Instructions to use Outlier-Ai/Outlier-150B-V3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Outlier-Ai/Outlier-150B-V3.2 with Transformers:

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

pipe = pipeline("text-generation", model="Outlier-Ai/Outlier-150B-V3.2", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Outlier-Ai/Outlier-150B-V3.2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Outlier-Ai/Outlier-150B-V3.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Outlier-Ai/Outlier-150B-V3.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Outlier-Ai/Outlier-150B-V3.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Outlier-Ai/Outlier-150B-V3.2

SGLang

How to use Outlier-Ai/Outlier-150B-V3.2 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-150B-V3.2" \
    --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": "Outlier-Ai/Outlier-150B-V3.2",
		"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 "Outlier-Ai/Outlier-150B-V3.2" \
        --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": "Outlier-Ai/Outlier-150B-V3.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Outlier-Ai/Outlier-150B-V3.2 with Docker Model Runner:
```
docker model run hf.co/Outlier-Ai/Outlier-150B-V3.2
```

Outlier-150B-V3.2

File size: 2,993 Bytes

---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
base_model: Qwen/Qwen2.5-72B-Instruct
base_model_relation: adapter
tags:
  - mixture-of-experts
  - moe
  - ternary
  - quantized
  - qwen2.5
  - outlier
  - local-llm
  - on-device
  - edge-ai
  - energy-efficient
  - sparse
  - overlay
  - research
  - apple-silicon
  - mac
  - mmlu-verified
language:
  - en
  - zh
  - fr
  - es
  - pt
  - de
  - it
  - ru
  - ja
  - ko
  - ar
  - vi
  - th
  - nl
  - pl
datasets: []
widget:
- example_title: Widget math
  messages:
  - role: user
    content: "If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? Show reasoning step by step."
- example_title: Logic grid
  messages:
  - role: user
    content: "Alice, Bob, and Carol each own a different pet (dog, cat, parrot). Alice does not own the dog. Bob owns the parrot. Who owns what?"
- example_title: Word problem
  messages:
  - role: user
    content: "A train leaves station A at 60 mph and another leaves station B, 180 miles away, at 40 mph traveling toward each other. When do they meet?"
model-index:
  - name: Outlier-150B-V3.2
    results:
      - task:
          type: text-generation
        dataset:
          name: MMLU
          type: cais/mmlu
        metrics:
          - type: accuracy
            value: 84.46
            name: MMLU 5-shot
---
> **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](https://outlier.host/?utm_source=hf&utm_medium=modelcard&utm_campaign=outlier_150b_v3_2)**.

# Outlier-150B-V3.2

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](https://huggingface.co/Outlier-Ai/Outlier-Nano-4B-MLX-4bit)
- [Outlier Core 27B — current default tier](https://huggingface.co/Outlier-Ai/Outlier-Core-27B-MLX-4bit)
- [Outlier Vision 35B-A3B — current multimodal tier](https://huggingface.co/Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit)
- [DeepSeek-R1-Distill-Qwen-7B — popular reasoning model](https://huggingface.co/Outlier-Ai/DeepSeek-R1-Distill-Qwen-7B-MLX-4bit)
- [Qwen3-Coder-30B-A3B — popular coding model](https://huggingface.co/Outlier-Ai/Qwen3-Coder-30B-A3B-Instruct-MLX-4bit)

For the latest verified benchmarks and downloads, visit **[outlier.host](https://outlier.host/?utm_source=hf&utm_medium=modelcard&utm_campaign=outlier_150b_v3_2)**.

## 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.