GPU).
+        candidates = []   # (abs_diff, layer, feat_idx, rate1, rate2,
+                          #  acts1_per_pos, acts2_per_pos)
+        for layer in layers:
+            sae = get_sae(layer)
+
+            # Full per-position feature activations — stay on SAE_DEVICE for GPU math
+            feats1 = compute_sae_features(hiddens1[layer], sae, raw=raw_acts)  # [seq1_len, SAE_WIDTH]
+            feats2 = compute_sae_features(hiddens2[layer], sae, raw=raw_acts)  # [seq2_len, SAE_WIDTH]
+
+            # Activation rate = fraction of kept positions where feature fires (> 0)
+            def _rate(feats: torch.Tensor, keep_idx: list) -> torch.Tensor:
+                if not keep_idx:
+                    return torch.zeros(feats.shape[1], device=feats.device, dtype=feats.dtype)
+                return (feats[keep_idx] > 0).float().mean(dim=0)
+
+            r1   = _rate(feats1, keep1)
+            r2   = _rate(feats2, keep2)
+            diff = (r1 - r2).abs()
+
+            # Top-k per layer (all kept — no global trim)
+            local_k    = min(top_k, SAE_WIDTH)
+            vals, idxs = torch.topk(diff, local_k)
+            for v, fi in zip(vals.tolist(), idxs.tolist()):
+                # Round to 3 dp — enough precision for color interpolation
+                a1_pos = [round(x, 3) for x in feats1[:, fi].tolist()]
+                a2_pos = [round(x, 3) for x in feats2[:, fi].tolist()]
+                candidates.append((v, layer, fi, float(r1[fi]), float(r2[fi]),
+                                   a1_pos, a2_pos))
+
+            # Free SAE weights and feature tensors before loading the next layer
+            del sae, feats1, feats2, diff
+
+        # Single cache clear after all layers — calling it per-layer is expensive
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+        # ── Per-layer sort: group by layer, within each layer sort by diff desc ─
+        candidates.sort(key=lambda x: (x[1], -x[0]))
+        diff_lookup: dict = {}
+        for diff_val, layer, feat_idx, *_ in candidates:
+            key = (layer, feat_idx)
+            if key not in diff_lookup or diff_val > diff_lookup[key]:
+                diff_lookup[key] = diff_val
+        tok_html, table_html = compare_to_html(candidates, text1, text2, tokens1_str, tokens2_str)
+        return tok_html, table_html, diff_lookup
+
+    except gr.Error:
+        raise
+    except Exception as e:
+        raise gr.Error(f"Comparison failed: {e}")
+
+
+# ─── CSS ─────────────────────────────────────────────────────────────────────
+
+CSS = """
+/* ══════════════════════════════════════════════════════════════════
+   Color tokens — single source of truth for light / dark palettes
+   ══════════════════════════════════════════════════════════════════ */
+:root {
+    --c-page-bg:      #f4f6fb;
+    --c-card-bg:      #ffffff;
+    --c-card-border:  #e4e7ef;
+    --c-card-shadow:  0 1px 4px rgba(0,0,0,0.06), 0 4px 16px rgba(0,0,0,0.04);
+    --c-header-bg:    linear-gradient(135deg,#eff6ff 0%,#e0eaff 55%,#ede9fe 100%);
+    --c-header-border:#c7d2fe;
+    --c-header-text:  #1e293b;
+    --c-header-h1:    #1e3a8a;
+    --c-header-p:     #475569;
+    --c-pill-bg:      rgba(37,99,235,0.08);
+    --c-pill-border:  rgba(37,99,235,0.22);
+    --c-pill-text:    #1e3a8a;
+    --c-chip-bg:      #eff4ff;
+    --c-chip-text:    #2563eb;
+    --c-btn2-bg:      #f8faff;
+    --c-btn2-border:  #d0d7e8;
+    --c-btn2-text:    #374151;
+    --c-outbox-bg:    #fafbff;
+    --c-outbox-text:  #1e293b;
+    --c-outbox-border:#e4e7ef;
+    --c-tab-text:     #374151;
+    --c-tab-sel:      #2563eb;
+    --c-divider:      #dde3f0;
+    --c-th-bg:        #f0f4ff;
+    --c-th-text:      #2563eb;
+}
+
+/* Dark mode via OS/browser preference */
+@media (prefers-color-scheme: dark) {
+    :root {
+        --c-page-bg:      #0f172a;
+        --c-card-bg:      #1e293b;
+        --c-card-border:  #334155;
+        --c-card-shadow:  0 1px 4px rgba(0,0,0,0.40), 0 4px 16px rgba(0,0,0,0.25);
+        --c-header-bg:    linear-gradient(135deg,#172554 0%,#1e3a8a 55%,#3b0764 100%);
+        --c-header-border:#1e40af;
