README.md

metadata

datasets:
  - yahma/alpaca-cleaned
  - Nebulous/gpt4all_pruned
language:
  - en

Inference Example:

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = "edu-linguistic/opt-1.3b-edu-sft"
model_name = 'facebook/opt-1.3b'

config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(model_name)

question = "<|prompter|> Consider the following function: f(x1, x2) = ln(x1). This function is…"

question = tokenizer.encode(question, return_tensors='pt')

generation_kwargs = {
    "do_sample": True,
    "top_k": 0,
    "top_p": 0.9,
    "bos_token_id": tokenizer.bos_token_id,
    "pad_token_id": tokenizer.pad_token_id,
    "eos_token_id": tokenizer.eos_token_id,
    "num_return_sequences": 1,
    "min_new_tokens": 10,
    "max_new_tokens": 512,
}

response = model.generate(input_ids=question, **generation_kwargs)
response = tokenizer.decode(response[0],
                           skip_special_tokens=False,
                           clean_up_tokenization_spaces=False
                           )
print(response)