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")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Desktop
Docker Model Runner
How to use appvoid/arco-reflection with Docker Model Runner:
```
docker model run hf.co/appvoid/arco-reflection
```

appvoid commited on Sep 9, 2024

Commit

a6b5d45

verified ·

1 Parent(s): 0e5c6c9

Update README.md

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@@ -19,4 +19,6 @@ 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>
-```

 ```
 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>" should be set as stop criteria to avoid the model continue generating text, further versions won't have this issue.