metadata

language:
  - zh
license: bigscience-bloom-rail-1.0
library_name: peft
tags:
  - bloom
thumbnail: >-
  https://s3.amazonaws.com/moonup/production/uploads/1677459920577-63b8e3432adad59f41dc65f4.jpeg?w=200&h=200&f=face
pipeline_tag: text-generation
widget:
  - {}
base_model: bigscience/bloomz-7b1-mt

Bloom 7B1 LightNovel ZH_CN LoRa Finetuned

BigScience Large Open-science Open-access Multilingual Language Model with 7,1 billion parameters finetuned on Chinese Translation of Japanese LightNovel using LoRa from PEFT (?)

Model Details

I just downloaded 50 LightNovels then finetuned the model on raw text.

Trained by Rorical

Use

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

peft_model_id = "Rorical/bloom-7b1-lightnovel-lora"
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', cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, cache_dir="cache")
model = PeftModel.from_pretrained(model, peft_model_id, cache_dir="cache")

prompt = "你是谁？\n"
batch = tokenizer(prompt, return_tensors='pt').to("cuda")

with torch.cuda.amp.autocast():
    output_tokens = model.generate(**batch, max_new_tokens=150, do_sample=True, top_k=50, top_p=0.95)

print(tokenizer.decode(output_tokens[0], skip_special_tokens=True))