Instructions to use crumb/core1-base-464m-c4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crumb/core1-base-464m-c4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="crumb/core1-base-464m-c4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("crumb/core1-base-464m-c4") model = AutoModelForCausalLM.from_pretrained("crumb/core1-base-464m-c4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use crumb/core1-base-464m-c4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "crumb/core1-base-464m-c4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "crumb/core1-base-464m-c4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/crumb/core1-base-464m-c4
- SGLang
How to use crumb/core1-base-464m-c4 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 "crumb/core1-base-464m-c4" \ --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": "crumb/core1-base-464m-c4", "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 "crumb/core1-base-464m-c4" \ --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": "crumb/core1-base-464m-c4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use crumb/core1-base-464m-c4 with Docker Model Runner:
docker model run hf.co/crumb/core1-base-464m-c4
Download pytorch_model.bin from crumb/core1-base-464m-c4: direct link, hf CLI and curl.
- Browser
- Download file 929 MB
-
https://huggingface.co/crumb/core1-base-464m-c4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://crumb/core1-base-464m-c4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/crumb/core1-base-464m-c4/resolve/main/pytorch_model.bin
929 MB
- Xet hash:
- 2ce8a6d063c8a1cbbfb5d90aeacc22e824e6a260244416f73f126d91421b7eab
- Size of remote file:
- 929 MB
- SHA256:
- 7fff9ad2c4a315146685f7fb0222f16d394723b55380c0bac94513b37c3218d3
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