Text Generation
Transformers
Safetensors
English
tinybuddy
tiny-lm
tinystories
educational
built-with-llama
small-model
custom_code
Instructions to use Eeppa/TinyBuddy-500K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eeppa/TinyBuddy-500K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Eeppa/TinyBuddy-500K", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Eeppa/TinyBuddy-500K", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Eeppa/TinyBuddy-500K with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Eeppa/TinyBuddy-500K" # 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-500K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Eeppa/TinyBuddy-500K
- SGLang
How to use Eeppa/TinyBuddy-500K 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-500K" \ --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-500K", "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-500K" \ --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-500K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Eeppa/TinyBuddy-500K with Docker Model Runner:
docker model run hf.co/Eeppa/TinyBuddy-500K
File size: 1,161 Bytes
de58358 | 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 29 30 31 32 33 34 35 36 37 38 39 | """
TinyBuddyConfig for TinyBuddy-500K
"""
from transformers import PretrainedConfig
class TinyBuddyConfig(PretrainedConfig):
model_type = "tinybuddy"
def __init__(
self,
vocab_size=2048,
hidden_size=96,
num_hidden_layers=2,
num_attention_heads=4,
num_key_value_heads=2,
intermediate_size=384,
max_position_embeddings=512,
rms_norm_eps=1e-6,
tie_word_embeddings=True,
bos_token_id=2,
eos_token_id=2,
pad_token_id=0,
**kwargs,
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.intermediate_size = intermediate_size
self.max_position_embeddings = max_position_embeddings
self.rms_norm_eps = rms_norm_eps
self.tie_word_embeddings = tie_word_embeddings
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id |