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
```

blackbook-lm commited on Jun 5

Commit

709ee31

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1 Parent(s): e02dbde

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@@ -22,7 +22,7 @@ model-index:
 ## Overview
-Quark-135m v0.2 is a compact bilingual language model designed for Italian and English, built entirely from scratch by [ThingsAI](https://things-ai.org). It represents the second generation of the Quark model family, featuring a custom bilingual BPE tokenizer and a modern transformer architecture.
 This is the **base pretrained model**. An SFT (instruction-tuned) version trained on bilingual conversational data is available for chat applications.

 ## Overview
+Quark-135m-Bilingual is a compact bilingual language model designed for Italian and English, built entirely from scratch by [ThingsAI](https://things-ai.org). It represents the second generation of the Quark model family, featuring a custom bilingual BPE tokenizer and a modern transformer architecture.
 This is the **base pretrained model**. An SFT (instruction-tuned) version trained on bilingual conversational data is available for chat applications.