Instructions to use rustformers/bloomz-ggml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rustformers/bloomz-ggml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rustformers/bloomz-ggml")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rustformers/bloomz-ggml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rustformers/bloomz-ggml with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rustformers/bloomz-ggml" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rustformers/bloomz-ggml", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rustformers/bloomz-ggml
- SGLang
How to use rustformers/bloomz-ggml 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 "rustformers/bloomz-ggml" \ --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": "rustformers/bloomz-ggml", "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 "rustformers/bloomz-ggml" \ --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": "rustformers/bloomz-ggml", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rustformers/bloomz-ggml with Docker Model Runner:
docker model run hf.co/rustformers/bloomz-ggml
Download bloomz-560m-q5_1-ggjt.bin from rustformers/bloomz-ggml: direct link, hf CLI and curl.
- Browser
- Download file 617 MB
-
https://huggingface.co/rustformers/bloomz-ggml/resolve/84bc3f74d38590254cd1f28e6d853b34275016c9/bloomz-560m-q5_1-ggjt.bin
- Command line
-
hf download hf://rustformers/bloomz-ggml@84bc3f74d38590254cd1f28e6d853b34275016c9/bloomz-560m-q5_1-ggjt.bin
-
curl -L -o bloomz-560m-q5_1-ggjt.bin https://huggingface.co/rustformers/bloomz-ggml/resolve/84bc3f74d38590254cd1f28e6d853b34275016c9/bloomz-560m-q5_1-ggjt.bin
617 MB
- Xet hash:
- b47c5ce7049dc141dea0c35631c38525edef10269647c3272589e8144cbd13ec
- Size of remote file:
- 617 MB
- SHA256:
- c5d7bf8899140da92ffcb1a8b994b454a20abc7b0807d594e0e237fb09ac20de
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.