SalpiBloomZ_15949_input_1024-1b7

<div style="text-align:center;width:450px;height:450px;"> <img src="https://huggingface.co/hackathon-somos-nlp-2023/SalpiBloomZ_15949_input_512-1b7/resolve/main/salpi.png" alt="SAlsapaca logo""> </div>

SalpiBloomZ-1b7: Spanish + BloomZ + Alpaca + softskills + virtual agents (WIP)

Adapter Description

This adapter was created with the PEFT library and allowed the base model bigscience/bloomz-1b7 to be fine-tuned on the hackathon-somos-nlp-2023/Habilidades_Agente_v1 by using the method LoRA.

How to use

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

peft_model_id = "hackathon-somos-nlp-2023/salsapaca-native" 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(peft_model_id)

Load the Lora model

model = PeftModel.from_pretrained(model, peft_model_id)

def gen_conversation(text): text = "<SC>instruction: " + text + "\n " batch = tokenizer(text, return_tensors='pt') with torch.cuda.amp.autocast(): output_tokens = model.generate(**batch, max_new_tokens=256, eos_token_id=50258, early_stopping = True, temperature=.9)

print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=False))

text = "hola"

gen_conversation(text)

Resources used

Google Colab machine with the following specifications

<div style="text-align:center;width:550px;height:550px;"> <img src="https://huggingface.co/hackathon-somos-nlp-2023/bertin-gpt-j-6B-es-finetuned-salpaca/resolve/main/resource.jpeg" alt="Resource logo"> </div>

Citation

@misc {hackathon-somos-nlp-2023, author = { {Edison Bejarano, Leonardo Bolaños, Alberto Ceballos, Santiago Pineda, Nicolay Potes} }, title = { SAlsapaca }, year = 2023, url = { https://huggingface.co/hackathon-somos-nlp-2023/salsapaca-native } publisher = { Hugging Face } }