Create app.py

app.py

@@ -0,0 +1,56 @@
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer, GenerationConfig
+
+peft_model_id = "mrm8488/falcon-7b-ft-codeAlpaca_20k-v2" # adapter
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map={"":0}, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
+
+model = PeftModel.from_pretrained(model, peft_model_id)
+model.eval()
+
+def generate(
+        instruction,
+        max_new_tokens=128,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        **kwargs
+):
+    prompt = instruction + "\n### Solution:\n"
+    print(prompt)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to("cuda")
+    attention_mask = inputs["attention_mask"].to("cuda")
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+            early_stopping=True
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Solution:")[1].lstrip("\n")
+
+import gradio as gr
+def my_function(input):
+    # Perform your task or computation using the input
+    # Return the output/result
+    return output
+    
+iface = gr.Interface(fn=my_function, inputs="text", outputs="text")
+iface.launch()
+