+        --c-header-text:  #e2e8f0;
+        --c-header-h1:    #bfdbfe;
+        --c-header-p:     #94a3b8;
+        --c-pill-bg:      rgba(96,165,250,0.12);
+        --c-pill-border:  rgba(96,165,250,0.30);
+        --c-pill-text:    #93c5fd;
+        --c-chip-bg:      #172554;
+        --c-chip-text:    #93c5fd;
+        --c-btn2-bg:      #1e293b;
+        --c-btn2-border:  #475569;
+        --c-btn2-text:    #e2e8f0;
+        --c-outbox-bg:    #0f172a;
+        --c-outbox-text:  #e2e8f0;
+        --c-outbox-border:#334155;
+        --c-tab-text:     #94a3b8;
+        --c-tab-sel:      #60a5fa;
+        --c-divider:      #334155;
+        --c-th-bg:        #172554;
+        --c-th-text:      #93c5fd;
+    }
+}
+
+/* Dark mode via Gradio's explicit dark-mode class (toggled manually) */
+.dark {
+    --c-page-bg:      #0f172a;
+    --c-card-bg:      #1e293b;
+    --c-card-border:  #334155;
+    --c-card-shadow:  0 1px 4px rgba(0,0,0,0.40), 0 4px 16px rgba(0,0,0,0.25);
+    --c-header-bg:    linear-gradient(135deg,#172554 0%,#1e3a8a 55%,#3b0764 100%);
+    --c-header-border:#1e40af;
+    --c-header-text:  #e2e8f0;
+    --c-header-h1:    #bfdbfe;
+    --c-header-p:     #94a3b8;
+    --c-pill-bg:      rgba(96,165,250,0.12);
+    --c-pill-border:  rgba(96,165,250,0.30);
+    --c-pill-text:    #93c5fd;
+    --c-chip-bg:      #172554;
+    --c-chip-text:    #93c5fd;
+    --c-btn2-bg:      #1e293b;
+    --c-btn2-border:  #475569;
+    --c-btn2-text:    #e2e8f0;
+    --c-outbox-bg:    #0f172a;
+    --c-outbox-text:  #e2e8f0;
+    --c-outbox-border:#334155;
+    --c-tab-text:     #94a3b8;
+    --c-tab-sel:      #60a5fa;
+    --c-divider:      #334155;
+    --c-th-bg:        #172554;
+    --c-th-text:      #93c5fd;
+}
+
+/* ── Page background ── */
+body, .gradio-container { background: var(--c-page-bg) !important; }
+
+/* ── Header card ── */
+.header-card {
+    background: var(--c-header-bg);
+    border-radius: 14px;
+    padding: 22px 28px 18px;
+    margin-bottom: 4px;
+    color: var(--c-header-text);
+    box-shadow: 0 4px 20px rgba(37,99,235,0.10);
+    border: 1px solid var(--c-header-border);
+}
+.header-card h1 { margin:0 0 6px; font-size:24px; font-weight:700; letter-spacing:-.3px; color:var(--c-header-h1); }
+.header-card p  { margin:0;       font-size:13px; color:var(--c-header-p); }
+.stat-pill {
+    display:inline-block;
+    background:var(--c-pill-bg);
+    border:1px solid var(--c-pill-border);
+    border-radius:20px;
+    padding:3px 13px;
+    font-size:12px;
+    color:var(--c-pill-text);
+    margin:4px 3px 0;
+}
+
+/* ── Panel cards ── */
+.panel-card {
+    background: var(--c-card-bg) !important;
+    border-radius: 12px !important;
+    box-shadow: var(--c-card-shadow) !important;
+    border: 1px solid var(--c-card-border) !important;
+    padding: 18px !important;
+}
+.panel-card > .form { gap: 12px !important; }
+
+/* ── Section label chips ── */
+.section-chip {
+    font-size: 11px;
+    font-weight: 700;
+    text-transform: uppercase;
+    letter-spacing: .8px;
+    color: var(--c-chip-text);
+    background: var(--c-chip-bg);
+    border-radius: 6px;
+    padding: 2px 10px;
+    display: inline-block;
+    margin-bottom: 10px;
+}
+
+/* ── Buttons ── */
+.btn-primary {
+    background: linear-gradient(135deg, #2563eb, #6d28d9) !important;
+    border: none !important;
+    border-radius: 8px !important;
+    font-weight: 600 !important;
+    font-size: 14px !important;
+    letter-spacing: .2px !important;
+    box-shadow: 0 2px 10px rgba(37,99,235,0.30) !important;
+    transition: all 0.18s ease !important;
+    color: #fff !important;
+    padding: 10px 0 !important;
+}
+.btn-primary:hover {
+    transform: translateY(-1px) !important;
+    box-shadow: 0 5px 18px rgba(37,99,235,0.40) !important;
+}
+.btn-secondary {
+    border-radius: 8px !important;
+    font-weight: 500 !important;
+    font-size: 13px !important;
+    border: 1px solid var(--c-btn2-border) !important;
+    background: var(--c-btn2-bg) !important;
+    color: var(--c-btn2-text) !important;
+    transition: all 0.15s ease !important;
+}
+.btn-secondary:hover {
+    background: var(--c-chip-bg) !important;
