Text Generation
Transformers
PyTorch
Safetensors
English
lightning
conversational
generative
custom_code
Instructions to use Aobangaming/lightning-30m-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aobangaming/lightning-30m-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aobangaming/lightning-30m-ft", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Aobangaming/lightning-30m-ft", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Aobangaming/lightning-30m-ft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aobangaming/lightning-30m-ft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aobangaming/lightning-30m-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Aobangaming/lightning-30m-ft
- SGLang
How to use Aobangaming/lightning-30m-ft 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 "Aobangaming/lightning-30m-ft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aobangaming/lightning-30m-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Aobangaming/lightning-30m-ft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aobangaming/lightning-30m-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Aobangaming/lightning-30m-ft with Docker Model Runner:
docker model run hf.co/Aobangaming/lightning-30m-ft
File size: 848 Bytes
f8f9c68 ceef542 f8f9c68 7c837d7 0cbd325 7c837d7 0cbd325 7c837d7 d9c0069 | 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 | from transformers import PretrainedConfig
class LightningConfig(PretrainedConfig):
model_type = "lightning"
def __init__(
self,
vocab_size=50000,
d_model=256,
nhead=4,
num_layers=4,
dropout=0.1,
max_seq_len=160,
pad_token_id=0,
eos_token_id=2,
**kwargs
):
super().__init__(
tie_word_embeddings=False,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
**kwargs
)
self.vocab_size = vocab_size
self.d_model = d_model
self.nhead = nhead
self.num_layers = num_layers
self.dropout = dropout
self.num_hidden_layers = num_layers
self.num_attention_heads = nhead
self.hidden_size = d_model
self.max_seq_len = max_seq_len |