Instructions to use Outlier-Ai/Outlier-10B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Outlier-Ai/Outlier-10B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Outlier-Ai/Outlier-10B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import OutlierMoE model = OutlierMoE.from_pretrained("Outlier-Ai/Outlier-10B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Outlier-Ai/Outlier-10B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Outlier-Ai/Outlier-10B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Outlier-Ai/Outlier-10B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Outlier-Ai/Outlier-10B
- SGLang
How to use Outlier-Ai/Outlier-10B 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 "Outlier-Ai/Outlier-10B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Outlier-Ai/Outlier-10B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Outlier-Ai/Outlier-10B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Outlier-Ai/Outlier-10B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Outlier-Ai/Outlier-10B with Docker Model Runner:
docker model run hf.co/Outlier-Ai/Outlier-10B
Superseded. This repo is a research artifact from an earlier Outlier lineage and is no longer the recommended download. The current shipping tier is at outlier.host.
Outlier-10B
This repo predates the v1.8 Outlier lineup. It is preserved here for reproducibility and historical reference, not as a production recommendation.
What replaced it
Current shipping tiers (see Outlier app v1.8+):
- Outlier Nano 4B — current entry tier
- Outlier Core 27B — current default tier
- Outlier Vision 35B-A3B — current multimodal tier
- DeepSeek-R1-Distill-Qwen-7B — popular reasoning model
- Qwen3-Coder-30B-A3B — popular coding model
For the latest verified benchmarks and downloads, visit outlier.host.
Original notes
This was a research / preview artifact. It may contain experimental adapters, overlays, or quantization variants that did not graduate into the shipping product. Treat any technical claims in earlier revisions of this card as provisional.
License
See YAML frontmatter above. Original license terms preserved.
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Evaluation results
- MMLU 5-shot on MMLUself-reported70.870