Instructions to use Connorvr/BrightBot-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Connorvr/BrightBot-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Connorvr/BrightBot-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Connorvr/BrightBot-small") model = AutoModelForCausalLM.from_pretrained("Connorvr/BrightBot-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Connorvr/BrightBot-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Connorvr/BrightBot-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Connorvr/BrightBot-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Connorvr/BrightBot-small
- SGLang
How to use Connorvr/BrightBot-small 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 "Connorvr/BrightBot-small" \ --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": "Connorvr/BrightBot-small", "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 "Connorvr/BrightBot-small" \ --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": "Connorvr/BrightBot-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Connorvr/BrightBot-small with Docker Model Runner:
docker model run hf.co/Connorvr/BrightBot-small
Download training_args.bin from Connorvr/BrightBot-small: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/Connorvr/BrightBot-small/resolve/main/training_args.bin
- Command line
-
hf download hf://Connorvr/BrightBot-small/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Connorvr/BrightBot-small/resolve/main/training_args.bin
1.33 kB
- Xet hash:
- 968c798bc6a22d92b0e57d44fdbdf022037292defbb6cbad2252938212601a47
- Size of remote file:
- 1.33 kB
- SHA256:
- 9cc944ea6b2746fb620c1201cb14a3cec086bfeccd68159e9fb6306794692660
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