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igor-im
/
flux_prompt_expander

Text Generation
Transformers
Safetensors
GGUF
PEFT
English
llama
text-generation-inference
unsloth
conversational
Model card Files Files and versions
xet
Community

Instructions to use igor-im/flux_prompt_expander with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use igor-im/flux_prompt_expander with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="igor-im/flux_prompt_expander")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("igor-im/flux_prompt_expander", dtype="auto")
  • PEFT

    How to use igor-im/flux_prompt_expander with PEFT:

    Task type is invalid.
  • llama-cpp-python

    How to use igor-im/flux_prompt_expander with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="igor-im/flux_prompt_expander",
    	filename="unsloth.F16.gguf",
    )
    
    llm.create_chat_completion(
    	messages = [
    		{
    			"role": "user",
    			"content": "What is the capital of France?"
    		}
    	]
    )
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use igor-im/flux_prompt_expander with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf igor-im/flux_prompt_expander:Q4_K_M
    # Run inference directly in the terminal:
    llama-cli -hf igor-im/flux_prompt_expander:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf igor-im/flux_prompt_expander:Q4_K_M
    # Run inference directly in the terminal:
    llama-cli -hf igor-im/flux_prompt_expander:Q4_K_M
    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 igor-im/flux_prompt_expander:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf igor-im/flux_prompt_expander:Q4_K_M
    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 igor-im/flux_prompt_expander:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf igor-im/flux_prompt_expander:Q4_K_M
    Use Docker
    docker model run hf.co/igor-im/flux_prompt_expander:Q4_K_M
  • LM Studio
  • Jan
  • vLLM

    How to use igor-im/flux_prompt_expander with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "igor-im/flux_prompt_expander"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "igor-im/flux_prompt_expander",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/igor-im/flux_prompt_expander:Q4_K_M
  • SGLang

    How to use igor-im/flux_prompt_expander with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "igor-im/flux_prompt_expander" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "igor-im/flux_prompt_expander",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "igor-im/flux_prompt_expander" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "igor-im/flux_prompt_expander",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Ollama

    How to use igor-im/flux_prompt_expander with Ollama:

    ollama run hf.co/igor-im/flux_prompt_expander:Q4_K_M
  • Unsloth Studio new

    How to use igor-im/flux_prompt_expander 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 igor-im/flux_prompt_expander 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 igor-im/flux_prompt_expander to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for igor-im/flux_prompt_expander to start chatting
  • Pi new

    How to use igor-im/flux_prompt_expander with Pi:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama-server -hf igor-im/flux_prompt_expander:Q4_K_M
    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": "igor-im/flux_prompt_expander:Q4_K_M"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Hermes Agent new

    How to use igor-im/flux_prompt_expander with Hermes Agent:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama-server -hf igor-im/flux_prompt_expander:Q4_K_M
    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 igor-im/flux_prompt_expander:Q4_K_M
    Run Hermes
    hermes
  • Docker Model Runner

    How to use igor-im/flux_prompt_expander with Docker Model Runner:

    docker model run hf.co/igor-im/flux_prompt_expander:Q4_K_M
  • Lemonade

    How to use igor-im/flux_prompt_expander with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull igor-im/flux_prompt_expander:Q4_K_M
    Run and chat with the model
    lemonade run user.flux_prompt_expander-Q4_K_M
    List all available models
    lemonade list
flux_prompt_expander
18.3 GB
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  • 1 contributor
History: 26 commits
igor-im's picture
igor-im
Upload 3 files
5cbf996 verified over 1 year ago
  • images
    Update images/prompts.txt over 1 year ago
  • .gitattributes
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  • README.md
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  • config.json
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  • model-00001-of-00002.safetensors
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  • model-00002-of-00002.safetensors
    1.46 GB
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  • model.safetensors.index.json
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  • tokenizer.json
    17.2 MB
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  • tokenizer_config.json
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  • unsloth.F16.gguf
    6.43 GB
    xet
    (Trained with Unsloth) over 1 year ago
  • unsloth.Q4_K_M.gguf
    2.02 GB
    xet
    (Trained with Unsloth) over 1 year ago
  • unsloth.Q8_0.gguf
    3.42 GB
    xet
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