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
File size: 1,401 Bytes
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"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 8192,
"initializer_range": 0.02,
"intermediate_size": 29568,
"max_position_embeddings": 32768,
"max_window_layers": 70,
"model_type": "outlier_150b_rexmoe",
"num_attention_heads": 64,
"num_hidden_layers": 80,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_theta": 1000000.0,
"sliding_window": 131072,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.43.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064,
"base_model_name_or_path": "/mnt/1tb/Qwen2.5-72B-Instruct",
"moe_layers": [
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],
"n_experts": 8,
"top_k": 2,
"psr_scales": [
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],
"group_size": 4,
"auto_map": {
"AutoConfig": "configuration_outlier_150b_rexmoe.OutlierReXMoEConfig",
"AutoModelForCausalLM": "modeling_outlier_150b_rexmoe.OutlierReXMoEForCausalLM"
}
} |