Updated to new versions

.gitignore

@@ -61,4 +61,6 @@ artifacts/
 # Tmp to avoid pushing things I'm testing
 __marimo__/
 AGENTS.md
+CLAUDE.md
+todo/
 # notebook_marimo.py

README.md

@@ -21,7 +21,7 @@ A web app built on top of [persona-vectors](../persona-vectors) that provides th
 
 - **Chat** — interactive conversations with a model using persona-based system prompts (templated or biography)
 - **Compare** — load saved activations and explore layer-wise cosine similarity, PCA, and UMAP projections
-- **Extract** — run activation extraction from HuggingFace or a local JSONL dataset directly from the browser
+- **Extract** — run activation extraction from HuggingFace persona datasets or a local JSONL dataset directly from the browser
 
 ## Repository Layout
 

pyproject.toml

@@ -5,8 +5,8 @@ description = "Streamlit UI for persona-vectors"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "persona-vectors>=0.2.1",
-    "persona-data>=0.1.0",
+    "persona-vectors>=0.3.0",
+    "persona-data>=0.2.0",
     "streamlit>=1.44.0",
     "plotly>=6.6.0",
     "python-dotenv>=1.2.2",
@@ -16,7 +16,7 @@ dependencies = [
 # Local development:
 # [tool.uv.sources]
 # persona-vectors = { path = "../persona-vectors", editable = true }
-# persona-vectors = { path = "../persona-data", editable = true }
+# persona-data = { path = "../persona-data", editable = true }
 
 # [build-system]
 # requires = ["uv_build>=0.11.3,<0.12"]

tabs/chat.py

@@ -133,6 +133,7 @@ def _render_editable_message(
     msg_col, edit_col = st.columns(
         list(column_ratio), gap="xsmall", vertical_alignment="center"
     )
+
     with msg_col:
         with st.chat_message(role):
             if tc is not None:
@@ -261,7 +262,7 @@ def _render_generation_settings(context_key: str, remote: bool) -> tuple[dict, b
     """Render the Advanced generation settings expander.
 
     Returns ``(gen_kwargs, advanced_generation)`` where ``advanced_generation``
-    is True when any setting differs from its default.
+    is True when any generation setting differs from its default.
     """
     with st.expander("Advanced", expanded=False):
         config_col1, config_col2 = st.columns([2, 1])

tabs/compare_chat.py

@@ -64,6 +64,14 @@ def render_compare_mode(
     advanced_generation: bool,
 ) -> None:
     """Render the full side-by-side comparison UI."""
+    model: StandardizedTransformer | None = None
+
+    def _get_model() -> StandardizedTransformer:
+        nonlocal model
+        if model is None:
+            model = cached_model(model_name=model_name, remote=remote)
+        return model
+
     contrast_key = widget_key(context_key, "token_contrast")
     contrast_enabled = st.toggle(
         "Token contrast",
@@ -176,7 +184,7 @@ def render_compare_mode(
         if st.session_state.pop(p_pending, False)
     ]
     if regen_panels:
-        model = cached_model(model_name=model_name, remote=remote)
+        model = _get_model()
 
         results: list[ChatReply | Exception] = []
         with st.spinner("Regenerating..."):
@@ -219,7 +227,7 @@ def render_compare_mode(
             if msg.get("_needs_contrast") and msg.get("role") == "assistant"
         ]
         if pending_edits:
-            model = cached_model(model_name=model_name, remote=remote)
+            model = _get_model()
             label_a = persona_label(left_persona)
             label_b = persona_label(right_persona)
             with st.spinner("Recomputing token contrast…"):

tabs/extract.py

@@ -41,53 +41,74 @@ def _extract_widget_key(
 _TOKEN_LEGEND = (
     '<div style="display:flex;gap:12px;flex-wrap:wrap;font-size:0.8em;margin-bottom:8px">'
     '<span style="background:#86efac;color:black;padding:1px 6px;border-radius:3px">masked</span>'
+    '<span style="color:#fde047;padding:1px 6px">question</span>'
     '<span style="color:#22d3ee;padding:1px 6px">response</span>'
     '<span style="color:#d946ef;font-weight:bold;padding:1px 6px">special</span>'
-    '<span style="color:#9ca3af;padding:1px 6px">prompt</span>'
+    '<span style="color:#9ca3af;padding:1px 6px">template</span>'
     "</div>"
 )
 
