metadata
library_name: peft
pipeline_tag: conversational
datasets:
- fnlp/moss-003-sft-data
Model
Qwen-7B-qlora-moss-003-sft is fine-tuned from Qwen-7B with moss-003-sft dataset by XTuner.
Quickstart
Usage with HuggingFace libraries
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, StoppingCriteria
from transformers.generation import GenerationConfig
class StopWordStoppingCriteria(StoppingCriteria):
def __init__(self, tokenizer, stop_word):
self.tokenizer = tokenizer
self.stop_word = stop_word
self.length = len(self.stop_word)
def __call__(self, input_ids, *args, **kwargs) -> bool:
cur_text = self.tokenizer.decode(input_ids[0])
cur_text = cur_text.replace('\r', '').replace('\n', '')
return cur_text[-self.length:] == self.stop_word
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B', trust_remote_code=True)
quantization_config = BitsAndBytesConfig(load_in_4bit=True, load_in_8bit=False, llm_int8_threshold=6.0, llm_int8_has_fp16_weight=False, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4')
model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen-7B', quantization_config=quantization_config, device_map='auto', trust_remote_code=True).eval()
model = PeftModel.from_pretrained(model, 'xtuner/Qwen-7B-qlora-moss-003-sft')
gen_config = GenerationConfig(max_new_tokens=512, do_sample=True, temperature=0.1, top_p=0.75, top_k=40)
# Note: In this example, we disable the use of plugins because the API depends on additional implementations.
# If you want to experience plugins, please refer to XTuner CLI!
prompt_template = (
'You are an AI assistant whose name is Qwen.\n'
'Capabilities and tools that Qwen can possess.\n'
'- Inner thoughts: disabled.\n'
'- Web search: disabled.\n'
'- Calculator: disabled.\n'
'- Equation solver: disabled.\n'
'- Text-to-image: disabled.\n'
'- Image edition: disabled.\n'
'- Text-to-speech: disabled.\n'
'<|Human|>: {input}<eoh>\n'
'<|Inner Thoughts|>: None<eot>\n'
'<|Commands|>: None<eoc>\n'
'<|Results|>: None<eor>\n')
text = '请给我介绍五个上海的景点'
inputs = tokenizer(prompt_template.format(input=text), return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs, generation_config=gen_config, stopping_criteria=[StopWordStoppingCriteria(tokenizer, '<eom>')])
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
"""
好的，以下是五个上海的景点介绍：
1. 上海博物馆：上海博物馆是中国最大的综合性博物馆之一，收藏了大量的历史文物和艺术品，包括青铜器、陶瓷、书画、玉器等。
2. 上海城隍庙：上海城隍庙是上海最古老的庙宇之一，建于明朝，是上海的标志性建筑之一。庙内有各种神像和文物，是了解上海历史文化的好去处。
3. 上海科技馆：上海科技馆是一座集科技、文化、教育为一体的综合性博物馆，展示了各种科技展品和互动体验项目，适合全家人一起参观。
4. 上海东方明珠塔：上海东方明珠塔是上海的标志性建筑之一，高468米。游客可以乘坐高速电梯到达观景台，欣赏上海的美景。
5. 上海迪士尼乐园：上海迪士尼乐园是中国第一个迪士尼主题公园，拥有各种游乐设施和表演节目，适合全家人一起游玩。
"""
Usage with XTuner CLI
Installation
pip install -U xtuner
Chat
export SERPER_API_KEY="xxx" # Please get the key from https://serper.dev to support google search!
xtuner chat Qwen/Qwen-7B --adapter xtuner/Qwen-7B-qlora-moss-003-sft --bot-name Qwen --prompt-template moss_sft --system-prompt moss_sft --with-plugins calculate solve search
Fine-tune
Use the following command to quickly reproduce the fine-tuning results.
NPROC_PER_NODE=8 xtuner train qwen_7b_qlora_moss_sft_all_e2_gpu8