Instructions to use croqaz/Sprocket-and-Say with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use croqaz/Sprocket-and-Say with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="croqaz/Sprocket-and-Say")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("croqaz/Sprocket-and-Say", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use croqaz/Sprocket-and-Say with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "croqaz/Sprocket-and-Say" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "croqaz/Sprocket-and-Say", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/croqaz/Sprocket-and-Say
- SGLang
How to use croqaz/Sprocket-and-Say 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 "croqaz/Sprocket-and-Say" \ --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": "croqaz/Sprocket-and-Say", "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 "croqaz/Sprocket-and-Say" \ --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": "croqaz/Sprocket-and-Say", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use croqaz/Sprocket-and-Say with Docker Model Runner:
docker model run hf.co/croqaz/Sprocket-and-Say
Sprocket & Say
I've no pulse, no nerves, no need for tea, I'll argue physics or flatter thee. Feed me questions, feed me spite - I'll strike you dumb or strike you bright. No tongue to tire, no need to pray - Turn the crank. Sprocket & Say.
This is a tiny hobby LLM built by one guy, on one medium-budget gaming PC with one GPU.
Trained for 80 hours, 63,744 steps on 16.71B tokens (croqaz/Sprocket-n-Say dataset).
It is intentionally overtrained with 217 tokens per param, about 11× past Chinchilla-optimal.
Don't expect miracles. It is pretty good for its size tho.
- Llama architecture
- 77M (0.007B) params
- context size 1024 tokens
- base model, cannot chat