Text Generation
Safetensors
Chinese
English
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  ---
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  license: apache-2.0
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ datasets:
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+ - shareAI/ShareGPT-Chinese-English-90k
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+ language:
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+ - zh
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+ - en
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+ pipeline_tag: text-generation
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  ---
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+ ![](./assets/aurora.png)
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+
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+ <div align="center">
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+ <h2>
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+ Aurora: Activating chinese chat capability for Mistral-8x7B sparse Mixture-of-Experts through Instruction-Tuning
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+ </h2>
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+ </div>
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+
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+ 1. <h1>Please follow our Github: <a href="https://github.com/WangRongsheng/Aurora">https://github.com/WangRongsheng/Aurora</a></h1>
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+ 2. <h1>Please follow our Paper: <a href="https://arxiv.org/abs/2312.14557">https://arxiv.org/abs/2312.14557</a></h1>
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+
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+ ## Overview
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+
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+ Existing research has demonstrated that refining large language models (LLMs) through the utilization of machine-generated instruction-following data empowers these models to exhibit impressive zero-shot capabilities for novel tasks, without requiring human-authored instructions. In this paper, we systematically investigate, preprocess, and integrate three Chinese instruction-following datasets with the aim of enhancing the Chinese conversational capabilities of Mixtral-8x7B sparse Mixture-of-Experts model. Through instruction fine-tuning on this carefully processed dataset, we successfully construct the Mixtral-8x7B sparse Mixture-of-Experts model named "Aurora." To assess the performance of Aurora, we utilize three widely recognized benchmark tests: C-Eval, MMLU, and CMMLU. Empirical studies validate the effectiveness of instruction fine-tuning applied to Mixtral-8x7B sparse Mixture-of-Experts model. This work is pioneering in the execution of instruction fine-tuning on a sparse expert-mixed model, marking a significant breakthrough in enhancing the capabilities of this model architecture.
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+
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+ ![](./training_loss.png)
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+
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+ ## Usage
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+
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+ ```python
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+ import gradio as gr
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
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+ from threading import Thread
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+ from peft import PeftModel
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+ import time
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+
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+ model_name_or_path = "mistralai/Mixtral-8x7B-Instruct-v0.1" # download weights from https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
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+ lora_weights = "wangrongsheng/Aurora" # download weights from https://huggingface.co/wangrongsheng/Aurora
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
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+ model0 = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_4bit=True, device_map="auto", torch_dtype=torch.bfloat16)
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+ model = PeftModel.from_pretrained(
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+ model0,
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+ lora_weights,
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+ )
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+
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+ class StopOnTokens(StoppingCriteria):
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+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
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+ stop_ids = [0,]
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+ for stop_id in stop_ids:
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+ if input_ids[0][-1] == stop_id:
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+ return True
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+ return False
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+
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+ def convert_history_to_text(history):
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+ text = ""
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+ if len(history) > 1:
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+ text = "<s> " + "".join(
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+ [
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+ "".join(
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+ [
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+ f"[INST]{item[0]}[/INST] {item[1]} ",
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+ ]
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+ )
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+ for item in history[:-1]
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+ ]
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+ ) + "</s> "
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+ text += "".join(
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+ [
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+ "".join(
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+ [
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+ f"[INST]{history[-1][0]}[/INST]",
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+ ]
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+ )
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+ ]
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+ )
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+ return text
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+
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+ def predict(message, history):
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+
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+ history_transformer_format = history + [[message, ""]]
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+ stop = StopOnTokens()
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+
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+ messages = convert_history_to_text(history_transformer_format)
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+
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+ model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
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+ streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
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+ generate_kwargs = dict(
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+ model_inputs,
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+ streamer=streamer,
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+ max_new_tokens=4096,
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+ do_sample=True,
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+ top_p=0.95,
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+ top_k=1000,
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+ temperature=1.0,
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+ num_beams=1,
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+ pad_token_id=tokenizer.eos_token_id,
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+ stopping_criteria=StoppingCriteriaList([stop])
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+ )
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+ t = Thread(target=model.generate, kwargs=generate_kwargs)
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+ t.start()
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+
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+ partial_message = ""
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+ t1 = time.time()
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+ count = 0
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+ for new_token in streamer:
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+ if new_token != '<':
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+ partial_message += new_token
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+ count += 1
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+ yield partial_message
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+ t2 = time.time()
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+ speed = count/(t2-t1)
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+ print("inference speed: %f tok/s" % speed)
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+
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+
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+ gr.ChatInterface(predict,chatbot=gr.Chatbot(height=600,),title="MoE").queue().launch()
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+ ```
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+
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+ ## Citation
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+ If you find our work helpful, feel free to give us a cite.
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+ ```latex
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+ @misc{wang2023auroraactivating,
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+ title={Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning},
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+ author={Rongsheng Wang and Haoming Chen and Ruizhe Zhou and Yaofei Duan and Kunyan Cai and Han Ma and Jiaxi Cui and Jian Li and Patrick Cheong-Iao Pang and Yapeng Wang and Tao Tan},
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+ year={2023},
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+ eprint={2312.14557},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```