 _MAX_PREVIEW_SAMPLES = 3
 
 
+def _token_style_for_index(
+    p: PreparedInput, token_idx: int, special_ids: set[int]
+) -> str:
+    if p.spans.response.token_start <= token_idx < p.spans.response.token_end:
+        style = "color:#22d3ee"
+    elif p.spans.question.token_start <= token_idx < p.spans.question.token_end:
+        style = "color:#fde047"
+    elif p.spans.template.token_start <= token_idx < p.spans.template.token_end:
+        style = "color:#9ca3af"
+    else:
+        style = "color:#9ca3af"
+
+    if int(p.input_ids[token_idx]) in special_ids:
+        style = "color:#d946ef;font-weight:bold"
+    if p.token_mask[token_idx]:
+        style = f"{style};background:#86efac;border-radius:2px;padding:0 1px"
+    return style
+
+
 def _render_sample_tokens_html(
     p: PreparedInput, tokenizer, *, max_tokens: int = 200
 ) -> str:
-    """Build an HTML token sequence for a single PreparedInput."""
+    """Build an HTML token sequence using the persona-vectors preview layout."""
     special_ids = set(tokenizer.all_special_ids)
-    ids = p.input_ids.tolist()
-    tokens = tokenizer.convert_ids_to_tokens(ids)
-
-    if len(ids) > max_tokens:
-        head = max_tokens // 2
-        tail = max_tokens - head
-        indices: list[int | None] = (
-            list(range(head)) + [None] + list(range(len(ids) - tail, len(ids)))
-        )
-    else:
-        indices = list(range(len(ids)))
+    seq_len = int(p.input_ids.shape[0])
+    head = max_tokens if max_tokens > 0 else seq_len
+    tail = 8 if max_tokens <= 0 else max(8, max_tokens // 4)
+    answer_extra = 8 if max_tokens <= 0 else max(8, max_tokens // 4)
+
+    prefix_end = min(p.spans.template.token_start + head, seq_len)
+    tail_start = min(max(prefix_end, p.spans.template.token_end - tail), seq_len)
+    answer_end = min(seq_len, p.spans.response.token_end + answer_extra)
+
+    indices: list[int | None] = list(range(0, prefix_end))
+    if prefix_end < tail_start:
+        indices.append(None)
+    indices.extend(range(tail_start, answer_end))
+    if answer_end < seq_len:
+        indices.append(None)
 
     spans: list[str] = []
     for idx in indices:
         if idx is None:
             spans.append('<span style="color:#9ca3af"> … </span>')
             continue
-        raw = tokens[idx].replace("▁", " ").replace("Ċ", "\n")
+        char_start, char_end = p.offset_mapping[idx]
+        raw = p.prompt_text[char_start:char_end]
+        if not raw:
+            raw = tokenizer.convert_ids_to_tokens([int(p.input_ids[idx])])[0]
         escaped = html.escape(raw)
-        if p.token_mask[idx]:
-            style = "background:#86efac;color:black;border-radius:2px"
-        elif ids[idx] in special_ids:
-            style = "color:#d946ef;font-weight:bold"
-        elif idx >= p.answer_start:
-            style = "color:#22d3ee"
-        else:
-            style = "color:#9ca3af"
-        spans.append(f'<span style="{style}">{escaped}</span>')
+        spans.append(
+            f'<span style="{_token_style_for_index(p, idx, special_ids)}">{escaped}</span>'
+        )
 
     return (
-        f'<pre style="white-space:pre-wrap;font-size:0.82em;line-height:1.5;'
-        f"background:#0e1117;padding:8px 10px;border-radius:6px;"
-        f'border:1px solid #333;margin:0">'
+        '<pre style="white-space:pre-wrap;font-size:0.82em;line-height:1.5;'
+        "background:#0e1117;padding:8px 10px;border-radius:6px;"
+        'border:1px solid #333;margin:0">'
         f"{''.join(spans)}</pre>"
     )
 
@@ -148,6 +169,10 @@ def render_extract_tab(remote: bool, model_name: str, dataset_source: str) -> No
         )
         return
 
+    if not getattr(dataset, "supports_qa", True):
+        st.info("This dataset is persona-only for now. Use Chat to browse personas.")
+        return
+
     personas = list(dataset)
     if not personas:
         st.warning("No personas found in the selected dataset.")
@@ -218,7 +243,7 @@ def render_extract_tab(remote: bool, model_name: str, dataset_source: str) -> No
 
         st.caption("Extraction settings")
         last_strategy = st.session_state.get(
-            _LAST_MASK_STRATEGY_KEY, MaskStrategy.RESPONSE_MEAN.value
+            _LAST_MASK_STRATEGY_KEY, MaskStrategy.ANSWER_MEAN.value
         )
         strategy_options = list(MaskStrategy)
         strategy_index = next(
@@ -284,9 +309,12 @@ def render_extract_tab(remote: bool, model_name: str, dataset_source: str) -> No
         st.markdown(_TOKEN_LEGEND, unsafe_allow_html=True)
         for persona, qa_pairs in runs:
             for variant in selected_variants:
-                system_prompt = format_roleplay_prompt(
-                    getattr(persona, f"{variant}_view"), mode="mc"
-                )
+                if variant == "baseline":
+                    system_prompt = format_roleplay_prompt()
+                else:
+                    system_prompt = format_roleplay_prompt(
+                        getattr(persona, f"{variant}_view")
+                    )
                 prepared = prepare_inputs(
                     tokenizer=model.tokenizer,
                     system_prompt=system_prompt,

utils/chat_export.py

@@ -1,4 +1,5 @@
 import json
+from dataclasses import asdict, is_dataclass
 from datetime import datetime, timezone
 from pathlib import Path
 
