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versae
/
filiberto-7B-instruct-exp1

Text Generation
MLX
Safetensors
GGUF
mistral
finetuned
conversational
Model card Files Files and versions
xet
Community
1

Instructions to use versae/filiberto-7B-instruct-exp1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • MLX

    How to use versae/filiberto-7B-instruct-exp1 with MLX:

    # Make sure mlx-lm is installed
    # pip install --upgrade mlx-lm
    
    # Generate text with mlx-lm
    from mlx_lm import load, generate
    
    model, tokenizer = load("versae/filiberto-7B-instruct-exp1")
    
    prompt = "Write a story about Einstein"
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )
    
    text = generate(model, tokenizer, prompt=prompt, verbose=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use versae/filiberto-7B-instruct-exp1 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 versae/filiberto-7B-instruct-exp1:F16
    # Run inference directly in the terminal:
    llama cli -hf versae/filiberto-7B-instruct-exp1:F16
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf versae/filiberto-7B-instruct-exp1:F16
    # Run inference directly in the terminal:
    llama cli -hf versae/filiberto-7B-instruct-exp1:F16
    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 versae/filiberto-7B-instruct-exp1:F16
    # Run inference directly in the terminal:
    ./llama-cli -hf versae/filiberto-7B-instruct-exp1:F16
    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 versae/filiberto-7B-instruct-exp1:F16
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf versae/filiberto-7B-instruct-exp1:F16
    Use Docker
    docker model run hf.co/versae/filiberto-7B-instruct-exp1:F16
  • LM Studio
  • Jan
  • vLLM

    How to use versae/filiberto-7B-instruct-exp1 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "versae/filiberto-7B-instruct-exp1"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "versae/filiberto-7B-instruct-exp1",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/versae/filiberto-7B-instruct-exp1:F16
  • Ollama

    How to use versae/filiberto-7B-instruct-exp1 with Ollama:

    ollama run hf.co/versae/filiberto-7B-instruct-exp1:F16
  • Unsloth Studio

    How to use versae/filiberto-7B-instruct-exp1 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 versae/filiberto-7B-instruct-exp1 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 versae/filiberto-7B-instruct-exp1 to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for versae/filiberto-7B-instruct-exp1 to start chatting
  • MLX LM

    How to use versae/filiberto-7B-instruct-exp1 with MLX LM:

    Generate or start a chat session
    # Install MLX LM
    uv tool install mlx-lm
    # Interactive chat REPL
    mlx_lm.chat --model "versae/filiberto-7B-instruct-exp1"
    Run an OpenAI-compatible server
    # Install MLX LM
    uv tool install mlx-lm
    # Start the server
    mlx_lm.server --model "versae/filiberto-7B-instruct-exp1"
    # Calling the OpenAI-compatible server with curl
    curl -X POST "http://localhost:8000/v1/chat/completions" \
       -H "Content-Type: application/json" \
       --data '{
         "model": "versae/filiberto-7B-instruct-exp1",
         "messages": [
           {"role": "user", "content": "Hello"}
         ]
       }'
  • Docker Model Runner

    How to use versae/filiberto-7B-instruct-exp1 with Docker Model Runner:

    docker model run hf.co/versae/filiberto-7B-instruct-exp1:F16
  • Lemonade

    How to use versae/filiberto-7B-instruct-exp1 with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull versae/filiberto-7B-instruct-exp1:F16
    Run and chat with the model
    lemonade run user.filiberto-7B-instruct-exp1-F16
    List all available models
    lemonade list
  • Atomic Chat
filiberto-7B-instruct-exp1
14.5 GB
Ctrl+K
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  • 1 contributor
History: 5 commits
versae's picture
versae
84c0c6daae1460f44d5684424b585d83f2537c796f926580d496d268289de5c9
ffe9a32 verified over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    675 Bytes
    84c0c6daae1460f44d5684424b585d83f2537c796f926580d496d268289de5c9 over 2 years ago
  • config.json
    2.13 kB
    84c0c6daae1460f44d5684424b585d83f2537c796f926580d496d268289de5c9 over 2 years ago
  • model-00001-of-00003.safetensors
    5.26 GB
    xet
    bba1db62ab9dfd22534607e8636cd6564479adb505cd0c4714055b6148e08255 over 2 years ago
  • model-00002-of-00003.safetensors
    5.35 GB
    xet
    11eb0bf848e985ef1a67dddaab1a340d1bcf5d0dd57233b0b424c69d335977cc over 2 years ago
  • model-00003-of-00003.safetensors
    3.87 GB
    xet
    213029cf88d086a9c7c86327ea56a3fb64cfd128f5786f8f03de5202460f98dd over 2 years ago
  • model.safetensors.index.json
    25.1 kB
    84c0c6daae1460f44d5684424b585d83f2537c796f926580d496d268289de5c9 over 2 years ago
  • special_tokens_map.json
    414 Bytes
    84c0c6daae1460f44d5684424b585d83f2537c796f926580d496d268289de5c9 over 2 years ago
  • tokenizer.json
    1.8 MB
    84c0c6daae1460f44d5684424b585d83f2537c796f926580d496d268289de5c9 over 2 years ago
  • tokenizer_config.json
    1.46 kB
    84c0c6daae1460f44d5684424b585d83f2537c796f926580d496d268289de5c9 over 2 years ago