metadata
license: llama2
datasets:
- tatsu-lab/alpaca
- OpenAssistant/oasst1
language:
- zh
- en
library_name: transformers
tags:
- baichuan
- lora
pipeline_tag: text-generation
inference: false
A bilingual instruction-tuned LoRA model of https://huggingface.co/meta-llama/Llama-2-13b-hf
- Instruction-following datasets used: alpaca, alpaca-zh, open assistant
- Training framework: https://github.com/hiyouga/LLaMA-Efficient-Tuning
Usage:
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
tokenizer = AutoTokenizer.from_pretrained("hiyouga/Llama-2-Chinese-13b-chat")
model = AutoModelForCausalLM.from_pretrained("hiyouga/Llama-2-Chinese-13b-chat").cuda()
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
query = "晚上睡不着怎么办"
template = (
"A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
"Human: {}\nAssistant: "
)
inputs = tokenizer([template.format(query)], return_tensors="pt")
inputs = inputs.to("cuda")
generate_ids = model.generate(**inputs, max_new_tokens=256, streamer=streamer)
You could also alternatively launch a CLI demo by using the script in https://github.com/hiyouga/LLaMA-Efficient-Tuning
python src/cli_demo.py --model_name_or_path hiyouga/Llama-2-Chinese-13b-chat
Loss curve:
