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somosnlp-hackathon-2023
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SalpiBloomZ_15949_input_1024-1b7

Text Generation
Spanish conversational
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YAML Metadata Warning: The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
SAlsapaca logo

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 = "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

Resource logo

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 } }

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