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# %%
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import gradio as gr
from transformers import LlamaTokenizer
from transformers import LlamaForCausalLM, GenerationConfig
from peft import PeftModel
import torch
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
device_map={'': 0}
def generate_instruction_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(
model,
tokenizer,
instruction,
input=None,
temperature=0.1,
top_p=0.75,
num_beams=4,
max_token=256,
):
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
num_beams=num_beams,
top_k=40,
no_repeat_ngram_size=3,
)
prompt = generate_instruction_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_token,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
res = output.split("### Response:")[1].strip()
print("Response:", res)
return res
def load_lora(lora_path, base_model="decapoda-research/llama-7b-hf"):
model = LlamaForCausalLM.from_pretrained(
base_model,
# load_in_8bit=True,
device_map=device_map,
low_cpu_mem_usage=True,
)
print("Loading LoRA...")
lora = PeftModel.from_pretrained(
model,
lora_path,
device_map=device_map,
)
return lora
base_model = "decapoda-research/llama-13b-hf"
tokenizer = LlamaTokenizer.from_pretrained(base_model, device_map=device_map)
# question = "如果今天是星期五, 那么后天是星期几?"
model = load_lora(lora_path="facat/alpaca-lora-cn-13b", base_model=base_model)
eval = lambda question, input, temperature, beams, max_token: evaluate(
model,
tokenizer,
question,
input=input,
temperature=temperature,
num_beams=beams,
max_token=max_token,
)
gr.Interface(
fn=eval,
inputs=[
gr.components.Textbox(
lines=2, label="Instruction", placeholder="Tell me about alpacas."
),
gr.components.Textbox(lines=2, label="Input", placeholder="none"),
gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
# gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"),
# gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"),
gr.components.Slider(
minimum=1, maximum=512, step=1, value=256, label="Max tokens"
),
],
outputs=[
gr.inputs.Textbox(
lines=8,
label="Output",
)
],
title=f"Alpaca-LoRA",
description=f"Alpaca-LoRA",
).launch()
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