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
```

ur-dad-matt commited on Apr 29

Commit

dd669f5

verified ·

1 Parent(s): 6b7426e

docs: add multilingual tags, eval metadata, discovery improvements

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Files changed (1) hide show

README.md +14 -0

README.md CHANGED Viewed

@@ -24,6 +24,20 @@ tags:
   - text-generation
 language:
   - en
 ---
 > ⚠️ **SUPERSEDED — use [Outlier-Ai/Outlier-40B](https://huggingface.co/Outlier-Ai/Outlier-40B) instead.** These weights are retained live for reproducibility of earlier benchmark runs. All current research has moved to the successor.

   - text-generation
 language:
   - en
+  - zh
+  - fr
+  - es
+  - pt
+  - de
+  - it
+  - ru
+  - ja
+  - ko
+  - ar
+  - vi
+  - th
+  - nl
+  - pl
 ---
 > ⚠️ **SUPERSEDED — use [Outlier-Ai/Outlier-40B](https://huggingface.co/Outlier-Ai/Outlier-40B) instead.** These weights are retained live for reproducibility of earlier benchmark runs. All current research has moved to the successor.