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
```

ur-dad-matt commited on Apr 10

Commit

dc24945

verified ·

1 Parent(s): 284f0b7

Upload configuration_outlier_150b_rexmoe.py with huggingface_hub

Browse files

Files changed (1) hide show

configuration_outlier_150b_rexmoe.py +23 -0

configuration_outlier_150b_rexmoe.py ADDED Viewed

	@@ -0,0 +1,23 @@

+from transformers import PretrainedConfig
+class OutlierReXMoEConfig(PretrainedConfig):
+    model_type = "outlier_150b_rexmoe"
+    def __init__(
+        self,
+        base_model_name_or_path=None,
+        moe_layers=None,
+        n_experts=8,
+        top_k=2,
+        psr_scales=None,
+        group_size=4,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.base_model_name_or_path = base_model_name_or_path
+        self.moe_layers = list(moe_layers or [])
+        self.n_experts = int(n_experts)
+        self.top_k = int(top_k)
+        self.psr_scales = list(psr_scales or [0.7, 0.9, 1.1, 1.3])
+        self.group_size = int(group_size)