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 / config.json

ThingsAI

Fix RoPE inv_freq per ogni layer

8fadcaf verified about 1 month ago

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832 Bytes

	{
	"architectures": [
	"QuarkForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_quark.QuarkConfig",
	"AutoModelForCausalLM": "modeling_quark.QuarkForCausalLM"
	},
	"base_pretrain": "15.7B tokens bilingual IT+EN",
	"d_ff": 1536,
	"d_model": 576,
	"dropout": 0.0,
	"dtype": "float32",
	"head_dim": 64,
	"languages": [
	"it",
	"en"
	],
	"max_seq_len": 2048,
	"model_type": "quark",
	"n_heads": 9,
	"n_kv_heads": 3,
	"n_layers": 30,
	"qkv_bias": true,
	"rms_eps": 1e-05,
	"rope_theta": 10000.0,
	"sft_dataset": "MBZUAI/Bactrian-X (it+en)",
	"sft_loss": 1.9,
	"sft_steps": 4000,
	"special_tokens": [
	"<\|user\|>",
	"<\|assistant\|>",
	"<\|end\|>"
	],
	"tie_word_embeddings": true,
	"tokenizer": "ThingAI/QuarkTokenizer",
	"transformers_version": "5.6.0",
	"vocab_size": 65537
	}