README.md

metadata

datasets:
  - cahya/instructions-all
license: bigscience-bloom-rail-1.0
language:
  - de
  - en
  - es
  - fr
  - hi
  - id
  - ja
  - ms
  - pt
  - ru
  - th
  - vi
  - zh
pipeline_tag: text-generation
widget:
  - text: >-
      一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the
      previous review as positive, neutral or negative?
    example_title: zh-en sentiment
  - text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？
    example_title: zh-zh sentiment
  - text: Suggest at least five related search terms to "Mạng neural nhân tạo".
    example_title: vi-en query
  - text: >-
      Proposez au moins cinq mots clés concernant «Réseau de neurones
      artificiels».
    example_title: fr-fr query
  - text: >-
      Explain in a sentence in Telugu what is backpropagation in neural
      networks.
    example_title: te-en qa
  - text: Why is the sky blue?
    example_title: en-en qa
  - text: >-
      Write a fairy tale about a troll saving a princess from a dangerous
      dragon. The fairy tale is a masterpiece that has achieved praise worldwide
      and its moral is "Heroes Come in All Shapes and Sizes". Story (in
      Spanish):
    example_title: es-en fable
  - text: >-
      Write a fable about wood elves living in a forest that is suddenly invaded
      by ogres. The fable is a masterpiece that has achieved praise worldwide
      and its moral is "Violence is the last refuge of the incompetent". Fable
      (in Hindi):
    example_title: hi-en fable

Bloomz-7b1-instruct

This is Bloomz-7b1-mt model fine-tuned with multilingual instruction dataset and using Peft Lora fine-tuning. Following languages are supported: English, German, French, Spanish, Hindi, Indonesian, Japanese, Malaysian, Portuguese, Russian, Thai, Vietnamese and Chinese.

Usage

Following is the code to do the inference using this model:

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

peft_model_id = "cahya/bloomz-7b1-instruct"
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(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)

batch = tokenizer("User: How old is the universe?\nAssistant: ", return_tensors='pt').to(0)


with torch.cuda.amp.autocast():
  output_tokens = model.generate(**batch, max_new_tokens=200,
                                 min_length=50,
                                 do_sample=True,
                                 top_k=40,
                                 top_p=0.9,
                                 temperature=0.2,
                                 repetition_penalty=1.2,
                                 num_return_sequences=1)

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