Instructions to use ThingAI/ARK-270M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThingAI/ARK-270M-Base with Transformers:

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

pipe = pipeline("text-generation", model="ThingAI/ARK-270M-Base", 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-270M-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ThingAI/ARK-270M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThingAI/ARK-270M-Base"
# 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-270M-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ThingAI/ARK-270M-Base

SGLang

How to use ThingAI/ARK-270M-Base 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-270M-Base" \
    --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-270M-Base",
		"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-270M-Base" \
        --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-270M-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ThingAI/ARK-270M-Base with Docker Model Runner:
```
docker model run hf.co/ThingAI/ARK-270M-Base
```

ThingsAI commited on Jun 6

Commit

efc6baa

verified ·

1 Parent(s): c58e770

Upload modeling_quark.py with huggingface_hub

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modeling_quark.py +1 -0

modeling_quark.py CHANGED Viewed

@@ -76,6 +76,7 @@ class QuarkForCausalLM(QuarkPreTrainedModel):
         self.lm_head=nn.Linear(config.d_model,config.vocab_size,bias=False)
         self.lm_head.weight=self.embed_tokens.weight
         self.post_init()
     def get_input_embeddings(self): return self.embed_tokens
     def set_input_embeddings(self, v): self.embed_tokens=v
     def get_output_embeddings(self): return self.lm_head

         self.lm_head=nn.Linear(config.d_model,config.vocab_size,bias=False)
         self.lm_head.weight=self.embed_tokens.weight
         self.post_init()
+        self.lm_head.weight = self.model.embed_tokens.weight
     def get_input_embeddings(self): return self.embed_tokens
     def set_input_embeddings(self, v): self.embed_tokens=v
     def get_output_embeddings(self): return self.lm_head