+    border-color: var(--c-tab-sel) !important;
+}
+
+/* ── Output boxes ── */
+.output-box textarea {
+    font-family: ui-monospace, monospace !important;
+    font-size: 13px !important;
+    line-height: 1.7 !important;
+    background: var(--c-outbox-bg) !important;
+    color: var(--c-outbox-text) !important;
+    border-color: var(--c-outbox-border) !important;
+    border-radius: 8px !important;
+}
+
+/* ── Dataframe ── */
+.feature-table table { font-family: ui-monospace, monospace; font-size: 13px; }
+.feature-table th    { background: var(--c-th-bg) !important; color: var(--c-th-text) !important;
+                        font-weight: 600; font-size: 12px; text-transform: uppercase; }
+
+/* ── Tab styling ── */
+.tab-nav button {
+    font-weight: 600 !important;
+    font-size: 14px !important;
+    border-radius: 8px 8px 0 0 !important;
+    color: var(--c-tab-text) !important;
+}
+.tab-nav button.selected {
+    color: var(--c-tab-sel) !important;
+    border-bottom: 2px solid var(--c-tab-sel) !important;
+}
+
+/* ── Divider ── */
+.section-divider {
+    border: none;
+    border-top: 1px dashed var(--c-divider);
+    margin: 6px 0 10px;
+}
+
+/* ── Slider label ── */
+label.svelte-1b6s6sv { font-size: 13px !important; font-weight: 500 !important; }
+"""
+
+# ─── Build the Gradio interface ───────────────────────────────────────────────
+
+with gr.Blocks(title="Qwen-Scope Feature Explorer") as demo:
+
+    # ── Header ────────────────────────────────────────────────────────────────
+    gr.HTML(
+        '<div class="header-card">'
+        '  <div style="display:flex;align-items:center;gap:8px;margin-bottom:6px;">'
+        '    <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/620760a26e3b7210c2ff1943/-s1gyJfvbE1RgO5iBeNOi.png" alt="Qwen Logo" style="height:24px;width:auto;">'
+        '    <h1 style="margin:0;">Qwen-Scope Feature Explorer</h1>'
+        '  </div>'
+        f'  <p>Interpret {MODEL_NAME_ANALYZING_NOW} via Sparse Autoencoders trained on each residual-stream layer from {MODEL_NAME_SAE_TRAINED_FROM}.</p>'
+        '  <div style="margin-top:10px;">'
+        f'    <span class="stat-pill">Model: {MODEL_NAME_ANALYZING_NOW}</span>'
+        f'    <span class="stat-pill">SAE trained from: {MODEL_NAME_SAE_TRAINED_FROM}</span>'
+        f'    <span class="stat-pill">Layers: {NUM_LAYERS}</span>'
+        f'    <span class="stat-pill">SAE width: {SAE_WIDTH:,}</span>'
+        f'    <span class="stat-pill">Top-k: {TOP_K}</span>'
+        f'    <span class="stat-pill">d_model: {D_MODEL}</span>'
+        '  </div>'
+        '</div>'
+    )
+
+    analysis_state = gr.State(None)   # (list[str] tokens, Tensor[seq, sae_width] features)
+    compare_diff_state = gr.State({})
+
+    with gr.Tabs(elem_classes="tab-nav"):
+
+        # ══════════════════════════════════════════════════════════════════════
+        # Tab 1 — Feature Comparison
+        # ══════════════════════════════════════════════════════════════════════
+        with gr.Tab("⚖️  Feature Comparison"):
+
+            with gr.Row(equal_height=False):
+
+                # ── Left column: inputs + settings + token preview ─────────────
+                with gr.Column(scale=2, min_width=300):
+
+                    with gr.Accordion("Examples", open=True) as t3_examples_accordion:
+                        with gr.Group(elem_classes="panel-card"):
+                            gr.HTML('<span class="section-chip">Examples</span>')
+                            t3_text1 = gr.Textbox(
+                                label="Example 1",
+                                lines=5,
+                                placeholder="Paste first text here…",
+                            )
+                            t3_text2 = gr.Textbox(
+                                label="Example 2",
+                                lines=5,
+                                placeholder="Paste second text here…",
+                            )
+
+                    with gr.Accordion("Comparison Settings", open=True) as t3_settings_accordion:
+                        with gr.Group(elem_classes="panel-card"):
