Create app.py

app.py

@@ -0,0 +1,80 @@
+from peft import PeftModel
+from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig
+
+tokenizer = LLaMATokenizer.from_pretrained("decapoda-research/llama-7b-hf")
+model = LLaMAForCausalLM.from_pretrained(
+    "decapoda-research/llama-7b-hf",
+    load_in_8bit=True,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(model, "tloen/alpaca-lora-7b")
+
+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. Answer step by step.
+
+### Instruction:
+{instruction}
+
+### Input:
+{input}
+
+### Response:"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. Answer step by step.
+
+### Instruction:
+{instruction}
+
+### Response:"""
+
+generation_config = GenerationConfig(
+    temperature=0.1,
+    top_p=0.75,
+    num_beams=4,
+)
+
+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())
+
+import gradio as gr
+from peft import PeftModel
+from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig
+
+import gradio as gr
+
+def evaluate1(instruction):
+    prompt = generate_prompt(instruction)
+    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)
+        return output.split("### Response:")[1].strip()
+
+inputs = gr.inputs.Textbox(lines=5, label="Instruction")
+outputs = gr.outputs.Textbox(label="Response")
+title = "LLaMA-7B Language Model"
+description = "This is a LLaMA-7B language model fine-tuned on various text datasets to generate text for a given task. It was trained on PyTorch by and is capable of generating high-quality, coherent text that is similar to human writing. The model is highly versatile and can be used for a variety of tasks, including text completion, summarization, and translation."
+copyright = "Copyright Bhaskar Tripathi (2023)"
+
+gr.Interface(evaluate1, inputs, outputs, title=title, description=description, footer=copyright, flag=False).launch()
+