Instructions to use sangdal/ChatBot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sangdal/ChatBot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sangdal/ChatBot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sangdal/ChatBot") model = AutoModelForCausalLM.from_pretrained("sangdal/ChatBot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sangdal/ChatBot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sangdal/ChatBot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sangdal/ChatBot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sangdal/ChatBot
- SGLang
How to use sangdal/ChatBot 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 "sangdal/ChatBot" \ --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": "sangdal/ChatBot", "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 "sangdal/ChatBot" \ --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": "sangdal/ChatBot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sangdal/ChatBot with Docker Model Runner:
docker model run hf.co/sangdal/ChatBot
Download data/train.json from sangdal/ChatBot: direct link, hf CLI and curl.
- Browser
- Download file 11.2 MB
-
https://huggingface.co/sangdal/ChatBot/resolve/main/data/train.json
- Command line
-
hf download hf://sangdal/ChatBot/data/train.json
-
curl -L -o train.json https://huggingface.co/sangdal/ChatBot/resolve/main/data/train.json
11.2 MB
- Xet hash:
- c73e223f871f3f5ac76b6d75a17847d62932258abc651bafd3f84dccfe8ce82b
- Size of remote file:
- 11.2 MB
- SHA256:
- 5830ebee17a23c2f551c3ff46446f826bfc27ef3a292b283091ab25cc6f15867
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