adds actually more interface improvements, language toggles , cpu fallbacks

README.md

@@ -64,5 +64,5 @@ python download_bundles.py --inspect-only --infer   # inspect existing downloads
 
 ## Deployment
 
-- **Hugging Face Spaces:** Set hardware to **Zero GPU** (or standard GPU). The app uses `@spaces.GPU` when available.
+- **Hugging Face Spaces:** Set hardware to **Zero GPU** (or standard GPU). The app uses `@spaces.GPU` when available. If Zero GPU workers fail with *"No CUDA GPUs are available"* (e.g. no free GPU in the pool), set the **CPU_FALLBACK** secret or environment variable to `1` so inference runs on CPU in-process instead of in GPU workers.
 - **Local:** Run `python app.py`; requires a GPU with ~10 GB VRAM for all 6 models.

app.py

@@ -7,58 +7,29 @@ import gradio as gr
 
 from inference import BAGUETTOTRON_ID, run_all
 from model_config import (
-    TIER_LABELS,
     combined_footprint,
     footprint_table_data,
     get_models_by_tier,
     MODELS,
 )
-from ui_strings import (
-    BTN_GENERATE,
-    COL_BAGUETTOTRON_HEADING,
-    COL_LUTH_HEADING,
-    FOOTPRINT_HEADERS,
-    FOOTPRINT_INTRO,
-    FOOTPRINT_SUMMARY_TEMPLATE,
-    HEADING_FOOTPRINT,
-    HEADING_GENERATION,
-    HEADING_HOW_TO_USE,
-    HEADING_LIVE_INFERENCE,
-    HOW_TO_USE,
-    INFO_REP_LUTH,
-    INFO_TEMP_BAGUETTOTRON,
-    JOIN_US,
-    LABEL_MAX_TOKENS,
-    LABEL_OUT_BAGUETTOTRON,
-    LABEL_OUT_LUTH_06,
-    LABEL_OUT_LUTH_07,
-    LABEL_OUT_LUTH_12,
-    LABEL_OUT_LUTH_17,
-    LABEL_OUT_LUTH_350,
-    LABEL_PROMPT,
-    LABEL_REPEAT_PENALTY,
-    LABEL_SYSTEM_PROMPT,
-    LABEL_TEMPERATURE,
-    LABEL_TOP_K,
-    LABEL_TOP_P,
-    PLACEHOLDER_PROMPT,
-    PLACEHOLDER_SYSTEM_PROMPT,
-    SUBTITLE,
-    TITLE,
-)
+from ui_strings import get_strings
 
 # Example run once on startup and cached for initial display (no system prompt)
 STARTUP_EXAMPLE_PROMPT = "dites moi en plus sur les jardins japonnais a paris :"
 STARTUP_EXAMPLE_SYSTEM = ""
 
-# Optional: use @spaces.GPU for ZeroGPU deployment
+# Optional: use @spaces.GPU for ZeroGPU deployment.
+# Set CPU_FALLBACK=1 (or any non-empty value) to skip ZeroGPU and run inference on CPU
+# when no GPU is available (avoids "RuntimeError: No CUDA GPUs are available" in workers).
+import os
+
 try:
     import spaces
 
-    GPU_DECORATOR = spaces.GPU
+    _use_cpu_fallback = os.environ.get("CPU_FALLBACK", "").strip().lower() in ("1", "true", "yes")
+    GPU_DECORATOR = (lambda f: f) if _use_cpu_fallback else spaces.GPU
 except ImportError:
     GPU_DECORATOR = lambda f: f  # no-op when not on Spaces
-# remove the lines above when using this code
 
 def build_params_by_model(
     temp_baguettotron: float,
@@ -136,9 +107,54 @@ def generate_all(
     return tuple(outputs)
 
