--- language: - es license: apache-2.0 datasets: - hackathon-somos-nlp-2023/Habilidades_Agente_v1 pipeline_tag: conversational ---
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# SalpiBloomZ-1b7: Spanish + BloomZ + Alpaca + softskills + virtual agents (WIP) ## Adapter Description This adapter was created with the [PEFT](https://github.com/huggingface/peft) library and allowed the base model [bigscience/bloomz-1b7](https://huggingface.co/bigscience/bloomz-1b7) to be fine-tuned on the [hackathon-somos-nlp-2023/Habilidades_Agente_v1](https://huggingface.co/datasets/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
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## Citation @misc {hackathon-somos-nlp-2023, author = { {Edison Bejarano, Leonardo BolaƱos, Alberto Ceballos, Santiago Pineda, Nicolay Potes} }, title = { SAlsapaca }, year = 2023, url = { https://huggingface.co/hackathon-somos-nlp-2023/salsapaca-native } publisher = { Hugging Face } }