Instructions to use ThingAI/ARK-135M-Bilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThingAI/ARK-135M-Bilingual with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ThingAI/ARK-135M-Bilingual", 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/ARK-135M-Bilingual", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ThingAI/ARK-135M-Bilingual with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThingAI/ARK-135M-Bilingual"
# 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/ARK-135M-Bilingual",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ThingAI/ARK-135M-Bilingual

SGLang

How to use ThingAI/ARK-135M-Bilingual 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-135M-Bilingual" \
    --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/ARK-135M-Bilingual",
		"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/ARK-135M-Bilingual" \
        --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/ARK-135M-Bilingual",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ThingAI/ARK-135M-Bilingual with Docker Model Runner:
```
docker model run hf.co/ThingAI/ARK-135M-Bilingual
```

ARK-135M-Bilingual / configuration_quark.py

ThingsAI

Upload 7 files

68f0523 verified 3 months ago

Raw

History Blame Contribute Delete

928 Bytes

	"""Quark model configuration for HuggingFace Transformers."""

	from transformers import PretrainedConfig


	class QuarkConfig(PretrainedConfig):
	model_type = "quark"

	def __init__(
	self,
	vocab_size=65537,
	d_model=576,
	n_heads=9,
	n_kv_heads=3,
	n_layers=30,
	d_ff=1536,
	head_dim=64,
	max_seq_len=2048,
	rope_theta=10000.0,
	rms_eps=1e-5,
	qkv_bias=True,
	dropout=0.0,
	**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__(**kwargs)