First release of llimba-demo

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README.md

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+---
+title: LLiMba 3B Demo
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 6.5.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Chat with LLiMba, an open 3B LLM that speaks Sardinian
+hardware: zero-a10g
+models:
+  - lballore/llimba-3b-instruct
+tags:
+  - sardinian
+  - limba-sarda-comuna
+  - lsc
+  - low-resource
+  - endangered-language
+  - chat
+  - demo
+---
+
+# 💬 LLiMba 3B Demo
+
+A live demo of [**LLiMba-3B-Instruct**](https://huggingface.co/lballore/llimba-3b-instruct), an open 3B-parameter Sardinian-speaking language model adapted from Qwen2.5-3B-Instruct on a single consumer GPU.
+
+LLiMba speaks fluent **Sardinian** (LSC, the standardized written form), accepts **Logudorese** and **Campidanese** input, and retains the multilingual capabilities of its Qwen2.5 base, so it also handles English, Italian, Spanish, and other Romance languages.
+
+## Try these prompts
+
+If you don't speak Sardinian, here are starters that work well:
+
+**Conversation:**
+- *Salude! Comente ìstas?* (Hi! How are you?)
+
+**Translation:**
+- *Translate to Sardinian: "The Mediterranean is rough today."*
+- *Traduzi in italianu: «Sa Sardigna est una ìsula bella meda in su Mediterràneu.»*
+
+**Sardinian culture and history:**
+- *Chie fiat Gigi Riva?* (Who was Gigi Riva?)
+- *Ite est su «cantu a tenore» sardu?* (What is Sardinian *cantu a tenore*?)
+
+**Open-ended:**
+- *Iscrie unu paragrafu in sardu subra de sa Sardigna.* (Write a paragraph in Sardinian about Sardinia.)
+
+## Inference settings
+
+The sidebar exposes the standard generation parameters. Defaults are tuned for natural Sardinian conversation; the model card recommends three preset profiles depending on use case:
+
+| Use case | Temperature | Top-p | Top-k | Repetition penalty |
+|---|---:|---:|---:|---:|
+| Translation, factual Q&A | 0.01 (greedy) | 1.0 | 1 | 1.0 |
+| Conversational chat (default) | 0.3 | 0.9 | 40 | 1.05 |
+| Creative or long-form | 0.7 | 0.9 | 40 | 1.1 |
+
+Temperatures above 0.7 may cause language-boundary drift (Sardinian to Italian) and amplify morphological hallucination on long open-ended prompts. The model was trained with Romance replay data to mitigate this, but the safe upper bound for production use is around 0.7.
+
+## About the project
+
+LLiMba is an open project to bring Sardinian, an endangered Romance language with roughly one million speakers, into modern NLP. The full release includes:
+
+- 🤖 **Model:** [lballore/llimba-3b-instruct](https://huggingface.co/lballore/llimba-3b-instruct)
+- 🤖 **Intermediate checkpoint:** [lballore/llimba-3b-instruct-cpt](https://huggingface.co/lballore/llimba-3b-instruct-cpt) (post-CPT, pre-SFT, for researchers)
+- 📚 **Pretraining corpus:** [lballore/llimba-corpus](https://huggingface.co/datasets/lballore/llimba-corpus) (~13.9M tokens)
+- 📚 **SFT data:** [lballore/llimba-sft](https://huggingface.co/datasets/lballore/llimba-sft) (~14K instruction pairs)
+- 📚 **Eval set:** [lballore/llimba-flores-srd-eval](https://huggingface.co/datasets/lballore/llimba-flores-srd-eval) (FLORES-200 subset)
+- 💻 **Code:** [github.com/lballore/LLiMba](https://github.com/lballore/LLiMba)
+
+The model was adapted via continued pretraining on Sardinian text (with Romance replay data to prevent language drift) followed by supervised fine-tuning with rsLoRA on instruction pairs. Full methodology and benchmarks in the [model card](https://huggingface.co/lballore/llimba-3b-instruct).
+
+## Limitations
+
+This is a 3B-parameter model and like all small LLMs it can produce confident wrong answers, especially on biographical or historical specifics. Treat factual outputs with skepticism.
+
+On long open-ended generation, the model may occasionally produce phonotactically valid but non-attested Sardinian words. Structured prompts ("List the three main causes") work more reliably than open-ended ones ("Tell me about X").
+
+The model targets LSC and was reviewed by a single native speaker of the Nuorese variant. Logudorese and Campidanese input is handled, but speakers of those variants may find outputs skew toward the standardized form.
+
+## License
+
+The demo code is released under [Apache 2.0](LICENSE). The model weights are released under [Apache 2.0](LICENSE).

