Instructions to use ForSureTesterSim/Holy-Trinity-7B-PCB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ForSureTesterSim/Holy-Trinity-7B-PCB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ForSureTesterSim/Holy-Trinity-7B-PCB")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ForSureTesterSim/Holy-Trinity-7B-PCB") model = AutoModelForCausalLM.from_pretrained("ForSureTesterSim/Holy-Trinity-7B-PCB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ForSureTesterSim/Holy-Trinity-7B-PCB with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ForSureTesterSim/Holy-Trinity-7B-PCB" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ForSureTesterSim/Holy-Trinity-7B-PCB", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ForSureTesterSim/Holy-Trinity-7B-PCB
- SGLang
How to use ForSureTesterSim/Holy-Trinity-7B-PCB 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 "ForSureTesterSim/Holy-Trinity-7B-PCB" \ --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": "ForSureTesterSim/Holy-Trinity-7B-PCB", "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 "ForSureTesterSim/Holy-Trinity-7B-PCB" \ --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": "ForSureTesterSim/Holy-Trinity-7B-PCB", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ForSureTesterSim/Holy-Trinity-7B-PCB with Docker Model Runner:
docker model run hf.co/ForSureTesterSim/Holy-Trinity-7B-PCB
Download model.safetensors from ForSureTesterSim/Holy-Trinity-7B-PCB: direct link, hf CLI and curl.
- Browser
- Download file 14.5 GB
-
https://huggingface.co/ForSureTesterSim/Holy-Trinity-7B-PCB/resolve/main/model.safetensors
- Command line
-
hf download hf://ForSureTesterSim/Holy-Trinity-7B-PCB/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ForSureTesterSim/Holy-Trinity-7B-PCB/resolve/main/model.safetensors
14.5 GB
- Xet hash:
- 4608ce61060da94332da7f62145ebfaf023407f0ea0e77bd3c220a2f327ee07d
- Size of remote file:
- 14.5 GB
- SHA256:
- 3fd918625aa0d3a433373b43114bf32ebfef23c0a3dcaea2313e2d16f7d1cf40
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