Add LoRA merging

app.py

@@ -35,8 +35,13 @@ MODEL_PATHS = {
     "sarcasm_less": "hallisky/lora-sarcasm-less-llama-3-8b", 
     "voice_passive": "hallisky/lora-voice-passive-llama-3-8b", 
     "voice_active": "hallisky/lora-voice-active-llama-3-8b", 
+    "type_persuasive": "hallisky/lora-type-persuasive-llama-3-8b",
+    "type_expository": "hallisky/lora-type-expository-llama-3-8b",
+    "type_narrative": "hallisky/lora-type-narrative-llama-3-8b",
+    "type_descriptive": "hallisky/lora-type-descriptive-llama-3-8b",
 }
 FIRST_MODEL = list(MODEL_PATHS.keys())[5]
+MAX_NEW_TOKENS=1024
 
 DESCRIPTION = """\
 # Authorship Obfuscation
@@ -113,49 +118,61 @@ def save_feedback(feedback_rating, feedback_text, latest_obfuscation):
     return "No Feedback Selected", ""
 
 @spaces.GPU
-def greet(input_text, length, function_words, grade_level, sarcasm, formality, voice, persuasive, descriptive, narrative, expository):
+def greet(input_text, length, function_words, grade_level, formality, sarcasm, voice, persuasive, descriptive, narrative, expository):
     global latest_obfuscation, user_id
     current_time = datetime.now().isoformat()
     
     sliders_dict = {}
     cur_keys = []
-    cur_keys.append(("length_more" if length > 0 else (None if length == 0 else "length_less"), length))
-    cur_keys.append(("function_more" if function_words > 0 else (None if function_words == 0 else "function_less"), function_words))
-    cur_keys.append(("grade_more" if grade_level > 0 else (None if grade_level == 0 else "grade_less"), grade_level))
-    cur_keys.append(("sarcasm_more" if sarcasm > 0 else (None if sarcasm == 0 else "sarcasm_less"), sarcasm))
-    cur_keys.append(("formality_more" if formality > 0 else (None if formality == 0 else "formality_less"), formality))
-    cur_keys.append(("voice_active" if voice > 0 else (None if voice == 0 else "voice_passive"), voice))
+    cur_keys.append(("length_more" if length > 0 else (None if length == 0 else "length_less"), abs(length)))
+    cur_keys.append(("function_more" if function_words > 0 else (None if function_words == 0 else "function_less"), abs(function_words)))
+    cur_keys.append(("grade_more" if grade_level > 0 else (None if grade_level == 0 else "grade_less"), abs(grade_level)))
+    cur_keys.append(("sarcasm_more" if sarcasm > 0 else (None if sarcasm == 0 else "sarcasm_less"), abs(sarcasm)))
+    cur_keys.append(("formality_more" if formality > 0 else (None if formality == 0 else "formality_less"), abs(formality)))
+    cur_keys.append(("voice_active" if voice > 0 else (None if voice == 0 else "voice_passive"),abs(voice)))
+    cur_keys.append(("type_persuasive" if persuasive != 0 else None, abs(persuasive)))
+    cur_keys.append(("type_descriptive" if descriptive != 0 else None, abs(descriptive)))
+    cur_keys.append(("type_narrative" if narrative != 0 else None, abs(narrative)))
+    cur_keys.append(("type_expository" if expository != 0 else None, abs(expository)))
 
     for cur_key in cur_keys:
         if cur_key[0] is not None:
             sliders_dict[cur_key[0]] = cur_key[1]
 
-    response = (
-        f"Hello!\n"
-        f"Input Text: {input_text}\n"
-        f"Length: {length}\n"
-        f"Function Words: {function_words}\n"
-        f"Grade Level: {grade_level}\n"
-        f"Sarcasm: {sarcasm}\n"
-        f"Formality: {formality}\n"
-        f"Voice: {voice}\n"
-        f"Persuasive: {persuasive}\n"
-        f"Descriptive: {descriptive}\n"
-        f"Narrative: {narrative}\n"
-        f"Expository: {expository}"
-    )
-
-    converted_text = convert_data_to_format(input_text)
-
-    # Convert the list of strings in data to a list of model inputs
-    inputs = tokenizer(converted_text, return_tensors="pt", max_length=2048, truncation=True).to(device)
-    input_length = inputs.input_ids.shape[1]
-    with torch.no_grad(): 
-        outputs = model.generate(**inputs, max_length=100, top_p = 0.95)
-
-    response = tokenizer.decode(outputs[0, input_length:], skip_special_tokens=True)
-    
-    print_nvidia_smi() # Print GPU usage
+    # Make the adapter and switch to it
+    print(sliders_dict)
+
+    if len(sliders_dict) > 0:
+        combo_adapter_name = ""
+        for slider_key in sliders_dict:
+            print(slider_key)
+            print(sliders_dict[slider_key])
+            combo_adapter_name += slider_key + str(int(100*sliders_dict[slider_key])) + "-"
+        combo_adapter_name = combo_adapter_name[:-1]
+        print(combo_adapter_name)
+        print(list(sliders_dict.values()))
+        print(list(sliders_dict.keys()))
+
+        # Add and set the weighted adapater
+        model.add_weighted_adapter(
+            list(sliders_dict.keys()),
+            weights = list(sliders_dict.values()),
+            adapter_name = combo_adapter_name,
+            combination_type = "cat"
+        )
+        model.set_adapter(combo_adapter_name)
+        
+        # Convert the list of strings in data to a list of model inputs
+        converted_text = convert_data_to_format(input_text)
+        inputs = tokenizer(converted_text, return_tensors="pt", max_length=2048, truncation=True).to(device)
+        input_length = inputs.input_ids.shape[1]
+        with torch.no_grad(): 
+            outputs = model.generate(**inputs, max_new_tokens=MAX_NEW_TOKENS, top_p = 0.95)
+        response = tokenizer.decode(outputs[0, input_length:], skip_special_tokens=True).strip()
+    else:
+        response = input_text # If no sliders passed, do not do anything
+
+    # print_nvidia_smi() # Print GPU usage
 
     # Save the new obfuscation result and reset feedback
     latest_obfuscation = {