Instructions to use LoganResearch/ARC-Base-8B-Condensed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoganResearch/ARC-Base-8B-Condensed with Transformers:

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

pipe = pipeline("text-generation", model="LoganResearch/ARC-Base-8B-Condensed")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LoganResearch/ARC-Base-8B-Condensed", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoganResearch/ARC-Base-8B-Condensed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoganResearch/ARC-Base-8B-Condensed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoganResearch/ARC-Base-8B-Condensed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoganResearch/ARC-Base-8B-Condensed

SGLang

How to use LoganResearch/ARC-Base-8B-Condensed 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 "LoganResearch/ARC-Base-8B-Condensed" \
    --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": "LoganResearch/ARC-Base-8B-Condensed",
		"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 "LoganResearch/ARC-Base-8B-Condensed" \
        --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": "LoganResearch/ARC-Base-8B-Condensed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoganResearch/ARC-Base-8B-Condensed with Docker Model Runner:
```
docker model run hf.co/LoganResearch/ARC-Base-8B-Condensed
```

LoganResearch commited on Jan 23

Commit

8bc141e

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1 Parent(s): 8ef1125

Update README.md

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@@ -881,7 +881,6 @@ The specification covers:
 ---
 ## Citation
 ```bibtex
 @software{napolitano2025arc,
   author       = {Napolitano, Logan Matthew},
@@ -893,7 +892,7 @@ The specification covers:
   license      = {CC BY 4.0}
 }
 ```
 @article{napolitano2025controlled,
   author       = {Napolitano, Logan Matthew},
   title        = {Controlled Language Models: Decode-Time Behavioral Control and Token Efficiency},
@@ -903,8 +902,7 @@ The specification covers:
   publisher    = {Zenodo},
   note         = {Primary technical reference for ARC-Base-8B-Condensed}
 }
 ```bibtex
 @article{napolitano2025controlfield,
   author       = {Napolitano, Logan Matthew},
@@ -916,8 +914,6 @@ The specification covers:
 }
 ```
----
 ## References
 1. Zou, A., et al. (2023). Representation Engineering: A Top-Down Approach to AI Transparency. arXiv:2310.01405

 ---
 ## Citation
 ```bibtex
 @software{napolitano2025arc,
   author       = {Napolitano, Logan Matthew},
   license      = {CC BY 4.0}
 }
 ```
+```bibtex
 @article{napolitano2025controlled,
   author       = {Napolitano, Logan Matthew},
   title        = {Controlled Language Models: Decode-Time Behavioral Control and Token Efficiency},
   publisher    = {Zenodo},
   note         = {Primary technical reference for ARC-Base-8B-Condensed}
 }
+```
 ```bibtex
 @article{napolitano2025controlfield,
   author       = {Napolitano, Logan Matthew},
 }
 ```
 ## References
 1. Zou, A., et al. (2023). Representation Engineering: A Top-Down Approach to AI Transparency. arXiv:2310.01405