File size: 4,957 Bytes
1178b35 2758ae4 1178b35 2758ae4 1178b35 2758ae4 1178b35 2758ae4 1178b35 c6bce9b 1178b35 2758ae4 1178b35 2758ae4 1178b35 2758ae4 1178b35 2758ae4 c6bce9b 2758ae4 1178b35 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
import torch
from peft import PeftModel
import transformers
import gradio as gr
assert (
"LlamaTokenizer" in transformers._import_structure["models.llama"]
), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
tokenizer = LlamaTokenizer.from_pretrained("daryl149/llama-2-7b-chat-hf")
BASE_MODEL = "daryl149/llama-2-13b-chat-hf"
LORA_WEIGHTS = "Sparticle/llama-2-13b-chat-japanese-lora"
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
try:
if torch.backends.mps.is_available():
device = "mps"
except:
pass
if device == "cuda":
model = LlamaForCausalLM.from_pretrained(
BASE_MODEL,
load_in_8bit=False,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True
)
elif device == "mps":
model = LlamaForCausalLM.from_pretrained(
BASE_MODEL,
device_map={"": device},
torch_dtype=torch.float16,
)
model = PeftModel.from_pretrained(
model,
LORA_WEIGHTS,
device_map={"": device},
torch_dtype=torch.float16,
)
else:
model = LlamaForCausalLM.from_pretrained(
BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True
)
model = PeftModel.from_pretrained(
model,
LORA_WEIGHTS,
device_map={"": device},
)
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:"""
if device != "cpu":
model.half()
model.eval()
if torch.__version__ >= "2":
model = torch.compile(model)
def evaluate(
instruction,
input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
max_new_tokens=128,
**kwargs,
):
if instruction == '' or instruction == None:
return 'Instruction not found. Please enter your instruction.\nInstructionを入力してください。'
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
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,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
return output.split("### Response:")[1].strip().replace('</s>', '')
g = gr.Interface(
fn=evaluate,
inputs=[
gr.components.Textbox(
lines=2, label="Instruction", placeholder="例１：日本語から英語に翻訳してください。\n\
例２：このテキストを要約してください。\n\
例３：英語から日本語に翻訳してください。"
),
gr.components.Textbox(lines=2, label="Input", placeholder="例１：日本語のテキスト\n\
例２：日本語の長いテキスト\n\
例３：英語のテキスト"),
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=1000, step=1, value=128, label="Max tokens"
),
],
outputs=[
gr.inputs.Textbox(
lines=5,
label="Output",
)
],
title="Llama2_7b_chat_Japanese_Lora",
description="Llama-2-13b-chat-Japanese-LoRA is a multi-purpose large language model for Japanese text.\n\
This model is presented by the joint effort of Sparticle Inc. and A. I. Hakusan Inc.\n\
Llama-2-13b-chat-Japanese-LoRAは日本語テキストのための多目的大規模言語モデルです。\n\
このモデルは日本語が話せる。日本語での指示や入力が可能です。\n\
このモデルは、Sparticle株式会社と株式会社白山人工知能の共同開発により発表されました。",
)
g.queue(concurrency_count=1)
g.launch() |