Create app.py

app.py

@@ -0,0 +1,52 @@
+import torch
+from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
+import gradio as gr
+import warnings
+import os
+
+# Remove command-line arguments parsing and use hardcoded defaults for simplicity in Spaces
+MODEL_PATH = "/model/13B_hf"
+LORA_PATH = "checkpoint-3000"
+USE_TYPEWRITER = 1
+USE_LOCAL = 1
+
+tokenizer = LlamaTokenizer.from_pretrained(MODEL_PATH)
+
+LOAD_8BIT = True
+BASE_MODEL = MODEL_PATH
+LORA_WEIGHTS = LORA_PATH
+
+lora_bin_path = os.path.join(LORA_PATH, "adapter_model.bin")
+if not os.path.exists(lora_bin_path) and USE_LOCAL:
+    # ... [rest of the path fixing logic]
+
+# ... [rest of the device and model loading logic]
+
+def generate_prompt(instruction, input=None):
+    # ... [rest of the generate_prompt function]
+
+def evaluate(
+    input,
+    temperature=0.1,
+    top_p=0.75,
+    top_k=40,
+    num_beams=4,
+    max_new_tokens=128,
+    min_new_tokens=1,
+    repetition_penalty=2.0,
+    **kwargs,
+):
+    # ... [rest of the evaluate function]
+
+gr.Interface(
+    fn=evaluate,
+    inputs=[
+        gr.components.Textbox(lines=2, label="Input", placeholder="Tell me about alpacas."),
+        # ... [rest of the inputs]
+    ],
+    outputs=[
+        gr.inputs.Textbox(lines=25, label="Output"),
+    ],
+    title="Chinese-Vicuna 中文小羊驼",
+    description="Chatlaw app trained on HK law data",
+).launch()