 
+def _ui_updates_for_locale(locale: str) -> tuple:
+    """Return (title, subtitle, heading_footprint, ...) for language toggle; order must match _LANG_OUTPUTS."""
+    s = get_strings(locale)
+    total_disk, total_vram = combined_footprint()
+    footprint_summary = s["FOOTPRINT_SUMMARY_TEMPLATE"].format(total_disk=total_disk, total_vram=total_vram)
+    return (
+        f"# {s['TITLE']}",
+        s["SUBTITLE"],
+        s["HEADING_FOOTPRINT"],
+        s["FOOTPRINT_INTRO"],
+        gr.update(headers=s["FOOTPRINT_HEADERS"]),
+        footprint_summary,
+        s["HEADING_GENERATION"],
+        s["COL_BAGUETTOTRON_HEADING"],
+        s["COL_LUTH_HEADING"],
+        gr.update(label=s["LABEL_TEMPERATURE"], info=s["INFO_TEMP_BAGUETTOTRON"]),
+        gr.update(label=s["LABEL_MAX_TOKENS"]),
+        gr.update(label=s["LABEL_TOP_P"]),
+        gr.update(label=s["LABEL_TOP_K"]),
+        gr.update(label=s["LABEL_REPEAT_PENALTY"]),
+        gr.update(label=s["LABEL_TEMPERATURE"]),
+        gr.update(label=s["LABEL_MAX_TOKENS"]),
+        gr.update(label=s["LABEL_TOP_P"]),
+        gr.update(label=s["LABEL_TOP_K"]),
+        gr.update(label=s["LABEL_REPEAT_PENALTY"], info=s["INFO_REP_LUTH"]),
+        s["HEADING_LIVE_INFERENCE"],
+        gr.update(label=s["TIER_SMALL"]),
+        gr.update(label=s["TIER_MEDIUM"]),
+        gr.update(label=s["TIER_LARGE"]),
+        gr.update(label=s["LABEL_OUT_BAGUETTOTRON"]),
+        gr.update(label=s["LABEL_OUT_LUTH_350"]),
+        gr.update(label=s["LABEL_OUT_LUTH_06"]),
+        gr.update(label=s["LABEL_OUT_LUTH_07"]),
+        gr.update(label=s["LABEL_OUT_LUTH_12"]),
+        gr.update(label=s["LABEL_OUT_LUTH_17"]),
+        gr.update(label=s["LABEL_SYSTEM_PROMPT"], placeholder=s["PLACEHOLDER_SYSTEM_PROMPT"]),
+        gr.update(label=s["LABEL_PROMPT"], placeholder=s["PLACEHOLDER_PROMPT"]),
+        gr.update(value=s["BTN_GENERATE"]),
+        s["HEADING_HOW_TO_USE"],
+        s["HOW_TO_USE"],
+        s["JOIN_US"],
+    )
+
+
 def create_ui():
+    s = get_strings("en")
     total_disk, total_vram = combined_footprint()
-    footprint_md = FOOTPRINT_SUMMARY_TEMPLATE.format(total_disk=total_disk, total_vram=total_vram)
+    footprint_md = s["FOOTPRINT_SUMMARY_TEMPLATE"].format(total_disk=total_disk, total_vram=total_vram)
 
     # Run startup example once and cache for initial output display
     default_params = build_params_by_model(
@@ -156,97 +172,112 @@ def create_ui():
         for m in models_by_tier[tier]:
             startup_outputs.append(startup_results.get(m.repo_id, ""))
 
-    with gr.Blocks(title=TITLE) as demo:
-        gr.Markdown(f"# {TITLE}")
-        gr.Markdown(SUBTITLE)
+    with gr.Blocks(title=s["TITLE"]) as demo:
+        # Language (🇫🇷 / 🇺🇸) and theme (light / dark) toggles
+        with gr.Row(elem_classes="toggle-row"):
+            lang_radio = gr.Radio(
+                choices=[("🇺🇸 English", "en"), ("🇫🇷 Français", "fr")],
+                value="en",
+                label="Language",
+                scale=0,
+                min_width=180,
+            )
+            theme_btn = gr.Button("🌙 Dark mode", variant="secondary", scale=0, min_width=120)
+            theme_btn.click(
+                None,
+                js="() => { document.body.classList.toggle('dark'); }",
+            )
+
+        title_md = gr.Markdown(f"# {s['TITLE']}")
+        subtitle_md = gr.Markdown(s["SUBTITLE"])
 
         # Row 1: Single consolidated comparison table
-        gr.Markdown(HEADING_FOOTPRINT)
-        gr.Markdown(FOOTPRINT_INTRO)
+        heading_footprint_md = gr.Markdown(s["HEADING_FOOTPRINT"])
+        footprint_intro_md = gr.Markdown(s["FOOTPRINT_INTRO"])
         footprint_df = gr.Dataframe(
             value=footprint_table_data(),
-            headers=FOOTPRINT_HEADERS,
+            headers=s["FOOTPRINT_HEADERS"],
             interactive=False,
         )
-        gr.Markdown(footprint_md)
+        footprint_summary_md = gr.Markdown(footprint_md)
 
