Text Generation
Transformers
Safetensors
English
tinybuddy
tiny-model
educational
record-breaker
ultra-small
smallest-llm
80k-parameters
custom_code
Instructions to use Eeppa/TinyBuddy-80K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eeppa/TinyBuddy-80K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Eeppa/TinyBuddy-80K", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Eeppa/TinyBuddy-80K", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Eeppa/TinyBuddy-80K with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Eeppa/TinyBuddy-80K" # 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-80K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Eeppa/TinyBuddy-80K
- SGLang
How to use Eeppa/TinyBuddy-80K 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-80K" \ --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-80K", "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-80K" \ --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-80K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Eeppa/TinyBuddy-80K with Docker Model Runner:
docker model run hf.co/Eeppa/TinyBuddy-80K
| { | |
| "_name_or_path": "Eeppa/TinyBuddy-100K", | |
| "architectures": [ | |
| "TinyBuddyForCausalLM" | |
| ], | |
| "model_type": "tinybuddy", | |
| "vocab_size": 1024, | |
| "hidden_size": 48, | |
| "num_hidden_layers": 1, | |
| "num_attention_heads": 4, | |
| "num_key_value_heads": 2, | |
| "intermediate_size": 192, | |
| "max_position_embeddings": 128, | |
| "block_size": 128, | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "pad_token_id": 0, | |
| "unk_token_id": 3, | |
| "transformers_version": "4.40.0", | |
| "torch_dtype": "float32", | |
| "num_parameters": 83856, | |
| "architecture_note": "1-layer Llama-style with GQA (4 query heads, 2 KV heads), RMSNorm, SiLU/SwiGLU, RoPE, tied embeddings", | |
| "auto_map": { | |
| "AutoConfig": "configuration_tinybuddy.TinyBuddyConfig", | |
| "AutoModelForCausalLM": "modeling_tinybuddy.TinyBuddyForCausalLM" | |
| } | |
| } |