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

Libraries

How to use appvoid/arco-reasoner with Transformers:

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use appvoid/arco-reasoner with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

appvoid commited on Sep 7, 2024

Commit

1683e12

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1 Parent(s): 78360d1

Update README.md

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@@ -14,6 +14,24 @@ tags:
 experimental model to expose arco to some reasoning
 # Uploaded  model
 - **Developed by:** appvoid

 experimental model to expose arco to some reasoning
+| Task         | Score | Metric    |
+|--------------|-------|-----------|
+| ARC Challenge| 0.3473| acc_norm  |
+| HellaSwag    | 0.5986| acc_norm  |
+| MMLU         | 0.2489| acc       |
+| PIQA         | 0.7318| acc_norm  |
+| Winogrande   | 0.6259| acc       |
+This table presents the extracted scores in a clear, tabular format. The "Task" column shows the name of each benchmark, the "Score" column displays the corresponding value, and the "Metric" column indicates whether the score is acc_norm or acc.
+format is this:
+```
+Instruction: <your instruction>
+Reasoning: // starting from here, the model will start to generate the resoning and output
+Output:
+```
 # Uploaded  model
 - **Developed by:** appvoid