update script

app.py

@@ -1,17 +1,24 @@
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 import gradio as gr
 
-# Load the fine-tuned model and tokenizer
-model_path = "BoburAmirov/test-llama-uz"  # Adjust this to the path where your fine-tuned model is saved
+# Load the base model and tokenizer
+base_model_path = "NousResearch/Llama-2-7b-chat-hf"  # Path to the base model
+tokenizer_path = "BoburAmirov/test-llama-uz"  # Path to the tokenizer
 
-model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto')
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-
-# Ensure the tokenizer settings match those used during training
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True)
 tokenizer.pad_token = tokenizer.eos_token
 tokenizer.padding_side = "right"
 
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(base_model_path)
+
+# Load the adapter
+adapter_path = "BoburAmirov/test-llama-uz/adapter_model.safetensors"
+model = PeftModel.from_pretrained(base_model, adapter_path)
+
 # Set the model to evaluation mode
 model.eval()
 
@@ -23,7 +30,7 @@ def generate_text(input_prompt):
     with torch.no_grad():
         output = model.generate(
             input_ids,
-            max_length=400,  # Adjust max_length as needed
+            max_length=200,  # Adjust max_length as needed
             num_return_sequences=1,
             temperature=0.7,  # Control randomness
             top_p=0.9,  # Control diversity
@@ -35,13 +42,16 @@ def generate_text(input_prompt):
     return generated_text
 
 # Create a Gradio interface
-iface = gr.Interface(
+interface = gr.Interface(
     fn=generate_text,
     inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
     outputs="text",
-    title="Text Generation with LLaMA",
-    description="Generate text using a fine-tuned LLaMA model."
+    title="Text Generation with LLaMA-2",
+    description="Enter a prompt and get generated text from the fine-tuned LLaMA-2 model."
 )
 
+# Launch the Gradio interface
+
+
 if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+    interface.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

@@ -1,3 +1,4 @@
 torch
 transformers
-gradio
+gradio
+peft