         # Row 2: Generation settings — two columns (Baguettotron | Luth)
-        gr.Markdown(HEADING_GENERATION)
+        heading_generation_md = gr.Markdown(s["HEADING_GENERATION"])
         with gr.Row():
             with gr.Column():
-                gr.Markdown(COL_BAGUETTOTRON_HEADING)
-                temp_baguettotron = gr.Slider(0, 2, value=0.5, label=LABEL_TEMPERATURE, info=INFO_TEMP_BAGUETTOTRON)
-                max_tok_baguettotron = gr.Number(value=512, label=LABEL_MAX_TOKENS, minimum=64, maximum=2048)
-                top_p_baguettotron = gr.Slider(0, 1, value=0.9, label=LABEL_TOP_P)
-                top_k_baguettotron = gr.Number(value=40, label=LABEL_TOP_K)
-                rep_baguettotron = gr.Slider(1.0, 1.5, value=1.1, label=LABEL_REPEAT_PENALTY)
+                col_baguettotron_md = gr.Markdown(s["COL_BAGUETTOTRON_HEADING"])
+                temp_baguettotron = gr.Slider(0, 2, value=0.5, label=s["LABEL_TEMPERATURE"], info=s["INFO_TEMP_BAGUETTOTRON"])
+                max_tok_baguettotron = gr.Number(value=512, label=s["LABEL_MAX_TOKENS"], minimum=64, maximum=2048)
+                top_p_baguettotron = gr.Slider(0, 1, value=0.9, label=s["LABEL_TOP_P"])
+                top_k_baguettotron = gr.Number(value=40, label=s["LABEL_TOP_K"])
+                rep_baguettotron = gr.Slider(1.0, 1.5, value=1.1, label=s["LABEL_REPEAT_PENALTY"])
             with gr.Column():
-                gr.Markdown(COL_LUTH_HEADING)
-                temp_luth = gr.Slider(0, 2, value=0.7, label=LABEL_TEMPERATURE)
-                max_tok_luth = gr.Number(value=256, label=LABEL_MAX_TOKENS, minimum=64, maximum=2048)
-                top_p_luth = gr.Slider(0, 1, value=0.9, label=LABEL_TOP_P)
-                top_k_luth = gr.Number(value=40, label=LABEL_TOP_K)
-                rep_luth = gr.Slider(1.0, 1.5, value=1.05, label=LABEL_REPEAT_PENALTY, info=INFO_REP_LUTH)
+                col_luth_md = gr.Markdown(s["COL_LUTH_HEADING"])
+                temp_luth = gr.Slider(0, 2, value=0.7, label=s["LABEL_TEMPERATURE"])
+                max_tok_luth = gr.Number(value=256, label=s["LABEL_MAX_TOKENS"], minimum=64, maximum=2048)
+                top_p_luth = gr.Slider(0, 1, value=0.9, label=s["LABEL_TOP_P"])
+                top_k_luth = gr.Number(value=40, label=s["LABEL_TOP_K"])
+                rep_luth = gr.Slider(1.0, 1.5, value=1.05, label=s["LABEL_REPEAT_PENALTY"], info=s["INFO_REP_LUTH"])
 
