Text Generation
Transformers
Safetensors
English
dwarf
bash
shell
linux
cli
code
small-language-model
conversational
custom_code
Instructions to use ThingAI/Dwarf-15M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThingAI/Dwarf-15M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ThingAI/Dwarf-15M", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ThingAI/Dwarf-15M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ThingAI/Dwarf-15M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ThingAI/Dwarf-15M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ThingAI/Dwarf-15M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ThingAI/Dwarf-15M
- SGLang
How to use ThingAI/Dwarf-15M 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 "ThingAI/Dwarf-15M" \ --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": "ThingAI/Dwarf-15M", "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 "ThingAI/Dwarf-15M" \ --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": "ThingAI/Dwarf-15M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ThingAI/Dwarf-15M with Docker Model Runner:
docker model run hf.co/ThingAI/Dwarf-15M
File size: 958 Bytes
441dc44 | 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 | """Dwarf model configuration."""
from transformers import PretrainedConfig
class DwarfConfig(PretrainedConfig):
model_type = "dwarf"
def __init__(
self,
vocab_size=8202,
d_model=320,
n_layers=12,
n_heads=5,
n_kv_heads=1,
d_ff=864,
max_seq_len=2048,
rope_theta=10000.0,
norm_eps=1e-5,
head_dim=64,
**kwargs,
):
self.vocab_size = vocab_size
self.d_model = d_model
self.n_layers = n_layers
self.n_heads = n_heads
self.n_kv_heads = n_kv_heads
self.d_ff = d_ff
self.max_seq_len = max_seq_len
self.rope_theta = rope_theta
self.norm_eps = norm_eps
self.head_dim = head_dim
self.num_hidden_layers = n_layers
self.hidden_size = d_model
self.num_attention_heads = n_heads
self.num_key_value_heads = n_kv_heads
super().__init__(**kwargs)
|