Instructions to use AlphaRandy/WhelanChatBot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlphaRandy/WhelanChatBot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AlphaRandy/WhelanChatBot")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AlphaRandy/WhelanChatBot") model = AutoModelForCausalLM.from_pretrained("AlphaRandy/WhelanChatBot") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AlphaRandy/WhelanChatBot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AlphaRandy/WhelanChatBot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AlphaRandy/WhelanChatBot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AlphaRandy/WhelanChatBot
- SGLang
How to use AlphaRandy/WhelanChatBot 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 "AlphaRandy/WhelanChatBot" \ --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": "AlphaRandy/WhelanChatBot", "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 "AlphaRandy/WhelanChatBot" \ --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": "AlphaRandy/WhelanChatBot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AlphaRandy/WhelanChatBot with Docker Model Runner:
docker model run hf.co/AlphaRandy/WhelanChatBot
- Xet hash:
- 1cebd96bd007e957e860ccd925056f3cbf2f30dfb74f278963688e8c98055578
- Size of remote file:
- 498 MB
- SHA256:
- bbc30eebee49f38a9cdf851018debbb296df3006853d32c8120877f30e441334
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.