--- library_name: peft pipeline_tag: conversational datasets: - fnlp/moss-003-sft-data ---
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## Model Llama-2-7b-qlora-moss-003-sft is fine-tuned from [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) with [moss-003-sft](https://huggingface.co/datasets/fnlp/moss-003-sft-data) dataset by [XTuner](https://github.com/InternLM/xtuner). ## Quickstart ### Usage with HuggingFace libraries ```python 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('meta-llama/Llama-2-7b-hf', 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('meta-llama/Llama-2-7b-hf', quantization_config=quantization_config, device_map='auto', trust_remote_code=True).eval() model = PeftModel.from_pretrained(model, 'xtuner/Llama-2-7b-qlora-moss-003-sft') gen_config = GenerationConfig(max_new_tokens=1024, 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 Llama2.\n' 'Capabilities and tools that Llama2 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}\n' '<|Inner Thoughts|>: None\n' '<|Commands|>: None\n' '<|Results|>: None\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, '')]) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) """ 好的,以下是五个上海的景点: 1. 外滩:外滩是上海的标志性景点之一,是一条长达1.5公里的沿江大道,沿途有许多历史建筑和现代化的高楼大厦。游客可以欣赏到黄浦江两岸的美景,还可以在这里拍照留念。 2. 上海博物馆:上海博物馆是上海市最大的博物馆之一,收藏了大量的历史文物和艺术品。博物馆内有许多展览,包括中国古代文物、近代艺术品和现代艺术品等。 3. 上海科技馆:上海科技馆是一座以科技为主题的博物馆,展示了许多科技产品和科技发展的历史。游客可以在这里了解到许多有趣的科技知识,还可以参加一些科技体验活动。 4. 上海迪士尼乐园:上海迪士尼乐园是中国第一个迪士尼乐园,是一个集游乐、购物、餐饮、娱乐等多种功能于一体的主题公园。游客可以在这里体验到迪士尼的经典故事和游乐设施。 5. 上海野生动物园:上海野生动物园是一座以野生动物观赏和保护为主题的大型动物园。它位于上海市浦东新区,是中国最大的野生动物园之一。 """ ``` ### Usage with XTuner CLI #### Installation ```shell pip install xtuner ``` #### Chat ```shell xtuner chat hf meta-llama/Llama-2-7b-hf --adapter xtuner/Llama-2-7b-qlora-moss-003-sft --bot-name Llama2 --prompt-template moss_sft --with-plugins calculate solve search --command-stop-word "" --answer-stop-word "" --no-streamer ``` #### Fine-tune Use the following command to quickly reproduce the fine-tuning results. ```shell NPROC_PER_NODE=8 xtuner train llama2_7b_qlora_moss_sft_all_e2_gpu8 ```