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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 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))
``` |