         # Row 3: Live inference — outputs above inputs
-        gr.Markdown(HEADING_LIVE_INFERENCE)
+        heading_live_md = gr.Markdown(s["HEADING_LIVE_INFERENCE"])
         models_by_tier = get_models_by_tier()
-        with gr.Tabs():
-            with gr.Tab(TIER_LABELS["small"]):
+        with gr.Tabs() as tabs_root:
+            with gr.Tab(s["TIER_SMALL"], id="tab_small") as tab_small:
                 with gr.Row():
                     out_baguettotron = gr.Textbox(
-                        label=LABEL_OUT_BAGUETTOTRON,
+                        label=s["LABEL_OUT_BAGUETTOTRON"],
                         lines=12,
                         max_lines=24,
                         value=startup_outputs[0],
                     )
                     out_luth_350 = gr.Textbox(
-                        label=LABEL_OUT_LUTH_350,
+                        label=s["LABEL_OUT_LUTH_350"],
                         lines=12,
                         max_lines=24,
                         value=startup_outputs[1],
                     )
-            with gr.Tab(TIER_LABELS["medium"]):
+            with gr.Tab(s["TIER_MEDIUM"], id="tab_medium") as tab_medium:
                 with gr.Row():
                     out_luth_06 = gr.Textbox(
-                        label=LABEL_OUT_LUTH_06,
+                        label=s["LABEL_OUT_LUTH_06"],
                         lines=12,
                         max_lines=24,
                         value=startup_outputs[2],
                     )
                     out_luth_07 = gr.Textbox(
-                        label=LABEL_OUT_LUTH_07,
+                        label=s["LABEL_OUT_LUTH_07"],
                         lines=12,
                         max_lines=24,
                         value=startup_outputs[3],
                     )
-            with gr.Tab(TIER_LABELS["large"]):
+            with gr.Tab(s["TIER_LARGE"], id="tab_large") as tab_large:
                 with gr.Row():
                     out_luth_12 = gr.Textbox(
-                        label=LABEL_OUT_LUTH_12,
+                        label=s["LABEL_OUT_LUTH_12"],
                         lines=12,
                         max_lines=24,
                         value=startup_outputs[4],
                     )
                     out_luth_17 = gr.Textbox(
-                        label=LABEL_OUT_LUTH_17,
+                        label=s["LABEL_OUT_LUTH_17"],
                         lines=12,
                         max_lines=24,
                         value=startup_outputs[5],
                     )
 
         system_prompt_in = gr.Textbox(
-            label=LABEL_SYSTEM_PROMPT,
-            placeholder=PLACEHOLDER_SYSTEM_PROMPT,
+            label=s["LABEL_SYSTEM_PROMPT"],
+            placeholder=s["PLACEHOLDER_SYSTEM_PROMPT"],
             lines=2,
         )
         prompt_in = gr.Textbox(
-            label=LABEL_PROMPT,
-            placeholder=PLACEHOLDER_PROMPT,
+            label=s["LABEL_PROMPT"],
+            placeholder=s["PLACEHOLDER_PROMPT"],
             lines=3,
             value=STARTUP_EXAMPLE_PROMPT,
         )
-        gen_btn = gr.Button(BTN_GENERATE, variant="primary")
+        gen_btn = gr.Button(s["BTN_GENERATE"], variant="primary")
 
         all_inputs = [
             prompt_in,
@@ -278,9 +309,53 @@ def create_ui():
         )
 
         # How to use & join us
-        gr.Markdown(HEADING_HOW_TO_USE)
-        gr.Markdown(HOW_TO_USE)
-        gr.Markdown(JOIN_US)
+        heading_how_to_use_md = gr.Markdown(s["HEADING_HOW_TO_USE"])
+        how_to_use_md = gr.Markdown(s["HOW_TO_USE"])
+        join_us_md = gr.Markdown(s["JOIN_US"])
+
+        # Language toggle: update all visible strings
+        _lang_outputs = [
+            title_md,
+            subtitle_md,
+            heading_footprint_md,
+            footprint_intro_md,
+            footprint_df,
+            footprint_summary_md,
+            heading_generation_md,
+            col_baguettotron_md,
+            col_luth_md,
+            temp_baguettotron,
+            max_tok_baguettotron,
+            top_p_baguettotron,
+            top_k_baguettotron,
+            rep_baguettotron,
+            temp_luth,
+            max_tok_luth,
+            top_p_luth,
+            top_k_luth,
+            rep_luth,
+            heading_live_md,
+            tab_small,
+            tab_medium,
+            tab_large,
+            out_baguettotron,
+            out_luth_350,
+            out_luth_06,
+            out_luth_07,
+            out_luth_12,
+            out_luth_17,
+            system_prompt_in,
+            prompt_in,
+            gen_btn,
+            heading_how_to_use_md,
+            how_to_use_md,
+            join_us_md,
+        ]
+        lang_radio.change(
+            fn=_ui_updates_for_locale,
+            inputs=[lang_radio],
+            outputs=_lang_outputs,
+        )
 
     return demo
 

ui_strings.py

@@ -3,6 +3,10 @@ UI text strings for the Baguettotron vs Luth Gradio app.
 Centralized for reuse and easier i18n.
 """
 
+# ---------------------------------------------------------------------------
+# English (default) — also used as key names
+# ---------------------------------------------------------------------------
+
 # App identity
 TITLE = "Is a government-backed darling any better than a highschoolers' project?"
 SUBTITLE = "Baguettotron vs Luth models : one is a fully subsidized project with priority access to public compute infrastructure , the other is a highschooler's project"
@@ -48,6 +52,11 @@ LABEL_OUT_LUTH_07 = "Luth-LFM2-700M"
 LABEL_OUT_LUTH_12 = "Luth-LFM2-1.2B"
 LABEL_OUT_LUTH_17 = "Luth-1.7B-Instruct"
 
