chivoom

<div style="text-align:center;width:250px;height:250px;"> <img src="https://huggingface.co/platzi/chivoom/resolve/main/chivoom_logo-removebg-preview.png"> </div>

Chivoom: Spanish Alpaca (Chiva) 🐐 + BLOOM 💮

IMPORTANT: This is just a PoC and still WIP!

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 (translated to Spanish) by using the method LoRA.

Model Description

BigScience Large Open-science Open-access Multilingual Language Model

BLOOM 7B1

Training data

We translated to Spanish the Alpaca dataset.

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.

Training procedure

TBA

How to use

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

peft_model_id = "platzi/chivoom"
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="auto")
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"""A continuación se muestra una instrucción que describe una tarea, emparejada con una entrada que proporciona más contexto. Escribe una respuesta que complete adecuadamente la petición.
### Instrucción:
{instruction}
### Entrada:
{input}
### Respuesta:"""
    else:
        return f"""A continuación se muestra una instrucción que describe una tarea. Escribe una respuesta que complete adecuadamente la petición.
### Instrucción:
{instruction}
### Respuesta:"""

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]

instruction = "¿Qué es un chivo?"

print("Instrucción:", instruction)
print("Respuesta:", generate(instruction))