Instructions to use LiuShisan123/CustomerServiceSystem_GGUF_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiuShisan123/CustomerServiceSystem_GGUF_7B with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LiuShisan123/CustomerServiceSystem_GGUF_7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use LiuShisan123/CustomerServiceSystem_GGUF_7B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

LM Studio
Jan
Ollama
How to use LiuShisan123/CustomerServiceSystem_GGUF_7B with Ollama:
```
ollama run hf.co/LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use LiuShisan123/CustomerServiceSystem_GGUF_7B with Docker Model Runner:
```
docker model run hf.co/LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0
```

Lemonade

How to use LiuShisan123/CustomerServiceSystem_GGUF_7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiuShisan123/CustomerServiceSystem_GGUF_7B:Q8_0

Run and chat with the model

lemonade run user.CustomerServiceSystem_GGUF_7B-Q8_0

List all available models

lemonade list

Atomic Chat

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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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

8-bit

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Model tree for LiuShisan123/CustomerServiceSystem_GGUF_7B

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Quantized

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