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cahya
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bloomz-7b1-instruct

Text Generation
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bloomz-7b1-instruct
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---
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
- 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))
```