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
Llamacpp Quantizations of Excalibur-7b
Using llama.cpp release b2440 for quantization.
Original model: https://huggingface.co/InferenceIllusionist/Excalibur-7b
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Excalibur-7b-Q8_0.gguf | Q8_0 | 7.69GB | Extremely high quality, generally unneeded but max available quant. |
| Excalibur-7b-Q6_K.gguf | Q6_K | 5.94GB | Very high quality, near perfect, recommended. |
| Excalibur-7b-Q5_K_M.gguf | Q5_K_M | 5.13GB | High quality, very usable. |
| Excalibur-7b-Q5_K_S.gguf | Q5_K_S | 4.99GB | High quality, very usable. |
| Excalibur-7b-Q5_0.gguf | Q5_0 | 4.99GB | High quality, older format, generally not recommended. |
| Excalibur-7b-Q4_K_M.gguf | Q4_K_M | 4.36GB | Good quality, similar to 4.25 bpw. |
| Excalibur-7b-Q4_K_S.gguf | Q4_K_S | 4.14GB | Slightly lower quality with small space savings. |
| Excalibur-7b-Q4_0.gguf | Q4_0 | 4.10GB | Decent quality, older format, generally not recommended. |
| Excalibur-7b-Q3_K_L.gguf | Q3_K_L | 3.82GB | Lower quality but usable, good for low RAM availability. |
| Excalibur-7b-Q3_K_M.gguf | Q3_K_M | 3.51GB | Even lower quality. |
| Excalibur-7b-Q3_K_S.gguf | Q3_K_S | 3.16GB | Low quality, not recommended. |
| Excalibur-7b-Q2_K.gguf | Q2_K | 2.71GB | Extremely low quality, not recommended. |
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
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