app.py

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+import os
+
+import gradio as gr
+import spaces
+import torch
+from threading import Thread
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+MODEL_ID = "lballore/llimba-3b-instruct"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# ---------------------------------------------------------------------------
+# System prompts
+# ---------------------------------------------------------------------------
+
+CHAT_SYSTEM_PROMPT = (
+    "Ses unu assistente chi chistionat in sardu (LSC). "
+    "Risponde in manera curtza, clara e pretzisa, chene repetire sa dimanda. "
+    "Si non connosches una risposta o non ses seguru, naralu in manera onesta "
+    "imbetzes de imbentare."
+)
+
+TRANSLATE_SYSTEM_TEMPLATE = (
+    "Ses unu tradutore espertu. Traduzi in {tgt} su testu chi sighit. "
+    "Su testu est in {src}. Risponde solu cun sa tradutzione, "
+    "sentza cummentos o ispiegatziones."
+)
+
+# Language names in Sardinian, used to fill the translation system prompt
+LANGUAGES = {
+    "Sardinian (LSC)": "sardu",
+    "Italian": "italianu",
+    "English": "inglesu",
+    "Spanish": "ispagnolu",
+}
+
+# ---------------------------------------------------------------------------
+# Examples
+# ---------------------------------------------------------------------------
+
+CHAT_EXAMPLES = [
+    "Salude! Comente ìstas?",
+    "Cale est sa capitale de sa Sardigna?",
+    "Chie fiat Gigi Riva?",
+    "Ite est su «cantu a tenore» sardu?",
+    "Iscrie unu paragrafu in sardu subra de sa Sardigna.",
+]
+
+TRANSLATE_EXAMPLES = [
+    "The weather is rough today.",
+    "Buongiorno, come stai? È una bellissima giornata oggi.",
+    "La cultura sarda è ricca di tradizioni antiche.",
+]
+
+# ---------------------------------------------------------------------------
+# Model loading (once at startup; ZeroGPU keeps weights on CPU until generate)
+# ---------------------------------------------------------------------------
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    dtype=torch.bfloat16,
+    device_map="auto",
+    token=HF_TOKEN,
+)
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _to_text(content):
+    """Normalize Gradio 6.x message content (may be list of dicts) to string."""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        return "".join(
+            part.get("text", "") if isinstance(part, dict) else str(part)
+            for part in content
+        )
+    return str(content)
+
+
+def _normalize_history(history):
+    return [
+        {"role": m["role"], "content": _to_text(m["content"])}
+        for m in history
+    ]
+
+
+def _stream_response(messages, max_tokens, temperature, top_p, top_k, rep_penalty):
+    """Run model.generate in a background thread and yield tokens as they arrive."""
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+        return_dict=True,
+    ).to(model.device)
+
+    streamer = TextIteratorStreamer(
+        tokenizer,
+        skip_prompt=True,
+        skip_special_tokens=True,
+    )
+
+    do_sample = temperature > 0.0
+    generation_kwargs = dict(
+        **inputs,
+        streamer=streamer,
+        max_new_tokens=max_tokens,
+        do_sample=do_sample,
+        temperature=temperature if do_sample else 1.0,
+        top_p=top_p if do_sample else 1.0,
+        top_k=top_k if do_sample else 50,
+        repetition_penalty=rep_penalty,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    response = ""
+    for new_text in streamer:
+        response += new_text
+        yield response
+
+# ---------------------------------------------------------------------------
+# Per-tab respond functions
+# ---------------------------------------------------------------------------
+
+@spaces.GPU(duration=60)
+def respond_chat(
+    message, history,
+    system_message, max_tokens, temperature, top_p, top_k, rep_penalty,
+):
+    messages = [{"role": "system", "content": system_message}]
+    messages.extend(_normalize_history(history))
+    messages.append({"role": "user", "content": _to_text(message)})
+
+    yield from _stream_response(
+        messages, max_tokens, temperature, top_p, top_k, rep_penalty,
+    )
+
+
+@spaces.GPU(duration=60)
+def respond_translate(
+    message, history,
+    source_lang, target_lang,
+    max_tokens, temperature, top_p, top_k, rep_penalty,
+):
+    src = LANGUAGES[source_lang]
+    tgt = LANGUAGES[target_lang]
