How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
# Run inference directly in the terminal:
llama-cli -hf LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
# Run inference directly in the terminal:
llama-cli -hf LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
Use Docker
docker model run hf.co/LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
Quick Links

Model Description

此模型是基于京东电商客服对话数据集微调而成的客服模型,旨在实现AI模型对用户问题作出针对性回答。

Base Model

基础模型:DeepSeek-R1-Distill-Qwen-7B
微调方法:LoRA

Datasets

数量:使用 6 万条中文客服对话数据,格式为 SFT 格式,每条数据包含多轮问答,覆盖电商、快递、客服常见场景。
来源:https://github.com/SimonJYang/JDDC-Baseline-Seq2Seq

Limitations

经过测试,该gguf格式模型使用llama cpp加载后,所有问题都是生成一样的答案,但是safetensors的就不会,目前还没搞懂什么情况,有兴趣的可以尝试加载一下。
不可商用以及任何非法用途,仅供交流学习使用!

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