Fix: restore correct requirements.txt

requirements.txt

@@ -1,134 +1,6 @@
-"""Gradio demo for the New Prussian translator (Apertus-8B-int8 + LoRA).
-
-Loads the pre-quantized int8 base model `strfry/Apertus-8B-Instruct-2509-int8`
-and applies the LoRA adapter `strfry/apertus-8b-prussian-youtube` — all at
-module level on CPU. ZeroGPU transfers everything to VRAM automatically when
-a request enters @spaces.GPU. No lazy loading, no safetensors CUDA issues.
-"""
-
-import json
-import torch
-import gradio as gr
-import safetensors.torch
-from huggingface_hub import hf_hub_download
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel, LoraConfig
-
-# ZeroGPU decorator if available; no-op fallback so the app also runs locally
-try:
-    import spaces
-
-    GPU = spaces.GPU
-except ImportError:  # local / non-ZeroGPU
-
-    def GPU(func=None, **_kwargs):
-        if func is None:
-            return lambda f: f
-        return func
-
-
-BASE_MODEL = "strfry/Apertus-8B-Instruct-2509-int8"
-ADAPTER = "strfry/apertus-8b-prussian-youtube"
-
-SYSTEM_PROMPT = "Translate to reconstructed neo-prussian:"
-MAX_NEW_TOKENS = 100
-
-# ── All initialisation happens at module level (ZeroGPU best practice) ─────
-# Tokenizer from the adapter repo (carries the chatml template).
-_tokenizer = AutoTokenizer.from_pretrained(ADAPTER, trust_remote_code=True)
-
-# Pre-quantized int8 base model — quantization_config is baked into config.json
-# so no BitsAndBytesConfig is needed. device_map="auto" works with ZeroGPU's
-# CUDA emulation at import time, then ZeroGPU swaps in real CUDA at request time.
-_base = AutoModelForCausalLM.from_pretrained(
-    BASE_MODEL,
-    device_map="auto",
-    trust_remote_code=True,
-)
-
-# Attach the LoRA adapter using EXPLICIT CPU loading to avoid the safetensors
-# CUDA path that fails in ZeroGPU's emulation mode (RuntimeError: No CUDA GPUs
-# are available). PeftModel.from_pretrained() triggers __torch_dispatch__ hooks;
-# loading weights manually on CPU bypasses that.
-_config_path = hf_hub_download(ADAPTER, "adapter_config.json")
-with open(_config_path) as f:
-    _peft_config = LoraConfig(**json.load(f))
-
-_model = PeftModel(_base, _peft_config)
-
-_weights_path = hf_hub_download(ADAPTER, "adapter_model.safetensors")
-_adapter_weights = safetensors.torch.load_file(_weights_path, device="cpu")
-_model.load_state_dict(_adapter_weights, strict=False)
-_model.eval()
-
-
-@GPU(duration=120)
-def translate(text: str) -> str:
-    """Tokenize, generate, decode — the GPU-heavy work lives here."""
-    if not text.strip():
-        return ""
-
-    messages = [
-        {"role": "system", "content": SYSTEM_PROMPT},
-        {"role": "user", "content": text.strip()},
-    ]
-    inputs = (
-        _tokenizer.apply_chat_template(
-            messages,
-            add_generation_prompt=True,
-            return_tensors="pt",
-            return_dict=True,
-        )
-        .to(_base.device)
-    )
-
-    # <|im_end|> is NOT a single special token in the base tokenizer — the
-    # model was trained to output it as subword pieces.  eos_token_id cannot
-    # help here (it points to <|assistant_end|>, which the adapter never
-    # generates).  We clean up after decoding instead.
-    with torch.no_grad():
-        out = _model.generate(
-            **inputs,
-            max_new_tokens=MAX_NEW_TOKENS,
-            do_sample=False,
-            repetition_penalty=1.2,
-            pad_token_id=_tokenizer.eos_token_id,
-        )
-    new_tokens = out[0][inputs["input_ids"].shape[1] :]
-    result = _tokenizer.decode(new_tokens, skip_special_tokens=False).strip()
-    result = result.replace("<|im_end|>", "").strip()
-    cutoff = result.find("<|im_start|>")
-    if cutoff != -1:
-        result = result[:cutoff].strip()
-    return result
-
-
-with gr.Blocks(title="New Prussian Translator") as demo:
-    gr.Markdown(
-        "# New Prussian Translator\n"
-        "Apertus-8B + LoRA. Translates **into** reconstructed neo-Prussian "
-        "from German, English, Lithuanian, Latvian, … Model is pre-loaded — "
-        "queries complete in a few seconds."
-    )
-    gr.Markdown(f"**Fixed system prompt:** `{SYSTEM_PROMPT}`")
-    with gr.Row():
-        with gr.Column():
-            text = gr.Textbox(lines=4, label="Source text")
-            btn = gr.Button("Translate", variant="primary")
-        with gr.Column():
-            output = gr.Textbox(lines=4, label="New Prussian")
-
-    gr.Examples(
-        examples=[
-            ["Ich gehe in den Wald"],
-            ["All is very white."],
-            ["Wie heißt du?"],
-        ],
-        inputs=[text],
-    )
-
-    btn.click(translate, text, output)
-    text.submit(translate, text, output)
-
-if __name__ == "__main__":
-    demo.launch()
+torch
+transformers
+accelerate
+peft
+bitsandbytes
+spaces