Spaces:
Sleeping
Sleeping
File size: 4,036 Bytes
926675f |
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 |
import os
import sys
import torch
from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
from alpaca.utils.prompter import Prompter
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
try:
if torch.backends.mps.is_available():
device = "mps"
except: # noqa: E722
pass
class AlpacaLora:
def __init__(self, load_8bit: bool = True,
base_model: str = "decapoda-research/llama-7b-hf",
lora_weights: str = "tloen/alpaca-lora-7b",
prompt_template: str = ""):
base_model = base_model or os.environ.get("BASE_MODEL", "")
assert (
base_model
), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
self.prompter = Prompter(prompt_template)
self.tokenizer = LlamaTokenizer.from_pretrained(base_model)
if device == "cuda":
self.model = LlamaForCausalLM.from_pretrained(
base_model,
load_in_8bit=load_8bit,
torch_dtype=torch.float16,
device_map="auto",
)
self.model = PeftModel.from_pretrained(
self.model,
lora_weights,
torch_dtype=torch.float16,
)
elif device == "mps":
self.model = LlamaForCausalLM.from_pretrained(
base_model,
device_map={"": device},
torch_dtype=torch.float16,
)
self.model = PeftModel.from_pretrained(
self.model,
lora_weights,
device_map={"": device},
torch_dtype=torch.float16,
)
else:
self.model = LlamaForCausalLM.from_pretrained(
base_model, device_map={"": device}, low_cpu_mem_usage=True
)
self.model = PeftModel.from_pretrained(
self.model,
lora_weights,
device_map={"": device},
)
# unwind broken decapoda-research config
self.model.config.pad_token_id = self.tokenizer.pad_token_id = 0 # unk
self.model.config.bos_token_id = 1
self.model.config.eos_token_id = 2
if not load_8bit:
self.model.half() # seems to fix bugs for some users.
self.model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(self.model)
def lora_generate(self, instruction, input):
# evaluate
temperature = 0
top_p = 0.75
top_k = 40
num_beams = 4
max_new_tokens = 128
stream_output = False
prompt = self.prompter.generate_prompt(instruction, input)
inputs = self.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,
)
generate_params = {
"input_ids": input_ids,
"generation_config": generation_config,
"return_dict_in_generate": True,
"output_scores": True,
"max_new_tokens": max_new_tokens,
}
with torch.no_grad():
generation_output = self.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 = self.tokenizer.decode(s)
return self.prompter.get_response(output), prompt
# PARAMS
load_8bit: bool = True
base_model: str = "decapoda-research/llama-7b-hf"
lora_weights: str = "./lora-alpaca" # "tloen/alpaca-lora-7b"
prompt_template: str = ""
server_name: str = "0.0.0.0"
share_gradio: bool = False
|