Instructions to use Outlier-Ai/Outlier-40B-V3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Outlier-Ai/Outlier-40B-V3.2 with Transformers:

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

pipe = pipeline("text-generation", model="Outlier-Ai/Outlier-40B-V3.2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Outlier-Ai/Outlier-40B-V3.2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Outlier-Ai/Outlier-40B-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-40B-V3.2"
# 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-40B-V3.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Outlier-Ai/Outlier-40B-V3.2

SGLang

How to use Outlier-Ai/Outlier-40B-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-40B-V3.2" \
    --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-40B-V3.2",
		"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-40B-V3.2" \
        --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-40B-V3.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Outlier-Ai/Outlier-40B-V3.2 with Docker Model Runner:
```
docker model run hf.co/Outlier-Ai/Outlier-40B-V3.2
```

Outlier-40B-V3.2

File size: 1,275 Bytes

9dea9f9

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  "output_dir": "/mnt/1tb/outlier-40b-v3_2",
  "moe_layers": [
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  "steps_per_expert": 1500,
  "batch_size": 4,
  "lr": 0.001,
  "router_steps": 100,
  "router_lr": 0.001,
  "top_k_logits": 128,
  "topk_experts": 2,
  "corpus_examples": 5000,
  "elapsed_min": 368.89529099485,
  "peak_gpu_gb": 98.52585554122925,
  "router_summary": {
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    "chunks": 7
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    "avg_ternary_zero_rate": 0.3134468748019292
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  "alpha_stats": {
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    "max": 0.09620270878076553,
    "mean": 0.06583456943133989
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  "memory_estimate": {
    "expert_state_gb": 3.1640625,
    "rough_base_gb": 28.0,
    "rough_overhead_gb": 45.0,
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  "training_records": 39000
}