+# Tab tier labels
+TIER_SMALL = "~0.3–0.4B (Small)"
+TIER_MEDIUM = "~0.6–0.7B (Medium)"
+TIER_LARGE = "~1–2B (Large)"
+
 # How to use & context
 HEADING_HOW_TO_USE = "## How to use this demo"
 HOW_TO_USE = """
@@ -60,8 +69,6 @@ HOW_TO_USE = """
 4. the ultimate judge of quality is the user's ability to use the model to achieve their goals
 
 Results are grouped by parameter size so you can compare Baguettotron with Luth-LFM2-350M in the Small tab, and the larger Luth models in the Medium and Large tabs.
-
-*Console:* If you see `lm_head.weight | MISSING` when loading Luth-0.6B or Luth-1.7B, that’s expected (tied embeddings); the models run correctly.
 """
 
 # Join us (TeamTonic community)
@@ -73,3 +80,104 @@ On 🤗Huggingface: [MultiTransformer](https://huggingface.co/MultiTransformer)
 On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Build Tonic](https://git.tonic-ai.com/contribute)
 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
 """
+
+# ---------------------------------------------------------------------------
+# Translations: en + fr (for language toggle)
+# ---------------------------------------------------------------------------
+
+TRANSLATIONS = {
+    "en": {
+        "TITLE": TITLE,
+        "SUBTITLE": SUBTITLE,
+        "HEADING_FOOTPRINT": HEADING_FOOTPRINT,
+        "FOOTPRINT_HEADERS": FOOTPRINT_HEADERS,
+        "FOOTPRINT_SUMMARY_TEMPLATE": FOOTPRINT_SUMMARY_TEMPLATE,
+        "FOOTPRINT_INTRO": FOOTPRINT_INTRO,
+        "HEADING_GENERATION": HEADING_GENERATION,
+        "COL_BAGUETTOTRON_HEADING": COL_BAGUETTOTRON_HEADING,
+        "COL_LUTH_HEADING": COL_LUTH_HEADING,
+        "LABEL_TEMPERATURE": LABEL_TEMPERATURE,
+        "LABEL_MAX_TOKENS": LABEL_MAX_TOKENS,
+        "LABEL_TOP_P": LABEL_TOP_P,
+        "LABEL_TOP_K": LABEL_TOP_K,
+        "LABEL_REPEAT_PENALTY": LABEL_REPEAT_PENALTY,
+        "INFO_TEMP_BAGUETTOTRON": INFO_TEMP_BAGUETTOTRON,
+        "INFO_REP_LUTH": INFO_REP_LUTH,
+        "HEADING_LIVE_INFERENCE": HEADING_LIVE_INFERENCE,
+        "LABEL_SYSTEM_PROMPT": LABEL_SYSTEM_PROMPT,
+        "PLACEHOLDER_SYSTEM_PROMPT": PLACEHOLDER_SYSTEM_PROMPT,
+        "LABEL_PROMPT": LABEL_PROMPT,
+        "PLACEHOLDER_PROMPT": PLACEHOLDER_PROMPT,
+        "BTN_GENERATE": BTN_GENERATE,
+        "LABEL_OUT_BAGUETTOTRON": LABEL_OUT_BAGUETTOTRON,
+        "LABEL_OUT_LUTH_350": LABEL_OUT_LUTH_350,
+        "LABEL_OUT_LUTH_06": LABEL_OUT_LUTH_06,
+        "LABEL_OUT_LUTH_07": LABEL_OUT_LUTH_07,
+        "LABEL_OUT_LUTH_12": LABEL_OUT_LUTH_12,
+        "LABEL_OUT_LUTH_17": LABEL_OUT_LUTH_17,
+        "TIER_SMALL": TIER_SMALL,
+        "TIER_MEDIUM": TIER_MEDIUM,
+        "TIER_LARGE": TIER_LARGE,
+        "HEADING_HOW_TO_USE": HEADING_HOW_TO_USE,
+        "HOW_TO_USE": HOW_TO_USE,
+        "JOIN_US": JOIN_US,
+    },
+    "fr": {
+        "TITLE": "Un chouchou subventionné vaut-il mieux qu'un projet de lycéen ?",
+        "SUBTITLE": "Baguettotron vs Luth : l'un est un projet subventionné avec accès prioritaire au calcul public, l'autre est un projet de lycéen.",
+        "HEADING_FOOTPRINT": "## Comparaison des modèles",
