Instructions to use appvoid/arco-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/arco-plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/arco-plus")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/arco-plus") model = AutoModelForCausalLM.from_pretrained("appvoid/arco-plus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/arco-plus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/arco-plus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/arco-plus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/arco-plus
- SGLang
How to use appvoid/arco-plus 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 "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 }'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 "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 }' - Docker Model Runner
How to use appvoid/arco-plus with Docker Model Runner:
docker model run hf.co/appvoid/arco-plus
| base_model: | |
| - appvoid/arco | |
| - h2oai/h2o-danube3-500m-base | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| # 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: | |
| ```yaml | |
| 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 | |
| ``` | |