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RthItalia
/
AICE-v1

Text Generation
RWKV
ONNX
Safetensors
GGUF
Italian
English
distillation
lora
Model card Files Files and versions
xet
Community

Instructions to use RthItalia/AICE-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • RWKV

    How to use RthItalia/AICE-v1 with RWKV:

    # No code snippets available yet for this library.
    
    # To use this model, check the repository files and the library's documentation.
    
    # Want to help? PRs adding snippets are welcome at:
    # https://github.com/huggingface/huggingface.js
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use RthItalia/AICE-v1 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 RthItalia/AICE-v1
    # Run inference directly in the terminal:
    llama cli -hf RthItalia/AICE-v1
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf RthItalia/AICE-v1
    # Run inference directly in the terminal:
    llama cli -hf RthItalia/AICE-v1
    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 RthItalia/AICE-v1
    # Run inference directly in the terminal:
    ./llama-cli -hf RthItalia/AICE-v1
    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 RthItalia/AICE-v1
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf RthItalia/AICE-v1
    Use Docker
    docker model run hf.co/RthItalia/AICE-v1
  • LM Studio
  • Jan
  • vLLM

    How to use RthItalia/AICE-v1 with vLLM:

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

    How to use RthItalia/AICE-v1 with Ollama:

    ollama run hf.co/RthItalia/AICE-v1
  • Unsloth Desktop
  • Docker Model Runner

    How to use RthItalia/AICE-v1 with Docker Model Runner:

    docker model run hf.co/RthItalia/AICE-v1
  • Lemonade

    How to use RthItalia/AICE-v1 with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull RthItalia/AICE-v1
    Run and chat with the model
    lemonade run user.AICE-v1-{{QUANT_TAG}}
    List all available models
    lemonade list
  • Atomic Chat
AICE-v1
10.8 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
RthItalia's picture
RthItalia
Squash history: keep only current release state
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    xet
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  • tokenizer.json
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  • tokenizer_config.json
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