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Model: BAAI/Tranport-llama3_1_8B_instruct
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- BAAI/IndustryInstruction_Transportation
+- BAAI/IndustryInstruction
+base_model:
+- meta-llama/Meta-Llama-3.1-8B-Instruct
+tags:
+- 交通运输
+- 语言模型
+- chatmodel
+---
+
+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)
+
+## training params
+
+The training framework is llama-factory, template=llama3
+
+```
+learning_rate=1e-5
+lr_scheduler_type=cosine
+max_length=2048
+warmup_ratio=0.05
+batch_size=64
+epoch=10
+```
+
+select best ckpt by the evaluation loss
+## evaluation
+
+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.
+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
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/642f6c64f945a8a5c9ee5b5d/c2GzApj4LlyETZ7ApPHx1.png)
+
+
+## How to use
+
+```python
+# !/usr/bin/env python
+# -*- coding:utf-8 -*-
+# ==================================================================
+# [Author]       : xiaofeng
+# [Descriptions] :
+# ==================================================================
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+
+llama3_jinja = """{% if messages[0]['role'] == 'system' %}
+    {% set offset = 1 %}
+{% else %}
+    {% set offset = 0 %}
+{% endif %}
+
+{{ bos_token }}
+{% for message in messages %}
+    {% if (message['role'] == 'user') != (loop.index0 % 2 == offset) %}
+        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
+    {% endif %}
+
+    {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] | trim + '<|eot_id|>' }}
+{% endfor %}
+
+{% if add_generation_prompt %}
+    {{ '<|start_header_id|>' + 'assistant' + '<|end_header_id|>\n\n' }}
+{% endif %}"""
+
+
+dtype = torch.bfloat16
+
+model_dir = "MonteXiaofeng/Tranport-llama3_1_8B_instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_dir,
+    device_map="cuda",
+    torch_dtype=dtype,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+tokenizer.chat_template = llama3_jinja # update template
+
+message = [
+    {"role": "system", "content": "You are a helpful assistant"},
+    {"role": "user", "content": "私人交通工具的发展对经济有什么影响？"},
+]
+prompt = tokenizer.apply_chat_template(
+    message, tokenize=False, add_generation_prompt=True
+)
+print(prompt)
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+prompt_length = len(inputs[0])
+print(f"prompt_length:{prompt_length}")
+
+generating_args = {
+    "do_sample": True,
+    "temperature": 1.0,
+    "top_p": 0.5,
+    "top_k": 15,
+    "max_new_tokens": 512,
+}
+
+
+generate_output = model.generate(input_ids=inputs.to(model.device), **generating_args)
+
+response_ids = generate_output[:, prompt_length:]
+response = tokenizer.batch_decode(
+    response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
+)
+print(response)
+"""
+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
+  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
+  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。
+"""
+
+```