+                            gr.HTML('<span class="section-chip">Comparison Settings</span>')
+                            with gr.Row():
+                                t3_layer_from = gr.Slider(
+                                    minimum=0, maximum=NUM_LAYERS - 1,
+                                    value=0, step=1,
+                                    label="Layer from",
+                                    scale=1,
+                                )
+                                t3_layer_to = gr.Slider(
+                                    minimum=0, maximum=NUM_LAYERS - 1,
+                                    value=NUM_LAYERS - 1, step=1,
+                                    label="Layer to",
+                                    scale=1,
+                                )
+                            t3_topk = gr.Number(
+                                value=5, precision=0,
+                                label="Top-K results",
+                                info="Number of (layer, feature) pairs to surface.",
+                            )
+                            with gr.Accordion("Advanced options", open=False):
+                                t3_skip_first = gr.Checkbox(
+                                    label="Exclude first token",
+                                    value=False,
+                                    info="Skip position 0 when computing mean activations.",
+                                )
+                                t3_remove_common_toks = gr.Checkbox(
+                                    label="Remove common tokens",
+                                    value=False,
+                                    info="Exclude positions whose token ID appears in both examples.",
+                                )
+                                t3_remove_prefix = gr.Checkbox(
+                                    label="Remove common prefix",
+                                    value=False,
+                                    info="Exclude the longest token-level prefix shared by both examples.",
+                                )
+                            t3_run = gr.Button(
+                                "⚖️  Compare Features",
+                                variant="primary",
+                                elem_classes="btn-primary",
+                            )
+
+                    with gr.Accordion("Features", open=True) as t3_features_accordion:
+                        with gr.Group(elem_classes="panel-card"):
+                            gr.HTML(
+                                '<span class="section-chip">Feature Comparison</span>'
+                                '<span style="font-size:12px;color:#888;margin-left:8px;">'
+                                'top-K features per layer · ranked by |rate(Ex1) − rate(Ex2)|'
+                                ' where rate = fraction of token positions where the feature fires · grouped by layer'
+                                '</span>'
+                            )
+                            t3_out = gr.HTML(
+                                value=(
+                                    '<div style="min-height:80px;display:flex;align-items:center;'
+                                    'justify-content:center;color:#bbb;font-size:13px;">'
+                                    'Enter two examples and click Compare.</div>'
+                                )
+                            )
+
+                # ── Right column: token activations ───────────────────────────
+                with gr.Column(scale=3, min_width=380):
+                    with gr.Group(elem_classes="panel-card"):
+                        gr.HTML(
+                            '<span class="section-chip">Token Activations</span>'
+                            '<span style="font-size:12px;color:#888;margin-left:8px;">'
+                            'hover a feature row on the left to highlight activations'
+                            '</span>'
+                        )
+                        t3_tok_html = gr.HTML(
+                            value=(
