Spaces:
Sleeping
Sleeping
File size: 2,768 Bytes
1c0e302 075341e 1c0e302 4671f72 1c0e302 4671f72 1c0e302 075341e 1c0e302 5da5aea 5f6f93d 1c0e302 66bd4f7 1c0e302 1578b0f 1c0e302 3817432 1c0e302 c8563aa d799106 1c0e302 e9c890c 40ac173 1c0e302 2386c63 1578b0f 1c0e302 1578b0f 2386c63 1578b0f b12f8f0 1c0e302 1578b0f |
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 |
import pathlib
import gradio as gr
import transformers
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
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 AutoModelForCausalLM.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:"""
model= load_model(name = 's3nh/pythia-410m-70k-steps-self-instruct-polish')
tokenizer = load_tokenizer(name = 's3nh/pythia-410m-70k-steps-self-instruct-polish')
generation_config = create_generator()
def evaluate(instruction, input=None):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"]
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=256
)
result = []
for s in generation_output.sequences:
output = tokenizer.decode(s)
result.append( output.split("### Response:")[1].strip())
return ' '.join(el for el in result)
def inference(text, input):
output = evaluate(instruction = text, input = input)
return output
def choose_model(name):
return load_model(name), load_tokenizer(name)
model, tokenizer = gr.Interface(choose_model, [gr.inputs.Dropdown(["s3nh/pythia-1.4b-deduped-16k-steps-self-instruct-polish", "s3nh/pythia-1.4b-deduped-16k-steps-self-instruct-polish"]), "text"], "text")
io = gr.Interface(
inference,
inputs = [gr.Textbox(
lines = 3,
max_lines = 10,
placeholder = "Add question here",
interactive = True,
show_label = False
),
gr.Textbox(
lines = 3,
max_lines = 10,
placeholder = "Add context here",
interactive = True,
show_label = False
)],
outputs = [gr.Textbox(lines = 1, label = 'Pythia410m', interactive = False)],
cache_examples = False,
)
io.launch() |