Instructions to use Hallzy/Peterbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hallzy/Peterbot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Hallzy/Peterbot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Hallzy/Peterbot") model = AutoModelForCausalLM.from_pretrained("Hallzy/Peterbot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Hallzy/Peterbot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Hallzy/Peterbot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hallzy/Peterbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Hallzy/Peterbot
- SGLang
How to use Hallzy/Peterbot 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 "Hallzy/Peterbot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hallzy/Peterbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Hallzy/Peterbot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hallzy/Peterbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Hallzy/Peterbot with Docker Model Runner:
docker model run hf.co/Hallzy/Peterbot
Download pytorch_model.bin from Hallzy/Peterbot: direct link, hf CLI and curl.
- Browser
- Download file 1.44 GB
-
https://huggingface.co/Hallzy/Peterbot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Hallzy/Peterbot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Hallzy/Peterbot/resolve/main/pytorch_model.bin
1.44 GB
- Xet hash:
- d3b688346f3cc0eee67fe51a91d3308b2551afcc436b6858ac3997775666e6b5
- Size of remote file:
- 1.44 GB
- SHA256:
- c58bc8f9f10218385769f8b8a37e158b371f21c26e29946f76a27afaad955931
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