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

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{
  "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
}