We opensource our
Aquila2
series, now including
Aquila2
, the base language models, namely
Aquila2-7B
,
Aquila2-34B
and
Aquila2-70B-Expr
, as well as
AquilaChat2
, the chat models, namely
AquilaChat2-7B
,
AquilaChat2-34B
and
AquilaChat2-70B-Expr
, as well as the long-text chat models, namely
AquilaChat2-7B-16k
and
AquilaChat2-34B-16k
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels.
Quick Start
1. Inference
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import BitsAndBytesConfig
model_info = "BAAI/Aquila2-70B-Expr"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True)
model.eval()
text = "请给出10个要到北京旅游的理由。"
tokens = tokenizer.encode_plus(text)['input_ids']
tokens = torch.tensor(tokens)[None,].to(device)
stop_tokens = ["###", "[UNK]", "</s>"]
with torch.no_grad():
out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007, bad_words_ids=[[tokenizer.encode(token)[0] for token in stop_tokens]])[0]
out = tokenizer.decode(out.cpu().numpy().tolist())
print(out)
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