metadata

license: bigscience-bloom-rail-1.0
language:
  - vi
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - bloom
  - causal-lm
  - pytorch
model-index:
  - name: vlsp-2023-vllm/hoa-1b4
    results:
      - task:
          name: Word prediction
          type: text-generation
        dataset:
          type: vlsp-2023-vllm/vi_lambada
          name: vi_lambada
          split: test
        metrics:
          - type: Perplexity
            value: 8.110657542682734
datasets:
  - vlsp-2023-vllm/vi_lambada
metrics:
  - perplexity

Hoa 1B4 (Bloom architecture)

Hoa is an autoregressive Large Language Model (LLM), based on Bloom's model architecture. Hoa was trained on part of the Common Crawl dataset in Vietnamese and English.

Details will be available soon.

To contact us, mail to: leanhcuong@gmail.com (Lê Anh Cường) | hieunguyen1053@outlook.com (Hiếu) | nv.cuong@int2.vn (Nguyễn Việt Cường)

How to use

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vlsp-2023-vllm/hoa-1b4")
model = AutoModelForCausalLM.from_pretrained("vlsp-2023-vllm/hoa-1b4", low_cpu_mem_usage=True)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
model.to(device)

prompt = "Địa chỉ trường Đại học Tôn Đức Thắng nằm ở số"
input_ids = tokenizer(prompt, return_tensors="pt")['input_ids'].to(device)

gen_tokens = model.generate(input_ids, max_length=max_length, repetition_penalty=1.1)

print(tokenizer.batch_decode(gen_tokens)[0])