This repo contains a low-rank adapter for LLaMA-7b fit on the data containing a request to write a python function.
How to use (8-bit)
import torch
from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
tokenizer = LLaMATokenizer.from_pretrained("decapoda-research/llama-13b-hf")
model = LLaMAForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
device_map="auto",
)
model = PeftModel.from_pretrained(model, "Aspik101/Alpaca7b_python_assistant")
def get_answer(question, model_version = model):
PROMPT =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:
{question}
### Response:
'''
inputs = tokenizer(
PROMPT,
return_tensors="pt",
)
input_ids = inputs["input_ids"].cuda()
generation_config = GenerationConfig(
temperature=0.2,
top_p=0.95,
repetition_penalty=1.15,
)
print("Generating...")
generation_output = model_version.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=128,
)
sentences = " ".join([tokenizer.decode(s) for s in generation_output.sequences])
print(sentences.split("Response:\n")[1])
Examples
get_answer("Write a function that read csv by pandas")
Generating...
def read_csv(file):
df = pd.read_csv('data/test.csv')
get_answer("Write a function that check if number is even")
Generating...
def is_even(n):
return n %2 ==0