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 / training_summary.json

ur-dad-matt

Upload folder using huggingface_hub

9dea9f9 verified 5 months ago

Raw

History Blame Contribute Delete

1.28 kB

	{
	"status": "complete",
	"base_model": "/mnt/1tb/Qwen2.5-14B-Instruct",
	"output_dir": "/mnt/1tb/outlier-40b-v3_2",
	"moe_layers": [
	11,
	12,
	13,
	14,
	15,
	16,
	17,
	18,
	19,
	20,
	21,
	22,
	23,
	24,
	25,
	26,
	27,
	28,
	29,
	30,
	31,
	32,
	33,
	34,
	35,
	36
	],
	"experts_per_layer": 8,
	"parallel_experts": 8,
	"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": {
	"router_loss": 0.050282299518585205,
	"router_time_s": 443.438959875999,
	"peak_gpu_gb": 98.52585554122925,
	"chunks": 7
	},
	"diversity": {
	"avg_pairwise_cosine": 0.9031525016813488,
	"min_pairwise_cosine": 0.6628175973892212,
	"avg_ternary_zero_rate": 0.3134468748019292
	},
	"alpha_stats": {
	"min": 0.007545572705566883,
	"max": 0.09620270878076553,
	"mean": 0.06583456943133989
	},
	"memory_estimate": {
	"expert_state_gb": 3.1640625,
	"rough_base_gb": 28.0,
	"rough_overhead_gb": 45.0,
	"rough_total_gb": 98.3125
	},
	"training_records": 39000
	}