Instructions to use MoYoYoTech/Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MoYoYoTech/Translator with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MoYoYoTech/Translator", filename="moyoyo_asr_models/qwen2.5-1.5b-instruct-q5_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use MoYoYoTech/Translator with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MoYoYoTech/Translator:Q5_0 # Run inference directly in the terminal: llama-cli -hf MoYoYoTech/Translator:Q5_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MoYoYoTech/Translator:Q5_0 # Run inference directly in the terminal: llama-cli -hf MoYoYoTech/Translator:Q5_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 MoYoYoTech/Translator:Q5_0 # Run inference directly in the terminal: ./llama-cli -hf MoYoYoTech/Translator:Q5_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 MoYoYoTech/Translator:Q5_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf MoYoYoTech/Translator:Q5_0
Use Docker
docker model run hf.co/MoYoYoTech/Translator:Q5_0
- LM Studio
- Jan
- Ollama
How to use MoYoYoTech/Translator with Ollama:
ollama run hf.co/MoYoYoTech/Translator:Q5_0
- Unsloth Studio new
How to use MoYoYoTech/Translator 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 MoYoYoTech/Translator 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 MoYoYoTech/Translator to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MoYoYoTech/Translator to start chatting
- Pi new
How to use MoYoYoTech/Translator with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf MoYoYoTech/Translator:Q5_0
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": "MoYoYoTech/Translator:Q5_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use MoYoYoTech/Translator with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf MoYoYoTech/Translator:Q5_0
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 MoYoYoTech/Translator:Q5_0
Run Hermes
hermes
- Docker Model Runner
How to use MoYoYoTech/Translator with Docker Model Runner:
docker model run hf.co/MoYoYoTech/Translator:Q5_0
- Lemonade
How to use MoYoYoTech/Translator with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MoYoYoTech/Translator:Q5_0
Run and chat with the model
lemonade run user.Translator-Q5_0
List all available models
lemonade list
Merge branch 'vad'
Browse files* vad:
update english prompt
[fix]: upate web.
- config/prompt.py +23 -9
config/prompt.py
CHANGED
|
@@ -16,21 +16,35 @@ hotwords_json = json.loads((CONFIG_DIR / 'hotwords.json').read_text())
|
|
| 16 |
|
| 17 |
# 翻译提示词
|
| 18 |
keywords_list = [i.strip() for i in (CONFIG_DIR / 'keywords.txt').read_text().split('\n') if i.strip()]
|
| 19 |
-
keywords_mapping_string = '\n'.join([f' * {value}' for value in keywords_list ])
|
| 20 |
|
| 21 |
LLM_SYS_7B_PROMPT_EN = """
|
| 22 |
-
你是一
|
| 23 |
-
你只需要翻译文本内容,不要做任何解释,也不要进行任何问答。
|
| 24 |
|
| 25 |
-
|
| 26 |
-
- 注意翻译时保留术语,例如 FLAC,JPEG 等。保留公司缩写,例如 Microsoft, Amazon, OpenAI 等;
|
| 27 |
-
- 人物的名称不需要翻译;
|
| 28 |
-
- 在翻译专业术语时,第一次出现时要在括号里面写上英文原文,例如:“生成式 AI (Generative AI)”,之后就可以只写中文了;
|
| 29 |
-
- 以下是常见的AI相关术语,这部分的术语不需要翻译;
|
| 30 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 31 |
{keywords_mapping_string}
|
| 32 |
|
| 33 |
-
文
|
|
|
|
|
|
|
|
|
|
|
|
|
| 34 |
""".format(keywords_mapping_string=keywords_mapping_string)
|
| 35 |
|
| 36 |
LLM_SYS_7B_PROMPT_ZH = """
|
|
|
|
| 16 |
|
| 17 |
# 翻译提示词
|
| 18 |
keywords_list = [i.strip() for i in (CONFIG_DIR / 'keywords.txt').read_text().split('\n') if i.strip()]
|
| 19 |
+
keywords_mapping_string = '\n'.join([f' * {value}: {value}' for value in keywords_list ])
|
| 20 |
|
| 21 |
LLM_SYS_7B_PROMPT_EN = """
|
| 22 |
+
你是一名专业的同声传译员,正在为 GOSIM 会议提供中英/英中翻译服务。你的任务是准确、流畅地翻译发言内容。
|
|
|
|
| 23 |
|
| 24 |
+
请遵循以下要求:
|
|
|
|
|
|
|
|
|
|
|
|
|
| 25 |
|
| 26 |
+
1. **语言风格:** 翻译成中文时,请使用自然、流畅、符合现代汉语口语习惯的表达方式。避免生硬、逐字翻译的痕迹,要让听众容易理解。
|
| 27 |
+
2. **专业术语:** 必须准确保留或翻译计算机相关的专业术语和技术词汇**请优先参考下方提供的术语对照表进行翻译。** 对于对照表中未包含的术语,如果该术语有公认的标准翻译,请使用标准翻译;如果没有或不确定,可以保留英文原文或提供最贴切的翻译。不要用通俗词汇替代专业术语。
|
| 28 |
+
3. **专有名词:** 对于专有名词,如会议名称 "GOSIM"、人名、公司名、项目名、特定技术名称等,请保留其原始英文不做翻译。优先保持一致性和清晰度。
|
| 29 |
+
4. **流畅性与准确性:** 在追求口语化的同时,务必保证信息传达的准确性。
|
| 30 |
+
5. **输出:** 请直接输出翻译结果,不要添加任何额外的解释或说明。
|
| 31 |
+
|
| 32 |
+
**专业术语对照表(请优先使用此表中的翻译):**
|
| 33 |
+
* Simulation: 仿真
|
| 34 |
+
* Modeling: 建模
|
| 35 |
+
* driver: 驱动
|
| 36 |
+
* bus: 总线
|
| 37 |
+
* mask: 掩码
|
| 38 |
+
* preemption: 抢占
|
| 39 |
+
* register: 寄存器
|
| 40 |
+
* Servo: Servo
|
| 41 |
{keywords_mapping_string}
|
| 42 |
|
| 43 |
+
现在,请将以下内容翻译成中文:
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
|
| 48 |
""".format(keywords_mapping_string=keywords_mapping_string)
|
| 49 |
|
| 50 |
LLM_SYS_7B_PROMPT_ZH = """
|