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app.py

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+"""
+app.py - Hugging Face Spaces (CPU) deployment for the fine-tuned Llama 3.2 1B LoRA.
+
+This runs WITHOUT unsloth or bitsandbytes, because both require a GPU/CUDA.
+Instead it loads a full-precision base model on CPU with `transformers` and
+applies your LoRA adapter on top with `peft`, then merges the two so CPU
+generation is a little faster.
+
+The adapter repo (mthuy/dssp-llama-1b-lora) is public, so no token is needed.
+"""
+
+import os
+import torch
+import gradio as gr
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# ------------------------------------------------------------
+# Configuration
+# ------------------------------------------------------------
+
+# Your fine-tuned LoRA adapter (also holds the tokenizer + chat template).
+ADAPTER_REPO_ID = "mthuy/dssp-llama-1b-lora"
+
+# Full-precision base model to apply the adapter on top of.
+# The adapter was trained on "unsloth/Llama-3.2-1B-Instruct-bnb-4bit" (4-bit),
+# which can't load on CPU. This is the same model, un-quantized and ungated.
+BASE_MODEL_ID = "unsloth/Llama-3.2-1B-Instruct"
+
+# Keep CPU generation responsive.
+torch.set_num_threads(os.cpu_count() or 2)
+
+# ------------------------------------------------------------
+# Load model + tokenizer once at startup
+# ------------------------------------------------------------
+
+print("Loading tokenizer from adapter repo...")
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER_REPO_ID)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+print("Loading base model on CPU (float32)...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL_ID,
+    torch_dtype=torch.float32,   # float32 is the safe choice on CPU
+    low_cpu_mem_usage=True,
+)
+
+print("Applying LoRA adapter...")
+model = PeftModel.from_pretrained(base_model, ADAPTER_REPO_ID)
+
+# Merge LoRA weights into the base for faster CPU inference, then drop PEFT wrappers.
+model = model.merge_and_unload()
+model.eval()
+
+print("Model ready.")
+
+# ------------------------------------------------------------
+# Inference
+# ------------------------------------------------------------
+
+def ask_model(question, max_new_tokens=60, temperature=0.0):
+    if not question or not question.strip():
+        return ""
+
+    messages = [{"role": "user", "content": question}]
+
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+        return_dict=True,
+    )
+    # Everything stays on CPU.
+
+    generation_args = {
+        "max_new_tokens": int(max_new_tokens),
+        "pad_token_id": tokenizer.eos_token_id,
+        "eos_token_id": tokenizer.eos_token_id,
+        "use_cache": True,
+    }
+
+    if temperature and temperature > 0:
+        generation_args["do_sample"] = True
+        generation_args["temperature"] = float(temperature)
+        generation_args["top_p"] = 0.9
+    else:
+        generation_args["do_sample"] = False
+
+    with torch.inference_mode():
+        outputs = model.generate(**inputs, **generation_args)
+
+    input_length = inputs["input_ids"].shape[-1]
+    new_tokens = outputs[0][input_length:]
+    response = tokenizer.decode(new_tokens, skip_special_tokens=True)
+
+    return response.strip()
+
+# ------------------------------------------------------------
+# Gradio UI
+# ------------------------------------------------------------
+
+with gr.Blocks(title="DSSP Fine-Tuned Llama 1B Demo") as demo:
+
+    gr.Markdown(
+        """
+        # DSSP Fine-Tuned Llama 1B Demo (Dog Food Edition)
+
+        This Space runs a Llama 3.2 1B model fine-tuned with a LoRA adapter on a
+        handful of dog food / canine nutrition facts. It runs on **CPU**, so the
+        first answer after a cold start can take a little while.
+
+        Adapter: `mthuy/dssp-llama-1b-lora`
+        """
+    )
+
+    question = gr.Textbox(
+        label="Question",
+        lines=3,
+        placeholder="Example: Is chocolate safe for dogs?"
+    )
+
+    with gr.Row():
+        max_new_tokens = gr.Slider(
+            minimum=20,
+            maximum=100,
+            value=60,
+            step=10,
+            label="Max new tokens"
+        )
+
+        temperature = gr.Slider(
+            minimum=0.0,
+            maximum=1.0,
+            value=0.0,
+            step=0.1,
+            label="Temperature"
+        )
+
+    answer = gr.Textbox(
+        label="Model answer",
+        lines=6
+    )
+
+    submit = gr.Button("Ask model")
+
+    submit.click(
+        fn=ask_model,
+        inputs=[question, max_new_tokens, temperature],
+        outputs=answer
+    )
+
+    gr.Examples(
+        examples=[
+            ["Is chocolate safe for dogs?", 60, 0.0],
+            ["Can dogs eat grapes?", 60, 0.0],
+            ["Are onions safe for dogs?", 60, 0.0],
+            ["What is the most important nutrient in a dog's diet?", 60, 0.0],
+            ["How often should an adult dog be fed?", 60, 0.0],
+        ],
+        inputs=[question, max_new_tokens, temperature],
+        outputs=answer,
+        fn=ask_model,
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+# CPU-only build of PyTorch (keeps the Space small and avoids CUDA wheels)
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+torch>=2.2,<3
+transformers>=4.45,<5
+peft>=0.13
+accelerate>=0.34
+sentencepiece>=0.2
+huggingface_hub>=0.25
+gradio>=4.44