Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

rawmodels
/
flint-roleplay-antislop

Text Generation
Transformers
Safetensors
GGUF
English
flint
roleplay
creative-writing
antislop - reinforcement-learning
Mixture of Experts
bf16
llama.cpp
Model card Files Files and versions
xet
Community

Instructions to use rawmodels/flint-roleplay-antislop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use rawmodels/flint-roleplay-antislop with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="rawmodels/flint-roleplay-antislop")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("rawmodels/flint-roleplay-antislop")
    model = AutoModelForCausalLM.from_pretrained("rawmodels/flint-roleplay-antislop", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use rawmodels/flint-roleplay-antislop 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 rawmodels/flint-roleplay-antislop:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf rawmodels/flint-roleplay-antislop:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf rawmodels/flint-roleplay-antislop:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf rawmodels/flint-roleplay-antislop: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 rawmodels/flint-roleplay-antislop:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf rawmodels/flint-roleplay-antislop: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 rawmodels/flint-roleplay-antislop:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf rawmodels/flint-roleplay-antislop:Q4_K_M
    Use Docker
    docker model run hf.co/rawmodels/flint-roleplay-antislop:Q4_K_M
  • Llama new
  • LM Studio
  • Jan
  • vLLM

    How to use rawmodels/flint-roleplay-antislop with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "rawmodels/flint-roleplay-antislop"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "rawmodels/flint-roleplay-antislop",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/rawmodels/flint-roleplay-antislop:Q4_K_M
  • SGLang

    How to use rawmodels/flint-roleplay-antislop 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 "rawmodels/flint-roleplay-antislop" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "rawmodels/flint-roleplay-antislop",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    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 "rawmodels/flint-roleplay-antislop" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "rawmodels/flint-roleplay-antislop",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Ollama

    How to use rawmodels/flint-roleplay-antislop with Ollama:

    ollama run hf.co/rawmodels/flint-roleplay-antislop:Q4_K_M
  • Unsloth Desktop
  • Docker Model Runner

    How to use rawmodels/flint-roleplay-antislop with Docker Model Runner:

    docker model run hf.co/rawmodels/flint-roleplay-antislop:Q4_K_M
  • Lemonade

    How to use rawmodels/flint-roleplay-antislop with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull rawmodels/flint-roleplay-antislop:Q4_K_M
    Run and chat with the model
    lemonade run user.flint-roleplay-antislop-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
flint-roleplay-antislop / eval
477 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
AlexWortega's picture
AlexWortega
Card: GRPO + calibrated reward wording; self-contained eval
82ee6f5 verified about 21 hours ago
  • roleplay_heldout.json
    101 kB
    Card: GRPO + calibrated reward wording; self-contained eval about 21 hours ago
  • rp_generate.py
    1.97 kB
    Card: GRPO + calibrated reward wording; self-contained eval about 21 hours ago
  • rp_judge.py
    3.13 kB
    flint-roleplay-antislop: merged weights, GGUF, card, eval about 22 hours ago
  • rp_judged.json
    367 kB
    flint-roleplay-antislop: merged weights, GGUF, card, eval about 22 hours ago
  • rp_scenes.json
    3.95 kB
    flint-roleplay-antislop: merged weights, GGUF, card, eval about 22 hours ago