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---
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
```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])
``` |