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: 8.606673731963474
- task: name: Fewshot Translation type: translation dataset: type: vlsp-2023-vllm/en-to-vi-formal-informal-tranlations name: English to Vietnamese Formal/Informal translation split: test metrics:
  - type: SacreBLEU value: 25.5 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

python

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])

RichardErkhov/vlsp-2023-vllm_-_hoa-1b4-4bits

Original model description:

Hoa 1B4 (Bloom architecture)

How to use

RichardErkhov/
vlsp-2023-vllm_-_hoa-1b4-4bits