120 lines
4.3 KiB
Markdown
120 lines
4.3 KiB
Markdown
---
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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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``` |