f

app.py

@@ -1,55 +1,43 @@
 import gradio as gr
 import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-MODEL_NAME = "cheberle/autotrain-35swc-b4r9z"
+# Repos
+BASE_MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
+ADAPTER_REPO = "cheberle/autotrain-35swc-b4r9z"
 
-# ---------------------------------------------------------------------------
-# 1) Load the tokenizer and model for sequence classification
-# ---------------------------------------------------------------------------
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, trust_remote_code=True)
+# 1. Load the PEFT config to confirm the base model
+peft_config = PeftConfig.from_pretrained(ADAPTER_REPO)
+print("PEFT Base Model:", peft_config.base_model_name_or_path)
 
-# Create a pipeline for text classification
-classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+# 2. Load the tokenizer & base model
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, trust_remote_code=True)
+
+# 3. Load your LoRA adapter weights onto the base model
+model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
 
-# ---------------------------------------------------------------------------
-# 2) Define inference function
-# ---------------------------------------------------------------------------
 def classify_text(text):
     """
-    Return the classification results in the format:
-    [
-      {
-        'label': 'POSITIVE',
-        'score': 0.98
-      }
-    ]
+    Simple prompting approach: we ask the model to return a single classification label 
+    (e.g., 'positive', 'negative', etc.).
+    You can refine this prompt, add chain-of-thought, or multiple classes as needed.
     """
-    results = classifier(text)
-    return results
+    prompt = f"Below is some text.\nText: {text}\nPlease classify the sentiment (positive or negative):"
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=64)
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return answer
 
-# ---------------------------------------------------------------------------
-# 3) Build the Gradio UI
-# ---------------------------------------------------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("<h3>Text Classification Demo</h3>")
-    
-    with gr.Row():
-        input_text = gr.Textbox(
-            lines=3, 
-            label="Enter text to classify",
-            placeholder="Type something..."
-        )
-        output = gr.JSON(label="Classification Output")
+    gr.Markdown("## Qwen + LoRA Adapter: Text Classification Demo")
+    input_box = gr.Textbox(lines=3, label="Enter text")
+    output_box = gr.Textbox(lines=3, label="Model's generated output (classification)")
 
     classify_btn = gr.Button("Classify")
+    classify_btn.click(fn=classify_text, inputs=input_box, outputs=output_box)
 
-    # Link the button to the function
-    classify_btn.click(fn=classify_text, inputs=input_text, outputs=output)
-
-# ---------------------------------------------------------------------------
-# 4) Launch the demo
-# ---------------------------------------------------------------------------
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1,4 +1,6 @@
 huggingface_hub==0.25.2
 torch
 transformers
-gradio
+peft
+gradio
+accelerate  # if needed for device_map