hoa-1b4/README.md

license, language, library_name, pipeline_tag, tags, model-index, datasets, metrics

license	language	library_name	pipeline_tag	tags	model-index	datasets	metrics
bigscience-bloom-rail-1.0	vi en	transformers	text-generation	bloom causal-lm pytorch	name results vlsp-2023-vllm/hoa-1b4 task dataset metrics name type Word prediction text-generation type name split vlsp-2023-vllm/vi_lambada vi_lambada test type value Perplexity 8.606673731963474 task dataset metrics name type Fewshot Translation translation type name split vlsp-2023-vllm/en-to-vi-formal-informal-tranlations English to Vietnamese Formal/Informal translation test type value SacreBLEU 25.5	vlsp-2023-vllm/vi_lambada	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])