Instructions to use Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ" # 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-APE-Aligner-2B-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ
- SGLang
How to use Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ with Docker Model Runner:
docker model run hf.co/Murasaki-Project/Murasaki-APE-Aligner-2B-AWQ
Murasaki-APE-Aligner-2B-AWQ
Github | Murasaki Translator | License: MIT
Other Versions: Base (BF16) | GGUF
简介
Murasaki-APE-Aligner-2B-AWQ 是格式修复引擎 Murasaki-APE-Aligner-2B 的 4-bit AWQ 量化版本(约 2.42 GB)。该版本在极大地降低显存占用并提升生成速度的同时,保持了与原始精度几乎一致的修复能力。完美支持 vLLM 等高并发推理框架,是具有显存限制但需要高速批量处理时的首选版本。
训练数据与核心能力: 为了应对极其复杂的真实游戏文本环境,本模型使用了涵盖 40 种以上 Galgame 与 RPG 游戏引擎、超过 1000 种不同格式的游戏控制符数据进行针对性训练。模型能够处理绝大多数控制符错位、丢失、冗余或参数被错误翻译的问题,并稳定输出逻辑正确、语法及代码完全正确的文本。
核心机制: 读取包含原文与模型初翻的 JSON 格式输入,自动对齐并修复草稿中损坏的控制代码。模型仅处理代码与格式规范,不会改动任何译文的具体内容。修复完成后直接输出纯文本字符串。
核心 Prompt 与输入输出格式
请使用以下格式激活模型的修复与对齐能力:
1. System Prompt
你是一个格式修复引擎。请读取JSON输入的原文和草稿,修复草稿中的控制符和格式,直接输出修复后的字符串,不要输出任何额外的JSON标签或解释。
2. User Input
必须为包含 ja (原文) 和 zh (译文初翻) 的 JSON 字符串:
{"ja": "その意思は[font color=0x64E560]尊重[resetfont]したいが……", "zh": "我很想[font color=0x64E560]尊重[resetfont][resetfont]你的意志……"}
3. Model Output
模型将直接返回修复后的纯文本,无任何多余的 JSON 嵌套或解释:
我很想[font color=0x64E560]尊重[resetfont]你的意志……
建议超参数 (Recommended Hyperparameters)
推荐参数如下:
- temperature:
0.0~0.1(越低越稳定) - top_p:
1.0 - top_k:
1(或禁用) - repetition_penalty:
1.0~1.05
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Murasaki-Project/Murasaki-APE-Aligner-2B