Instructions to use appvoid/arco-reflection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/arco-reflection with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/arco-reflection")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/arco-reflection")
model = AutoModelForCausalLM.from_pretrained("appvoid/arco-reflection")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use appvoid/arco-reflection with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/arco-reflection"
# 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-reflection",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/arco-reflection

SGLang

How to use appvoid/arco-reflection 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-reflection" \
    --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-reflection",
		"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-reflection" \
        --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-reflection",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use appvoid/arco-reflection with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for appvoid/arco-reflection to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for appvoid/arco-reflection to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for appvoid/arco-reflection to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="appvoid/arco-reflection",
    max_seq_length=2048,
)

Docker Model Runner
How to use appvoid/arco-reflection with Docker Model Runner:
```
docker model run hf.co/appvoid/arco-reflection
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

this model is fine-tuned (and potentially overfitted) version of arco on a small reflection dataset.

the model works best with this format:

You are an AI system that returns a good <output> based on the reasoning made, always remember to return an <output> tag at the end. Instruction: <your prompt goes here>
<thinking>

as a mistake, the model is unable to understand when to stop so output tag should be set as stop criteria to avoid the model continue generating text, further versions won't have this issue.

Downloads last month: 8

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