+        "FOOTPRINT_HEADERS": [
+            "Modèle",
+            "Params",
+            "Disque/VRAM brut (Mo)",
+            "Visible sur téléphone",
+            "GGUF Q4_K_M (Mo)",
+            "Source",
+        ],
+        "FOOTPRINT_SUMMARY_TEMPLATE": "**Empreinte totale —** VRAM (est.) : {total_vram:.2f} Go",
+        "FOOTPRINT_INTRO": "Transformers (BF16) disque/VRAM ; tailles GGUF depuis PleIAs/Baguettotron-GGUF (HF) et bundles LEAP.",
+        "HEADING_GENERATION": "## Réglages de génération (par famille de modèle)",
+        "COL_BAGUETTOTRON_HEADING": "**Baguettotron (321M)** — *raisonnement*",
+        "COL_LUTH_HEADING": "**Luth (0,4B–1,7B)** — *instruct*",
+        "LABEL_TEMPERATURE": "Température",
+        "LABEL_MAX_TOKENS": "Tokens max",
+        "LABEL_TOP_P": "Top p",
+        "LABEL_TOP_K": "Top k",
+        "LABEL_REPEAT_PENALTY": "Pénalité de répétition",
+        "INFO_TEMP_BAGUETTOTRON": "Plus bas pour un raisonnement plus déterministe",
+        "INFO_REP_LUTH": "Luth/LFM2 utilisent souvent ~1,05",
+        "HEADING_LIVE_INFERENCE": "## Inférence en direct",
+        "LABEL_SYSTEM_PROMPT": "Prompt système (optionnel)",
+        "PLACEHOLDER_SYSTEM_PROMPT": "ex. Tu es un assistant qui répond en français.",
+        "LABEL_PROMPT": "Prompt",
+        "PLACEHOLDER_PROMPT": "Saisissez votre prompt ici…",
+        "BTN_GENERATE": "Générer",
+        "LABEL_OUT_BAGUETTOTRON": "Baguettotron (321M)",
+        "LABEL_OUT_LUTH_350": "Luth-LFM2-350M (0,4B)",
+        "LABEL_OUT_LUTH_06": "Luth-0.6B-Instruct",
+        "LABEL_OUT_LUTH_07": "Luth-LFM2-700M",
+        "LABEL_OUT_LUTH_12": "Luth-LFM2-1.2B",
+        "LABEL_OUT_LUTH_17": "Luth-1.7B-Instruct",
+        "TIER_SMALL": "~0,3–0,4B (Petit)",
+        "TIER_MEDIUM": "~0,6–0,7B (Moyen)",
+        "TIER_LARGE": "~1–2B (Grand)",
+        "HEADING_HOW_TO_USE": "## Comment utiliser cette démo",
+        "HOW_TO_USE": """
+**Contexte :** Cette app envoie le même prompt à **Baguettotron** (PleIAs, 321M) et aux **cinq modèles Luth** (kurakurai, 0,4B–1,7B) pour comparer les sorties côte à côte. Le tableau ci-dessus résume la taille, la VRAM et les options GGUF/LEAP.
+
+**Étapes :**
+1. [Baguettotron-GGUF-321M](https://hf.co/PleIAs/Baguettotron-GGUF-321M) prétend des perfs hors du réel observable.
+2. le jeu [SYNTH](https://hf.co/datasets/pleias/synth) doit être audité pour contamination avant usage.
+3. la compression des modèles PleIAs sous-performe par rapport à une compression identique sur des modèles similaires.
+4. le critère final reste la capacité de l'utilisateur à atteindre ses objectifs avec le modèle.
+
+Les résultats sont regroupés par taille pour comparer Baguettotron avec Luth-LFM2-350M dans l'onglet Petit, et les Luth plus gros dans Moyen et Grand.
+""",
+        "JOIN_US": JOIN_US,  # same as en (links + community)
+    },
+}
+
+
+def get_strings(locale: str) -> dict:
+    """Return the full dict of UI strings for the given locale. Fallback to 'en'."""
+    return TRANSLATIONS.get(locale, TRANSLATIONS["en"]).copy()