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--- |
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license: apache-2.0 |
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datasets: |
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- BAAI/IndustryInstruction |
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- BAAI/IndustryInstruction_Hospitality-Catering |
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base_model: |
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- meta-llama/Meta-Llama-3.1-8B-Instruct |
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tags: |
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- hotel |
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- 住宿_餐饮_酒店 |
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- 语言模型 |
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- 中英文 |
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- chatmodel |
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--- |
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This model is finetuned on the model llama3.1-8b-instruct using the dataset [BAAI/IndustryInstruction_Artificial-Intelligence](https://huggingface.co/datasets/BAAI/IndustryInstruction_Artificial-Intelligence) dataset, the dataset details can jump to the repo: [BAAI/IndustryInstruction](https://huggingface.co/datasets/BAAI/IndustryInstruction) |
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## training params |
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The training framework is llama-factory, template=llama3 |
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``` |
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learning_rate=1e-5 |
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lr_scheduler_type=cosine |
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max_length=2048 |
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warmup_ratio=0.05 |
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batch_size=64 |
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epoch=10 |
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``` |
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select best ckpt by the evaluation loss |
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## evaluation |
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Duto to there is no evaluation benchmark, we can not eval the model |
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## How to use |
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```python |
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# !/usr/bin/env python |
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# -*- coding:utf-8 -*- |
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# ================================================================== |
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# [Author] : xiaofeng |
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# [Descriptions] : |
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# ================================================================== |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import transformers |
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import torch |
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llama3_jinja = """{% if messages[0]['role'] == 'system' %} |
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{% set offset = 1 %} |
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{% else %} |
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{% set offset = 0 %} |
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{% endif %} |
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{{ bos_token }} |
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{% for message in messages %} |
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{% if (message['role'] == 'user') != (loop.index0 % 2 == offset) %} |
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{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }} |
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{% endif %} |
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{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] | trim + '<|eot_id|>' }} |
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{% endfor %} |
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{% if add_generation_prompt %} |
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{{ '<|start_header_id|>' + 'assistant' + '<|end_header_id|>\n\n' }} |
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{% endif %}""" |
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dtype = torch.bfloat16 |
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model_dir = "MonteXiaofeng/Hospitality-llama3_1_8B_instruct" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_dir, |
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device_map="cuda", |
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torch_dtype=dtype, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_dir) |
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tokenizer.chat_template = llama3_jinja # update template |
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message = [ |
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{"role": "system", "content": "You are a helpful assistant"}, |
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{ |
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"role": "user", |
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"content": "请举例说明在住宿与餐饮行业中，灵活用工模式的真实运用场景，以及它如何促进从业者的发展。", |
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}, |
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] |
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prompt = tokenizer.apply_chat_template( |
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message, tokenize=False, add_generation_prompt=True |
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) |
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print(prompt) |
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inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") |
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prompt_length = len(inputs[0]) |
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print(f"prompt_length:{prompt_length}") |
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generating_args = { |
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"do_sample": True, |
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"temperature": 1.0, |
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"top_p": 0.5, |
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"top_k": 15, |
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"max_new_tokens": 512, |
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} |
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generate_output = model.generate(input_ids=inputs.to(model.device), **generating_args) |
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response_ids = generate_output[:, prompt_length:] |
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response = tokenizer.batch_decode( |
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response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True |
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)[0] |
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""" |
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灵活用工模式在住宿与餐饮行业中的应用场景主要体现在以下几个方面：首先，酒店和餐厅可以根据业务需求灵活调整员工的工作时间和地点，例如，使用灵活用工模式可以让前台接待员工在客流量高峰期工作更多时间，而在低谷期则可以减少工作量，节省人力成本。其次，灵活用工模式还可以帮助企业进行员工培训和提升，例如，通过在线学习平台，员工可以在非工作时间学习新的技能或知识，提高个人能力。最后，灵活用工模式还可以促进员工的发展，例如，通过灵活调度，员工可以有更多的时间和机会从事更有价值的工作，如客户关系管理、创新项目等，提升个人职业发展路径。 |
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""" |
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print(f"response:{response}") |
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``` |
