How to use from
SGLangUse 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 "appvoid/arco-plus" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "appvoid/arco-plus",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
arco+
This is an untrained passthrough model based on arco and danube as a first effort to train a small enough reasoning language model that generalizes across all kind of reasoning tasks.
Benchmarks
| Parameters | Model | MMLU | ARC | HellaSwag | PIQA | Winogrande | Average |
|---|---|---|---|---|---|---|---|
| 488m | arco-lite | 23.22 | 33.45 | 56.55 | 69.70 | 59.19 | 48.46 |
| 773m | arco-plus | 23.06 | 36.43 | 60.09 | 72.36 | 60.46 | 50.48 |
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: appvoid/arco
layer_range: [0, 14]
- sources:
- model: h2oai/h2o-danube3-500m-base
layer_range: [4, 16]
merge_method: passthrough
dtype: float16
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "appvoid/arco-plus" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/arco-plus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'