@@ -7,6 +8,19 @@ from persona_data.environment import get_artifacts_dir
 from utils.helpers import slugify
 
 
+def _serialise_message(message: dict[str, object]) -> dict[str, object]:
+    payload: dict[str, object] = {
+        "role": message["role"],
+        "content": message["content"],
+    }
+    for key in ("_contrast",):
+        value = message.get(key)
+        if value is None:
+            continue
+        payload[key] = asdict(value) if is_dataclass(value) else value
+    return payload
+
+
 def save_chat_export(
     *,
     model_name: str,
@@ -49,7 +63,7 @@ def save_chat_export(
         "messages": (
             [{"role": "system", "content": system_prompt}] if system_prompt else []
         )
-        + messages,
+        + [_serialise_message(message) for message in messages],
     }
 
     export_dir = (

utils/datasets.py

@@ -6,6 +6,10 @@ from tempfile import mkdtemp
 from typing import Any
 
 import streamlit as st
+from persona_data.nemotron_personas import (
+    NemotronPersonasFranceDataset,
+    NemotronPersonasUSADataset,
+)
 from persona_data.synth_persona import PersonaDataset as LocalPersonaDataset
 from persona_data.synth_persona import SynthPersonaDataset
 
@@ -19,6 +23,20 @@ def cached_hf_dataset() -> SynthPersonaDataset:
     return SynthPersonaDataset()
 
 
+@st.cache_resource(show_spinner=False)
+def cached_nemotron_dataset() -> NemotronPersonasFranceDataset:
+    """Load the Nemotron France HuggingFace dataset once."""
+
+    return NemotronPersonasFranceDataset()
+
+
+@st.cache_resource(show_spinner=False)
+def cached_nemotron_usa_dataset() -> NemotronPersonasUSADataset:
+    """Load the Nemotron USA HuggingFace dataset once."""
+
+    return NemotronPersonasUSADataset()
+
+
 def _upload_cache_dir() -> Path:
     cache_dir = st.session_state.get("_upload_cache_dir")
     if cache_dir is None:
@@ -46,12 +64,24 @@ def load_dataset(
     dataset_source: str,
     personas_file: Any = None,
     qa_file: Any = None,
-) -> tuple[SynthPersonaDataset | LocalPersonaDataset, str]:
+) -> tuple[
+    SynthPersonaDataset
+    | NemotronPersonasFranceDataset
+    | NemotronPersonasUSADataset
+    | LocalPersonaDataset,
+    str,
+]:
     """Load the selected dataset source for the UI."""
 
     if dataset_source == DATASET_SOURCES[0]:
         return cached_hf_dataset(), "SynthPersona"
 
+    if dataset_source == DATASET_SOURCES[1]:
+        return cached_nemotron_dataset(), "Nemotron France"
+
+    if dataset_source == DATASET_SOURCES[2]:
+        return cached_nemotron_usa_dataset(), "Nemotron USA"
+
     if personas_file is None or qa_file is None:
         raise ValueError("Upload both personas.jsonl and qa.jsonl files")
 

utils/helpers.py

@@ -16,7 +16,12 @@ MODE_LABELS = list(VARIANT_LABELS.values())
 # Reverse lookup: label -> key
 MODE_LABEL_TO_KEY = {v: k for k, v in VARIANT_LABELS.items()}
 
-DATASET_SOURCES = ["HuggingFace: synth-persona", "Local JSONL upload"]
+DATASET_SOURCES = [
+    "HuggingFace: synth-persona",
+    "HuggingFace: nemotron-france",
+    "HuggingFace: nemotron-usa",
+    "Local JSONL upload",
+]
 ANALYSIS_MODES = ["Cosine similarity", "PCA", "UMAP"]
 
 ANALYSIS_HELP_TEXT = {

uv.lock

@@ -274,10 +274,10 @@ wheels = [
 
 [[package]]
 name = "cuda-pathfinder"
-version = "1.5.2"
+version = "1.5.3"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/d3/d6/ac63065d33dd700fee7ebd7d287332401b54e31b9346e142f871e1f0b116/cuda_pathfinder-1.5.3-py3-none-any.whl", hash = "sha256:dff021123aedbb4117cc7ec81717bbfe198fb4e8b5f1ee57e0e084fec5c8577d", size = 49991, upload-time = "2026-04-14T20:09:27.037Z" },
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@@ -454,7 +454,7 @@ wheels = [
 
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-version = "1.10.1"
+version = "1.11.0"
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@@ -467,9 +467,9 @@ dependencies = [
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+version = "0.2.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
     { name = "numpy" },
+    { name = "pyarrow" },
     { name = "python-dotenv" },
     { name = "torch" },
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