+                                '<div style="min-height:60px;display:flex;align-items:center;'
+                                'justify-content:center;color:#bbb;font-size:13px;padding:8px;">'
+                                'Run Compare to see token activations here.</div>'
+                            )
+                        )
+
+        # ══════════════════════════════════════════════════════════════════════
+        # Tab 2 — Feature Steering
+        # ══════════════════════════════════════════════════════════════════════
+        with gr.Tab("🎛️  Feature Steering"):
+
+            with gr.Row(equal_height=False):
+
+                # ── Left column: prompt + steering controls ────────────────
+                with gr.Column(scale=2, min_width=280):
+                    with gr.Group(elem_classes="panel-card"):
+                        gr.HTML('<span class="section-chip">Prompt</span>')
+                        t2_prompt = gr.Textbox(
+                            label=None,
+                            lines=5,
+                            placeholder="Enter a generation prompt…",
+                            show_label=False,
+                        )
+
+                        gr.HTML('<span class="section-chip">Token Position Preview</span>'
+                                '<span style="font-size:12px;color:#888;margin-left:8px;">'
+                                'amber = steered &nbsp;·&nbsp; updates as you type'
+                                '</span>')
+
+                        t2_pos_preview = gr.HTML(
+                            value=(
+                                '<div style="padding:10px;color:#bbb;font-size:13px;">'
+                                'Enter a prompt above to preview steered positions.</div>'
+                            )
+                        )
+
+                    with gr.Group(elem_classes="panel-card"):
+                        gr.HTML('<span class="section-chip">Steering Parameters</span>')
+
+                        with gr.Row():
+                            t2_layer = gr.Slider(
+                                minimum=0, maximum=NUM_LAYERS - 1,
+                                value=10, step=1,
+                                label="Layer",
+                                scale=3,
+                            )
+                            t2_feat = gr.Number(
+                                value=0, precision=0,
+                                label="Feature index",
+                                info=f"0 – {SAE_WIDTH - 1}",
+                                scale=2,
+                            )
+
+                        t2_pos = gr.Textbox(
+                            label="Token positions to steer",
+                            value="all",
+                            placeholder="all  |  0,1,5  |  3-7  |  0,2,5-8",
+                            info=(
+                                "all → every token  |  "
+                                "0,1,5 → individual positions  |  "
+                                "3-7 → inclusive range  |  "
+                                "combinations e.g. 0,2,5-8"
+                            ),
+                        )
+                        t2_steer_output_only = gr.Checkbox(
+                            label="Also steer generated tokens",
+                            value=True,
+                            info=(
+                                "When enabled, every generated token is steered in addition to "
+                                "whatever the positions field specifies for the prompt."
+                            ),
+                        )
+
+                        gr.HTML('<span class="section-chip">Steering Strength</span>')
+                        t2_steer_mode = gr.Radio(
+                            choices=["Light", "Medium", "Strong", "Custom"],
+                            value="Light",
+                            label=None,
+                            show_label=False,
+                            info=(
+                                "Calibrated to the most different feature found in "
+                                "Feature Comparison. Run that tab first."
