Text Generation
Transformers
outlier_150b_rexmoe
mixture-of-experts
Mixture of Experts
ternary
quantized
qwen2.5
outlier
local-llm
on-device
edge-ai
energy-efficient
sparse
overlay
research
apple-silicon
mac
mmlu-verified
custom_code
Eval Results (legacy)
Instructions to use Outlier-Ai/Outlier-150B-V3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "group_01_expert_00": 0.013282203115522861, | |
| "group_01_expert_01": 0.007036757655441761, | |
| "group_01_expert_02": 0.022870026528835297, | |
| "group_01_expert_03": 0.013156086206436157, | |
| "group_01_expert_04": 0.015288042835891247, | |
| "group_01_expert_05": 0.005860608536750078, | |
| "group_01_expert_06": 0.02469489350914955, | |
| "group_01_expert_07": 0.016237638890743256, | |
| "group_02_expert_00": 0.018590012565255165, | |
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| "group_02_expert_07": 0.019574716687202454, | |
| "group_03_expert_00": 0.010419095866382122, | |
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| "group_09_expert_00": 0.004305507522076368, | |
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| "group_10_expert_00": 0.0071843089535832405, | |
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| "group_11_expert_00": 0.003223187057301402, | |
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| } |