Create app.py

app.py

@@ -0,0 +1,60 @@
+import pathlib
+import gradio as gr
+import transformers
+from transformers import AutoTokenizer
+from transformers import ModelForCausalLM 
+from transformers import GenerationConfig
+from typing import List, Dict, Union
+from typing import Any, TypeVar
+
+Pathable = Union[str, pathlib.Path]
+
+def load_model(name: str) -> Any:
+    return ModelForCausalLM.from_pretrained(name)
+
+def load_tokenizer(name: str) -> Any:
+    return AutoTokenizer.from_pretrained(name)
+
+def create_generator():
+    return GenerationConfig(
+    temperature=1.0,
+    top_p=0.75,
+    num_beams=4,
+)
+    
+def generate_prompt(instruction, input=None):
+    if input:
+        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Input:
+{input}
+
+### Response:"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:"""
+
+
+
+
+def evaluate(instruction, input=None):
+    prompt = generate_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].cuda()
+    generation_output = model.generate(
+        input_ids=input_ids,
+        generation_config=generation_config,
+        return_dict_in_generate=True,
+        output_scores=True,
+        max_new_tokens=256
+    )
+    for s in generation_output.sequences:
+        output = tokenizer.decode(s)
+        print("Response:", output.split("### Response:")[1].strip())