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
Upload README.md with huggingface_hub
Browse files
README.md
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# BusyBeaver-50M
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BusyBeaver-50M is a compact agent-policy model for strict JSON tool-call prediction. It is not a general chatbot. It takes a compact agent state, task goal, recent observations, and available tool schemas, then predicts exactly one next tool call for a local agent harness.
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This repository is the canonical packaging of the internally tracked V10 checkpoint 200 run.
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model.eval()
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## Safety
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BusyBeaver-50M predicts tool calls; it does not execute them. Production harnesses should:
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# BusyBeaver-50M
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BusyBeaver-50M is a compact agent-policy model for strict JSON tool-call prediction. It is not a general chatbot. It takes a compact agent state, task goal, recent observations, and available tool schemas, then predicts exactly one next tool call for a local agent harness.
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This repository is the canonical packaging of the internally tracked V10 checkpoint 200 run.
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model.eval()
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```
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## Harness Integration
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BusyBeaver can be exposed to normal agent harnesses through the included OpenAI-compatible adapter server. The model still emits only strict JSON tool-policy objects; it should be used as a tool-call policy helper, not as the main chat model.
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```bash
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python scripts/busybeaver_openai_server.py \
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--model GestaltLabs/BusyBeaver-50M \
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--host 127.0.0.1 \
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--port 8765
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```
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Use `http://127.0.0.1:8765/v1` as the OpenAI-compatible base URL and `BusyBeaver-50M` as the model id. Native support in engines such as llama.cpp, vLLM, or Ollama requires either a BusyBeaver architecture adapter or a future export to a standard runtime format.
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## Safety
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BusyBeaver-50M predicts tool calls; it does not execute them. Production harnesses should:
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