Instructions to use appvoid/arco-2-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/arco-2-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/arco-2-instruct")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/arco-2-instruct") model = AutoModelForCausalLM.from_pretrained("appvoid/arco-2-instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/arco-2-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/arco-2-instruct" # 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-2-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/arco-2-instruct
- SGLang
How to use appvoid/arco-2-instruct 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-2-instruct" \ --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-2-instruct", "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-2-instruct" \ --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-2-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use appvoid/arco-2-instruct with Docker Model Runner:
docker model run hf.co/appvoid/arco-2-instruct
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base_model:
- appvoid/palmer-004-turbo
- appvoid/text-arco
- appvoid/arco-2
- appvoid/arco-reflection
- appvoid/arco-2-reasoning-20k
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
<style>
img{
user-select: none;
transition: all 0.2s ease;
border-radius: .5rem;
}
img:hover{
transform: rotate(2deg);
filter: invert(100%);
}
@import url('https://fonts.googleapis.com/css2?family=Vollkorn:ital,wght@0,400..900;1,400..900&display=swap');
</style>
<div style="background-color: transparent; border-radius: .5rem; padding: 2rem; font-family: monospace; font-size: .85rem; text-align: justify;">

this model was not trained, it is a merged of experts to improve instruction following tasks and reasoning.
#### prompt
there is no prompt intentionally set.
#### supporters
<a href="https://ko-fi.com/appvoid" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 34px !important; margin-top: -4px;width: 128px !important; filter: contrast(2) grayscale(100%) brightness(100%);" ></a>
### trivia
arco-2-instruct (codenamed as arco-exp-17) is a merge of reflection models that improved instruction accuracy a little better than the original one.
</div> |