Text Generation
Transformers
Safetensors
busybeaver_qdelta
busybeaver
tool-calling
agent-policy
json
local-agents
qdelta
50m
Instructions to use GestaltLabs/BusyBeaver-50M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GestaltLabs/BusyBeaver-50M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GestaltLabs/BusyBeaver-50M")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("GestaltLabs/BusyBeaver-50M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use GestaltLabs/BusyBeaver-50M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GestaltLabs/BusyBeaver-50M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GestaltLabs/BusyBeaver-50M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/GestaltLabs/BusyBeaver-50M
- SGLang
How to use GestaltLabs/BusyBeaver-50M 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 "GestaltLabs/BusyBeaver-50M" \ --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": "GestaltLabs/BusyBeaver-50M", "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 "GestaltLabs/BusyBeaver-50M" \ --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": "GestaltLabs/BusyBeaver-50M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use GestaltLabs/BusyBeaver-50M with Docker Model Runner:
docker model run hf.co/GestaltLabs/BusyBeaver-50M
- Xet hash:
- cb46122d5ce99d2f29b52380434144f615a5b00bf9c099ac2678c3bf07aec14c
- Size of remote file:
- 5.84 kB
- SHA256:
- 0d2535d2d034cc23032036ce4ac74d7680256890d3513f7979a162ddb0b04ced
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.