Instructions to use lordjia/wawj-14b-chat-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lordjia/wawj-14b-chat-gguf 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 lordjia/wawj-14b-chat-gguf:Q4_0
# Run inference directly in the terminal:
llama cli -hf lordjia/wawj-14b-chat-gguf:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lordjia/wawj-14b-chat-gguf:Q4_0
# Run inference directly in the terminal:
llama cli -hf lordjia/wawj-14b-chat-gguf:Q4_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 lordjia/wawj-14b-chat-gguf:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf lordjia/wawj-14b-chat-gguf:Q4_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 lordjia/wawj-14b-chat-gguf:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lordjia/wawj-14b-chat-gguf:Q4_0

Use Docker

docker model run hf.co/lordjia/wawj-14b-chat-gguf:Q4_0

LM Studio
Jan

vLLM

How to use lordjia/wawj-14b-chat-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lordjia/wawj-14b-chat-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lordjia/wawj-14b-chat-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lordjia/wawj-14b-chat-gguf:Q4_0

Ollama
How to use lordjia/wawj-14b-chat-gguf with Ollama:
```
ollama run hf.co/lordjia/wawj-14b-chat-gguf:Q4_0
```

Unsloth Studio

How to use lordjia/wawj-14b-chat-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lordjia/wawj-14b-chat-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lordjia/wawj-14b-chat-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lordjia/wawj-14b-chat-gguf to start chatting

Docker Model Runner
How to use lordjia/wawj-14b-chat-gguf with Docker Model Runner:
```
docker model run hf.co/lordjia/wawj-14b-chat-gguf:Q4_0
```

Lemonade

How to use lordjia/wawj-14b-chat-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lordjia/wawj-14b-chat-gguf:Q4_0

Run and chat with the model

lemonade run user.wawj-14b-chat-gguf-Q4_0

List all available models

lemonade list

Atomic Chat

北京方言对话模型

基于 Qwen1.5/2 大模型微调的北京方言对话模型，提供口语化的北京方言对话体验。项目包括 7B 和 14B 版本，使用北京方言情景喜剧台词微调。

模型描述

7B 版本 (wawj-7b-chat-gguf): 中等大小模型，适用于平衡性能和质量。基于 Qwen1.5-7B-Chat 微调。
7B 版本 (wawj-7b-instruct-gguf): 中等大小模型，适用于平衡性能和质量。基于 Qwen2-7B-Instruct 微调。
14B 版本 (wawj-14b-chat-gguf): 大模型，优化了口语中的幽默感，适用于高质量生成。基于 Qwen1.5-14B-Chat 微调。

使用指南

在 LM Studio 使用

在 LM Studio 中搜索 lordjia/wawj 直接使用模型，无需手动下载。设置 System Prompt 为：

以口语化的北京方言与用户对话。

在 Ollama 使用

在 Ollama 中，您可以直接使用已经提供的 Modelfile 来创建模型实例，无需进行额外设置。

模型链接

Downloads last month: 7

GGUF

Model size

14B params

Architecture

qwen2

Hardware compatibility

4-bit