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RthItalia
/
AICE-v1

Text Generation
ONNX
Safetensors
GGUF
Italian
English
rwkv
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
  • llama-cpp-python

    How to use RthItalia/AICE-v1 with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="RthItalia/AICE-v1",
    	filename="mobile/AICE_v1_rwkv4_custom.gguf",
    )
    
    output = llm(
    	"Once upon a time,",
    	max_tokens=512,
    	echo=True
    )
    print(output)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use RthItalia/AICE-v1 with llama.cpp:

    Install from brew
    brew install llama.cpp
    # 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
    Install from WinGet (Windows)
    winget install llama.cpp
    # 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
    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 Studio new

    How to use RthItalia/AICE-v1 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 RthItalia/AICE-v1 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 RthItalia/AICE-v1 to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for RthItalia/AICE-v1 to start chatting
  • 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
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
93800aa 3 months ago
  • mobile
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  • .gitattributes
    226 Bytes
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  • EU_TRAINING_SUMMARY.md
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  • HF_RELEASE_CHECKLIST.md
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  • MOBILE_Q4_PIPELINE.md
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  • README.md
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  • RELEASE_MANIFEST_SHA256.txt
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  • config.json
    442 Bytes
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  • generation_config.json
    117 Bytes
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  • model.safetensors
    3.03 GB
    xet
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  • resoconto.txt
    1.38 kB
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  • special_tokens_map.json
    441 Bytes
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  • tokenizer.json
    3.56 MB
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  • tokenizer_config.json
    4.86 kB
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