+                            ),
+                        )
+                        t2_custom_strength = gr.Number(
+                            value=5.0,
+                            label="Custom strength",
+                            info="Direct steering magnitude (used when Custom is selected above).",
+                            visible=False,
+                            precision=2,
+                        )
+                        t2_steer_info = gr.HTML(
+                            value=(
+                                '<div style="font-size:11px;color:#888;padding:4px 6px;'
+                                'background:#f8faff;border-radius:5px;">'
+                                'Light ≈ 5.0 · Medium ≈ 20.0 · Strong ≈ 100.0<br>'
+                                '<span style="color:#bbb;">Run Feature Comparison to calibrate.</span>'
+                                '</div>'
+                            )
+                        )
+
+                        gr.HTML('<hr class="section-divider">')
+                        with gr.Accordion("Sampling options", open=False):
+                            t2_maxtok = gr.Slider(
+                                minimum=20, maximum=300,
+                                value=100, step=10,
+                                label="Max new tokens",
+                            )
+                            t2_greedy = gr.Checkbox(
+                                label="Greedy decoding",
+                                value=True,
+                                info="When enabled, all sampling parameters below are ignored.",
+                            )
+                            with gr.Row():
+                                t2_temperature = gr.Slider(
+                                    minimum=0.01, maximum=2.0,
+                                    value=GEN_TEMPERATURE, step=0.01,
+                                    label="Temperature",
+                                    interactive=GEN_DO_SAMPLE,
+                                )
+                                t2_top_p = gr.Slider(
+                                    minimum=0.0, maximum=1.0,
+                                    value=GEN_TOP_P, step=0.01,
+                                    label="Top-p (nucleus)",
+                                    interactive=GEN_DO_SAMPLE,
+                                )
+                            with gr.Row():
+                                t2_top_k_tok = gr.Slider(
+                                    minimum=0, maximum=200,
+                                    value=GEN_TOP_K, step=1,
+                                    label="Top-k (tokens)",
+                                    info="0 = disabled",
+                                    interactive=GEN_DO_SAMPLE,
+                                )
+                                t2_rep_penalty = gr.Slider(
+                                    minimum=1.0, maximum=3.0,
+                                    value=GEN_REP_PENALTY, step=0.05,
+                                    label="Repetition penalty",
+                                    info="1.0 = no penalty",
+                                    interactive=GEN_DO_SAMPLE,
+                                )
+
+                        t2_run = gr.Button(
+                            "▶  Generate Both Outputs",
+                            variant="primary",
+                            elem_classes="btn-primary",
+                        )
+
+                # ── Right column: outputs ──────────────────────────────────
+                with gr.Column(scale=3, min_width=380):
+
+                    with gr.Group(elem_classes="panel-card"):
+                        gr.HTML(
+                            '<span class="section-chip">Original Output</span>'
+                            '<span style="font-size:12px;color:#888;margin-left:8px;">'
+                            'No steering applied</span>'
+                        )
+                        t2_orig = gr.Textbox(
+                            label=None, lines=7,
+                            interactive=False,
+                            show_label=False,
+                            placeholder="Original generation will appear here…",
+                            elem_classes="output-box",
+                        )
+                        gr.HTML(
+                            '<span class="section-chip" style="margin-top:10px;'
+                            'display:inline-block;">Token Probabilities</span>'
+                            '<span style="font-size:12px;color:#888;margin-left:8px;">'
+                            'blue intensity = confidence &nbsp;·&nbsp; hover = top-k</span>'
+                        )
+                        t2_orig_probs = gr.HTML(
+                            value='<div style="padding:10px;color:#bbb;font-size:13px;">'
+                                  'Run generation to see token probabilities.</div>'
+                        )
+
+                    with gr.Group(elem_classes="panel-card"):
+                        gr.HTML(
+                            '<span class="section-chip" style="background:#fef3f2;color:#dc2626;">'
+                            'Steered Output</span>'
+                            '<span style="font-size:12px;color:#888;margin-left:8px;">'
+                            'With SAE feature injection</span>'
+                        )
+                        t2_steered = gr.Textbox(
+                            label=None, lines=7,
+                            interactive=False,
+                            show_label=False,
+                            placeholder="Steered generation will appear here…",
+                            elem_classes="output-box",
+                        )
+                        gr.HTML(
+                            '<span class="section-chip" style="background:#fef3f2;color:#dc2626;'
