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alwaysgood
/
TranslateQwen_v1-GGUF

Image-Text-to-Text
GGUF
llama.cpp
qwen3.5
qwen
translation
vision
multimodal
lmstudio
Model card Files Files and versions
xet
Community

Instructions to use alwaysgood/TranslateQwen_v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use alwaysgood/TranslateQwen_v1-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 alwaysgood/TranslateQwen_v1-GGUF:BF16
    # Run inference directly in the terminal:
    llama cli -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
    # Run inference directly in the terminal:
    llama cli -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
    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 alwaysgood/TranslateQwen_v1-GGUF:BF16
    # Run inference directly in the terminal:
    ./llama-cli -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
    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 alwaysgood/TranslateQwen_v1-GGUF:BF16
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
    Use Docker
    docker model run hf.co/alwaysgood/TranslateQwen_v1-GGUF:BF16
  • LM Studio
  • Jan
  • vLLM

    How to use alwaysgood/TranslateQwen_v1-GGUF with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "alwaysgood/TranslateQwen_v1-GGUF"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "alwaysgood/TranslateQwen_v1-GGUF",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/alwaysgood/TranslateQwen_v1-GGUF:BF16
  • Ollama

    How to use alwaysgood/TranslateQwen_v1-GGUF with Ollama:

    ollama run hf.co/alwaysgood/TranslateQwen_v1-GGUF:BF16
  • Unsloth Desktop
  • Docker Model Runner

    How to use alwaysgood/TranslateQwen_v1-GGUF with Docker Model Runner:

    docker model run hf.co/alwaysgood/TranslateQwen_v1-GGUF:BF16
  • Lemonade

    How to use alwaysgood/TranslateQwen_v1-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull alwaysgood/TranslateQwen_v1-GGUF:BF16
    Run and chat with the model
    lemonade run user.TranslateQwen_v1-GGUF-BF16
    List all available models
    lemonade list
  • Atomic Chat
TranslateQwen_v1-GGUF
11.8 GB
Ctrl+K
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  • 1 contributor
History: 8 commits
alwaysgood's picture
alwaysgood
Clarify BF16 and vision files
c880df5 verified 3 months ago
  • .gitattributes
    1.72 kB
    Add files using upload-large-folder tool 3 months ago
  • README.md
    2.65 kB
    Clarify BF16 and vision files 3 months ago
  • TranslateQwen_v1-BF16.gguf
    8.42 GB
    xet
    Add files using upload-large-folder tool 3 months ago
  • TranslateQwen_v1-Q4_K_M.gguf
    2.71 GB
    xet
    Add Q4_K_M GGUF 3 months ago
  • TranslateQwen_v1-mmproj-BF16.gguf
    676 MB
    xet
    Add vision mmproj GGUF 3 months ago