+    system_message = TRANSLATE_SYSTEM_TEMPLATE.format(src=src, tgt=tgt)
+
+    messages = [{"role": "system", "content": system_message}]
+    messages.extend(_normalize_history(history))
+    messages.append({"role": "user", "content": _to_text(message)})
+
+    yield from _stream_response(
+        messages, max_tokens, temperature, top_p, top_k, rep_penalty,
+    )
+
+# ---------------------------------------------------------------------------
+# UI
+# ---------------------------------------------------------------------------
+
+DESCRIPTION = (
+    "Chat with [LLiMba-3B-Instruct](https://huggingface.co/lballore/llimba-3b-instruct), "
+    "an open 3B LLM that speaks **Sardinian** (LSC, with Logudorese and Campidanese "
+    "accepted as input). The model retains the multilingual capabilities of its Qwen2.5 base."
+)
+
+
+with gr.Blocks(title="LLiMba 3B Demo") as demo:
+    gr.Markdown("# 💬 LLiMba 3B Demo")
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Tabs():
+        # ----- Chat tab -----
+        with gr.Tab("💬 Chat"):
+            gr.ChatInterface(
+                respond_chat,
+                additional_inputs=[
+                    gr.Textbox(
+                        value=CHAT_SYSTEM_PROMPT,
+                        label="System message",
+                        info="Default tells the model to be concise and admit uncertainty.",
+                        lines=4,
+                    ),
+                    gr.Slider(
+                        minimum=1, maximum=2048, value=512, step=1,
+                        label="Max new tokens",
+                    ),
+                    gr.Slider(
+                        minimum=0.0, maximum=1.0, value=0.3, step=0.05,
+                        label="Temperature",
+                        info="0 = greedy. ≤0.5 recommended to limit hallucination and language drift.",
+                    ),
+                    gr.Slider(
+                        minimum=0.05, maximum=1.0, value=0.9, step=0.05,
+                        label="Top-p (nucleus sampling)",
+                    ),
+                    gr.Slider(
+                        minimum=1, maximum=100, value=40, step=1,
+                        label="Top-k",
+                    ),
+                    gr.Slider(
+                        minimum=1.0, maximum=2.0, value=1.05, step=0.05,
+                        label="Repetition penalty",
+                    ),
+                ],
+                examples=[[p] for p in CHAT_EXAMPLES],
+                cache_examples=False,
+            )
+
+        # ----- Translate tab -----
+        with gr.Tab("🌐 Translate"):
+            gr.ChatInterface(
+                respond_translate,
+                additional_inputs=[
+                    gr.Dropdown(
+                        choices=list(LANGUAGES.keys()),
+                        value="English",
+                        label="Source language",
+                    ),
+                    gr.Dropdown(
+                        choices=list(LANGUAGES.keys()),
+                        value="Sardinian (LSC)",
+                        label="Target language",
+                    ),
+                    gr.Slider(
+                        minimum=1, maximum=2048, value=512, step=1,
+                        label="Max new tokens",
+                    ),
+                    gr.Slider(
+                        minimum=0.0, maximum=1.0, value=0.0, step=0.05,
+                        label="Temperature",
+                        info="0 = greedy. Recommended for translation.",
+                    ),
+                    gr.Slider(
+                        minimum=0.05, maximum=1.0, value=1.0, step=0.05,
+                        label="Top-p",
+                    ),
+                    gr.Slider(
+                        minimum=1, maximum=100, value=1, step=1,
+                        label="Top-k",
+                    ),
+                    gr.Slider(
+                        minimum=1.0, maximum=2.0, value=1.0, step=0.05,
+                        label="Repetition penalty",
+                    ),
+                ],
+                examples=[[p] for p in TRANSLATE_EXAMPLES],
+                cache_examples=False,
+            )
+
+
+if __name__ == "__main__":
+    demo.launch(theme=gr.themes.Soft())

requirements.txt

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+gradio>=6.5.1
+spaces
+transformers>=4.46.0
+torch
+accelerate