Instructions to use zhatrix/logistics-qwen-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use zhatrix/logistics-qwen-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "zhatrix/logistics-qwen-lora") - Notebooks
- Google Colab
- Kaggle
物流通 · Logistics LoRA for Qwen2.5-7B-Instruct
物流行业智能助手的 LoRA 适配器(PEFT 格式)。配合知识库检索与业务工具使用,覆盖客服问答、单据/地址抽取、路径调度、行业知识四类场景。
- 基座:
Qwen/Qwen2.5-7B-Instruct - LoRA:r=8,alpha=160,作用于后 16 层的 q/k/v/o/gate/up/down
- 训练:mlx_lm LoRA 200 步,1435 条合成样本(工具轨迹由真实工具执行生成,知识问答由教师模型生成,训练 prompt 与线上一致:含检索上下文与工具定义)
- 评测:项目自带 20 例规则评测,基座 0.825 → LoRA 0.880(抽取任务 0 → 0.8)
使用
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "Qwen/Qwen2.5-7B-Instruct"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, "zhatrix/logistics-qwen-lora")
模型以 Qwen chat template 的工具调用格式工作(tools 传入模板,输出 <tool_call>…</tool_call>)。在线演示:https://huggingface.co/spaces/zhatrix/logistics-llm | ModelScope 镜像:https://www.modelscope.cn/models/zh4trix/logistics-qwen-lora | 完整应用见 GitHub:https://github.com/zhatrix/logicLLM
局限
- 演示用途;运单/网点等数据为模拟数据。
- 7B 模型数值比较偏弱(如"53 度 vs 70%");未指明服务类型时可能自行假设。
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