File size: 1,788 Bytes
46a6322 cdb8786 241ed54 cdb8786 534cb9b dc7d2de 241ed54 46a6322 9a2fcc2 46a6322 241ed54 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
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
- 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: 25.9
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])
``` |