README.md

---
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-7b
  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 7B (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-7b")
model = AutoModelForCausalLM.from_pretrained("vlsp-2023-vllm/hoa-7b", 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])
```