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--- |
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license: apache-2.0 |
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datasets: |
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- BAAI/IndustryInstruction_Transportation |
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- BAAI/IndustryInstruction |
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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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- 交通运输 |
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- 语言模型 |
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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_Transportation](https://huggingface.co/datasets/BAAI/IndustryInstruction_Transportation) 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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Since I only found an instruction dataset [DUOMO-Lab/Transgpt_sft_v2](https://huggingface.co/datasets/DUOMO-Lab/Transgpt_sft_v2) in the field of traffic, in order to remove the influence of the base model, I used the data in llama3.1-8b-instruc for fine-tuning and compared and evaluated our model. |
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The evaluation method is: use GPT4 on the validation set of each dataset to compare good, tie, and loss. The evaluation results are as follows |
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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/Tranport-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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{"role": "user", "content": "私人交通工具的发展对经济有什么影响？"}, |
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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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) |
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print(response) |
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""" |
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私人交通工具的发展对经济有着深远的影响。首先，私人交通工具的发展可以促进汽车制造业的繁荣。随着私人交通工具的需求增加，汽车制造商将面临更大的市场需求，从而带动产业链的发展，创造就业机会，增加经济收入。其次，私人交通工具的发展也会带动相关 |
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业的发展，如燃料供应、维修服务和保险等。这些行业的发展将为经济增长做出贡献。此外，私人交通工具的发展还会促进城市交通的便利性，提高人们的生活质量，从而带动消费，刺激经济发展。然而，私人交通工具的发展也会带来一些负面影响，如交通拥堵和环境 |
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染等问题。因此，政府需要采取相应的政策措施来平衡经济发展和环境保护的需要。总的来说，私人交通工具的发展对经济有着重要的影响，需要综合考虑各种因素进行合理规划和管理。 |
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""" |
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``` |
