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

  • Log In
  • Sign Up

bartowski
/
Excalibur-7b-GGUF

Text Generation
Transformers
GGUF
mergekit
Merge
Model card Files Files and versions
xet
Community

Instructions to use bartowski/Excalibur-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use bartowski/Excalibur-7b-GGUF with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="bartowski/Excalibur-7b-GGUF")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("bartowski/Excalibur-7b-GGUF", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use bartowski/Excalibur-7b-GGUF with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "bartowski/Excalibur-7b-GGUF"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "bartowski/Excalibur-7b-GGUF",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/bartowski/Excalibur-7b-GGUF
  • SGLang

    How to use bartowski/Excalibur-7b-GGUF 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 "bartowski/Excalibur-7b-GGUF" \
        --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": "bartowski/Excalibur-7b-GGUF",
    		"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 "bartowski/Excalibur-7b-GGUF" \
            --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": "bartowski/Excalibur-7b-GGUF",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use bartowski/Excalibur-7b-GGUF with Docker Model Runner:

    docker model run hf.co/bartowski/Excalibur-7b-GGUF
Excalibur-7b-GGUF
69.2 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
bartowski's picture
bartowski
Llamacpp quants
e488f42 verified about 2 years ago
  • .gitattributes
    2.48 kB
    Llamacpp quants about 2 years ago
  • Excalibur-7b-IQ3_M.gguf
    3.28 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-IQ3_S.gguf
    3.18 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-IQ4_NL.gguf
    4.16 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-IQ4_XS.gguf
    3.94 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q2_K.gguf
    2.72 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q3_K_L.gguf
    3.82 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q3_K_M.gguf
    3.52 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q3_K_S.gguf
    3.16 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q4_0.gguf
    4.11 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q4_K_M.gguf
    4.37 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q4_K_S.gguf
    4.14 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q5_0.gguf
    5 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q5_K_M.gguf
    5.13 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q5_K_S.gguf
    5 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q6_K.gguf
    5.94 GB
    xet
    Llamacpp quants about 2 years ago
  • Excalibur-7b-Q8_0.gguf
    7.7 GB
    xet
    Llamacpp quants about 2 years ago
  • README.md
    2.96 kB
    Llamacpp quants about 2 years ago