Text Generation
Transformers
Safetensors
English
Italian
quark
causal-lm
small-language-model
gqa
rope
swiglu
bash
code
custom_code
Instructions to use ThingAI/ARK-72M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThingAI/ARK-72M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ThingAI/ARK-72M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ThingAI/ARK-72M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ThingAI/ARK-72M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ThingAI/ARK-72M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ThingAI/ARK-72M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ThingAI/ARK-72M
- SGLang
How to use ThingAI/ARK-72M 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/ARK-72M" \ --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": "ThingAI/ARK-72M", "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 "ThingAI/ARK-72M" \ --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": "ThingAI/ARK-72M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ThingAI/ARK-72M with Docker Model Runner:
docker model run hf.co/ThingAI/ARK-72M
File size: 1,408 Bytes
92a7d9d c54f415 92a7d9d | 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 40 41 42 43 44 | """
Quark model configuration — compatibile con AutoConfig / AutoModel HuggingFace.
"""
from transformers import PretrainedConfig
class QuarkConfig(PretrainedConfig):
model_type = "quark"
# Spezza la ricorsione in to_diff_dict(): senza questo, transformers tenta
# di costruire self.__class__() per il diff, che richiama __init__ -> __repr__
# -> to_diff_dict() -> self.__class__() all'infinito.
has_no_defaults_at_init = True
def __init__(
self,
vocab_size = 65_536,
d_model = 512,
n_heads = 8,
n_kv_heads = 2,
n_layers = 14,
d_ff = 1344,
head_dim = 64,
max_seq_len = 2048,
rope_theta = 10_000.0,
rms_eps = 1e-5,
qkv_bias = True,
dropout = 0.0,
tie_word_embeddings = True,
**kwargs,
):
self.vocab_size = vocab_size
self.d_model = d_model
self.n_heads = n_heads
self.n_kv_heads = n_kv_heads
self.n_layers = n_layers
self.d_ff = d_ff
self.head_dim = head_dim
self.max_seq_len = max_seq_len
self.rope_theta = rope_theta
self.rms_eps = rms_eps
self.qkv_bias = qkv_bias
self.dropout = dropout
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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