Text Generation
Transformers
Safetensors
English
tinybuddy
tiny-lm
tinystories
educational
built-with-llama
custom_code
Instructions to use Eeppa/TinyBuddy-30M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eeppa/TinyBuddy-30M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Eeppa/TinyBuddy-30M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Eeppa/TinyBuddy-30M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Eeppa/TinyBuddy-30M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Eeppa/TinyBuddy-30M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Eeppa/TinyBuddy-30M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Eeppa/TinyBuddy-30M
- SGLang
How to use Eeppa/TinyBuddy-30M 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 "Eeppa/TinyBuddy-30M" \ --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": "Eeppa/TinyBuddy-30M", "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 "Eeppa/TinyBuddy-30M" \ --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": "Eeppa/TinyBuddy-30M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Eeppa/TinyBuddy-30M with Docker Model Runner:
docker model run hf.co/Eeppa/TinyBuddy-30M
File size: 750 Bytes
c360acf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # TinyBuddy-30M A 30 million parameter GPT-style transformer trained on TinyStories. ## Architecture - 6 layers, 8 attention heads, 256 embedding dim - 50,000 vocabulary size (untied weights) - 512 context length (trained on 128 for speed) ## Training - Dataset: TinyStories (5,000 stories) - Steps: 1,500 - Hardware: CPU only - Loss: ~5.5 (coherent but not good) ## What It Can Do - Generate 2-3 word fragments that resemble story patterns - Sometimes repeat words from the prompt - Produce gibberish that's trying to be English ## What It Cannot Do - Tell a coherent story - Answer questions - Anything useful ## Why It Exists To demonstrate that even a tiny transformer learns *patterns*, not rules. This is a real AI, just a very small one. |