+                            'margin-top:10px;display:inline-block;">Token Probabilities</span>'
+                            '<span style="font-size:12px;color:#888;margin-left:8px;">'
+                            'red intensity = confidence &nbsp;·&nbsp; hover = top-k</span>'
+                        )
+                        t2_steer_probs = gr.HTML(
+                            value='<div style="padding:10px;color:#bbb;font-size:13px;">'
+                                  'Run generation to see token probabilities.</div>'
+                        )
+
+            t2_run.click(
+                cb_generate,
+                inputs=[t2_prompt, t2_layer, t2_feat, t2_pos, t2_steer_mode, compare_diff_state,
+                        t2_steer_output_only, t2_maxtok,
+                        t2_greedy, t2_top_k_tok, t2_top_p, t2_rep_penalty,
+                        t2_temperature, t2_custom_strength],
+                outputs=[t2_orig, t2_steered, t2_orig_probs, t2_steer_probs],
+            )
+            t3_run.click(
+                cb_compare,
+                inputs=[t3_text1, t3_text2, t3_layer_from, t3_layer_to, t3_topk,
+                        t3_skip_first, t3_remove_common_toks, t3_remove_prefix],
+                outputs=[t3_tok_html, t3_out, compare_diff_state],
+            ).then(
+                fn=lambda: [gr.update(open=False), gr.update(open=False)],
+                inputs=None,
+                outputs=[t3_examples_accordion, t3_settings_accordion],
+            )
+            _sampling_controls = [
+                t2_temperature, t2_top_p, t2_top_k_tok, t2_rep_penalty
+            ]
+            t2_greedy.change(
+                fn=lambda g: [gr.update(interactive=not g)] * 4,
+                inputs=[t2_greedy],
+                outputs=_sampling_controls,
+            )
+            t2_prompt.change(
+                cb_update_steer_preview,
+                inputs=[t2_prompt, t2_pos],
+                outputs=[t2_pos_preview],
+            )
+            t2_pos.change(
+                cb_update_steer_preview,
+                inputs=[t2_prompt, t2_pos],
+                outputs=[t2_pos_preview],
+            )
+
+            def _update_steer_info(mode: str, diff_lookup, layer, feat_idx):
+                if mode == "Custom":
+                    return (
+                        '<div style="font-size:11px;color:#555;padding:4px 6px;'
+                        'background:#f8faff;border-radius:5px;">'
+                        'Enter a custom steering strength value above.'
+                        '</div>'
+                    )
+                d = 0.0
+                source_note = '<span style="color:#bbb;">Run Feature Comparison to calibrate.</span>'
+                if diff_lookup and isinstance(diff_lookup, dict):
+                    key = (int(layer), int(feat_idx))
+                    if key in diff_lookup:
+                        d = float(diff_lookup[key])
+                        source_note = (
+                            f'<span style="color:#16a34a;">feature #{int(feat_idx)} '
+                            f'@ layer {int(layer)} · diff = {d:.3f}</span>'
+                        )
+                    else:
+                        d = float(max(diff_lookup.values(), default=0.0))
+                        source_note = (
+                            f'<span style="color:#64748b;">feature not in compare results — '
+                            f'using global max diff = {d:.3f}</span>'
+                        )
+                if d <= 0:
+                    vals = {"Light": 5.0, "Medium": 20.0, "Strong": 100.0}
+                else:
+                    vals = {
+                        "Light":  round(d * 0.5, 2),
+                        "Medium": round(d * 2.0, 2),
+                        "Strong": round(d * 10.0, 2),
+                    }
+                return (
+                    f'<div style="font-size:11px;color:#555;padding:4px 6px;'
+                    f'background:#f8faff;border-radius:5px;">'
+                    f'Light ≈ {vals["Light"]} · Medium ≈ {vals["Medium"]} · Strong ≈ {vals["Strong"]}<br>'
+                    + source_note + '</div>'
+                )
+
+            _steer_info_inputs = [t2_steer_mode, compare_diff_state, t2_layer, t2_feat]
+            for _trigger in [t2_steer_mode.change, compare_diff_state.change,
+                             t2_layer.change, t2_feat.change]:
+                _trigger(
+                    fn=_update_steer_info,
+                    inputs=_steer_info_inputs,
+                    outputs=[t2_steer_info],
+                )
+
+            # Show/hide custom strength input depending on radio selection
+            t2_steer_mode.change(
+                fn=lambda m: gr.update(visible=(m == "Custom")),
+                inputs=[t2_steer_mode],
+                outputs=[t2_custom_strength],
+            )
+
+
+if __name__ == '__main__':
+    # Pre-load model onto GPU before accepting requests, so the first
+    # button click doesn't stall waiting for a 30 B-param model to load.
+    print("Pre-loading model onto GPU…")
+    get_model()
+    print("Model ready. Starting Gradio server…")
+    demo.queue(max_size=4)
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=PORT,
+        # share=True creates a public gradio.live URL that bypasses the
+        # Alibaba Cloud DSW gateway (which blocks POST API requests).
+        # The URL printed below is valid for 72 h.
+        share=True,
+        strict_cors=False,
+        show_error=True,
+        ssr_mode=False,
+        theme=gr.themes.Soft(),
+        css=CSS,
+    )

layer0.sae.pt

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layer1.sae.pt

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layer10.sae.pt

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layer11.sae.pt

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