Update app.py

app.py

@@ -1,35 +1,36 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel, PeftConfig
 import torch
 
-# Carrega tokenizer e modelo manualmente
-tokenizer = AutoTokenizer.from_pretrained("lambdaindie/lambda-1v-1B")
-model = AutoModelForCausalLM.from_pretrained("lambdaindie/lambda-1v-1B")
+# Carrega config do adaptador
+config = PeftConfig.from_pretrained("lambdaindie/lambda-1v-1B")
 
-# Garante uso de CPU
+# Carrega modelo base + LoRA
+base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(base_model, "lambdaindie/lambda-1v-1B")
+
+# Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Envia pra CPU
 device = torch.device("cpu")
 model.to(device)
 
-# Função de geração
 def responder(prompt):
     inputs = tokenizer(prompt, return_tensors="pt").to(device)
     outputs = model.generate(
         **inputs,
         max_new_tokens=50,
-        do_sample=True,
-        top_p=0.95,
         temperature=0.8,
-        pad_token_id=tokenizer.eos_token_id  # evita warning se for causal
+        top_p=0.95,
+        pad_token_id=tokenizer.eos_token_id,
     )
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Interface Gradio
-iface = gr.Interface(
-    fn=responder,
-    inputs=gr.Textbox(lines=2, placeholder="Escreve algo..."),
-    outputs="text",
-    title="Lambda-1v-1B",
-    description="Modelo local de geração de texto criado por Marius Jabami.",
-)
-
+iface = gr.Interface(fn=responder,
+                     inputs=gr.Textbox(lines=2, placeholder="Escreve algo..."),
+                     outputs="text",
+                     title="Lambda-1v-1B (LoRA)",
+                     description="Modelo LoRA fine-tuned por Marius Jabami.")
 iface.launch()