Text Generation
Transformers
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alpaca
bloom
LLM
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AlpacOOM: Alpaca ๐Ÿฆ™ + BLOOM ๐Ÿ’ฎ

Adapter Description

This adapter was created with the PEFT library and allowed the base model BigScience/BLOOM 7B1 to be fine-tuned on the Stanford's Alpaca Dataset by using the method LoRA.

Model Description

BigScience Large Open-science Open-access Multilingual Language Model

BLOOM 7B1

Training data

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

  • The text-davinci-003 engine to generate the instruction data instead of davinci.
  • A new prompt was written that explicitly gave the requirement of instruction generation to text-davinci-003.
  • Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
  • The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
  • Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.

This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.

Supported Tasks and Leaderboards

The Alpaca dataset is designed for instruction training pre-trained language models.

Training procedure

TBA

How to use

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

peft_model_id = "mrm8488/Alpacoom"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map={"":0})
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-7b1")

model = PeftModel.from_pretrained(model, peft_model_id)
model.eval()

# Based on the inference code by `tloen/alpaca-lora`
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:"""

def generate(
        instruction,
        input=None,
        temperature=0.1,
        top_p=0.75,
        top_k=40,
        num_beams=4,
        **kwargs,
):
    prompt = generate_prompt(instruction, input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()
    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=256,
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    return output.split("### Response:")[1].strip().split("Below")[0]

instruction = "Tell me about alpacas"

print("Instruction:", instruction)
print("Response:", generate(instruction))

Citation

@misc {manuel_romero_2023,
    author       = { {Manuel Romero} },
    title        = { Alpacoom (Revision 874f989) },
    year         = 2023,
    url          = { https://huggingface.co/mrm8488/Alpacoom },
    doi          = { 10.57967/hf/0449 },
    publisher    = { Hugging Face }
}
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