Instructions to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Murasaki-Project/Murasaki-14B-v0.2-GGUF",
	filename="Murasaki-14B-v0.2-IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

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 Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

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 Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Murasaki-Project/Murasaki-14B-v0.2-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": "Murasaki-Project/Murasaki-14B-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

Ollama
How to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with Ollama:
```
ollama run hf.co/Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Murasaki-Project/Murasaki-14B-v0.2-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 Murasaki-Project/Murasaki-14B-v0.2-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 Murasaki-Project/Murasaki-14B-v0.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Murasaki-Project/Murasaki-14B-v0.2-GGUF to start chatting

Pi new

How to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M
```

Lemonade

How to use Murasaki-Project/Murasaki-14B-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Murasaki-Project/Murasaki-14B-v0.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Murasaki-14B-v0.2-GGUF-Q4_K_M

List all available models

lemonade list

Murasaki-14B-v0.2 (GGUF)

System 2 Reasoning Model for ACGN Translation
原生 CoT 思维链 · 长上下文 · ACGN 领域特化翻译模型

Github | Benchmark | BF16 Version | License: CC BY-NC-SA 4.0

简介

Murasaki-14B 是专为 ACGN 领域（轻小说、Galgame、漫画等）优化的 System 2 推理型翻译模型。

不同于传统的直觉式（System 1）模型，Murasaki-8B 引入了原生 Chain-of-Thought (CoT) 思维链技术。在生成译文前，模型会先在 <think> 标签内完成风格定调、动作流解析、人设推导及人称确认。这种机制显著提升了长难句的解析精度与叙事连贯性，特别是精准解决了 ACGN 翻译中常见的施动者/受动者判定模糊、人称混淆及语境风格漂移等难点，大幅提升了译文的准确度与可读性。

Prompt 预设说明

本模型针对三种不同场景进行了专项训练。请根据您的翻译对象，在参考文档中选择对应的 System Prompt 使用：

轻小说翻译模式 (Light Novel Mode)
- 适用场景：轻小说正文、Web 小说、文学性较强的文本。
- 特点：注重叙事连贯性、文学风格保留和长上下文关联。
剧本模式翻译 (Script Mode)
- 适用场景：动画字幕、Galgame 脚本、漫画文本。
- 特点：针对对话进行了优化，擅长处理碎片化文本、口语表达，以及大量缺失主语的处理。
短句翻译模式 (Short Sentence Mode)
- 适用场景：UI 文本、系统提示、无上下文的独立短句。
- 特点：去除冗余的思维链联想，直译原意，避免过度脑补。

具体 Prompt 文本请参考 F16版本的介绍页面

文件列表与显存需求

✨ Now Live: 无需下载模型，点击 Online Demo 在线体验模型。

文件名	量化方法	文件大小	推荐显存	适用场景
`Murasaki-14B-v0.2-Q6_K.gguf`	Q6_K	11.29 GB	16GB+	推荐：最佳性能
`Murasaki-14B-v0.2-Q5_K_M.gguf`	Q5_K_M	9.79 GB	12GB+	高精度需求
`Murasaki-14B-v0.2-Q4_K_M.gguf`	Q4_K_M	8.38 GB	10GB+	经典量化
`Murasaki-14B-v0.2-IQ4_XS.gguf`	IQ4_XS	7.55 GB	10GB+	推荐：性价比最优
`Murasaki-14B-v0.2-IQ3_M.gguf`	IQ3_M	6.41 GB	8GB+	极限压缩

快速开始 (GGUF)

方法 1: 使用官方 GUI (推荐)

为了获得最佳的翻译体验（并自动应用上述三种模式），请使用我们配套开发的开源前端翻译GUI(v1.6.0以上版本)： 👉 Murasaki Translator (GitHub)

方法 2: 使用 llama.cpp

./llama-cli -m Murasaki-14B-v0.2-IQ4_XS.gguf \
  -p "[你是一位精通中日双语的资深ACGN翻译家...]" \
  -n 2048 \
  -t 8 \
  --temp 0.3 \
  -c 8192

推理参数建议

Temperature: 0.1 - 0.5 (推荐 0.3)
Repetition Penalty: 从 1.0 开始，如出现复读可增加至 1.05 - 1.1
Max New Tokens: 建议 4096 或更高

协议与致谢

Base Model: 特别感谢 SakuraLLM 提供的优秀 Base 模型。
License: 软件代码遵循 Apache-2.0 协议，模型权重遵循 CC BY-NC-SA 4.0 协议，严禁用于任何商业用途。

Downloads last month: 426

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

Model tree for Murasaki-Project/Murasaki-14B-v0.2-GGUF

Base model

Murasaki-Project/Murasaki-14B-v0.2

Quantized

(1)

this model

Space using Murasaki-Project/Murasaki-14B-v0.2-GGUF 1

Collection including Murasaki-Project/Murasaki-14B-v0.2-GGUF

Murasaki 14B v0.2

Collection

Murasaki-14B v0.2的全量权重与量化版本合集。 • 2 items • Updated Mar 14