license: other
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We opensource our Aquila2 series, now including Aquila2, the base language models, namely Aquila2-7B and Aquila2-34B, as well as AquilaChat2, the chat models, namely AquilaChat2-7B and AquilaChat2-34B, 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.
2023.10.25 🔥 AquilaChat2-34B-16K v1.2 is based on the previous AquilaChat2-34B-16K. The AquilaChat2-34B-16K-V1.2 has significantly improved long-text synthesis capabilities compared to the V1 version, approaching the level of GPT-3.5-16K. Additionally, the V1.2 version incorporates more conventional instruction fine-tuning corpora, enhancing its performance in non-long-text scenarios compared to the V1 version.
Quick Start AquilaChat2-34B-16K（Chat model）
1. Inference
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = torch.device("cuda:0")
model_info = "BAAI/AquilaChat2-34B-16k"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True, torch_dtype=torch.bfloat16,
# quantization_config=quantization_config, # Uncomment this line for 4bit quantization
)
model.eval()
model.to(device)
text = "请给出10个要到北京旅游的理由。"
from predict import predict
out = predict(model, text, tokenizer=tokenizer, max_gen_len=200, top_p=0.95,
seed=1234, topk=100, temperature=0.9, sft=True, device=device,
model_name="AquilaChat2-34B-16K")
print(out)
License
Aquila2 series open-source model is licensed under BAAI Aquila Model Licence Agreement