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 21

Commit

b66d66c

1 Parent(s): 26ec3cc

Fix MODEL_PATH to use merged model

Browse files

Files changed (1) hide show

ubermenschetien_v2_full.py +1 -1

ubermenschetien_v2_full.py CHANGED Viewed

@@ -61,7 +61,7 @@ LOGS_DIR = os.path.join(ROOT, "improvement_logs")
 ROLLBACK_DIR = os.path.join(ROOT, "rollback_checkpoints")
 # Model paths
-MODEL_PATH = "NousResearch/Hermes-3-Llama-3.1-8B"
 DENSE_CHECKPOINT = os.path.join(ROOT, "dense_checkpoints/step_100")
 CFHOT_CHECKPOINT = os.path.join(ROOT, "cfhot_checkpoints/ckpt_5000")
 MULTI_HEAD_DIR = os.path.join(ROOT, "multi_head_checkpoints")

 ROLLBACK_DIR = os.path.join(ROOT, "rollback_checkpoints")
 # Model paths
+MODEL_PATH = "LoganResearch/ARC-Base-8B-Condensed"
 DENSE_CHECKPOINT = os.path.join(ROOT, "dense_checkpoints/step_100")
 CFHOT_CHECKPOINT = os.path.join(ROOT, "cfhot_checkpoints/ckpt_5000")
 MULTI_HEAD_DIR = os.path.join(ROOT, "multi_head_checkpoints")