Instructions to use optimum-intel-internal-testing/phi-3.5-moe-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use optimum-intel-internal-testing/phi-3.5-moe-tiny-random with Transformers:

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

pipe = pipeline("text-generation", model="optimum-intel-internal-testing/phi-3.5-moe-tiny-random", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("optimum-intel-internal-testing/phi-3.5-moe-tiny-random", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("optimum-intel-internal-testing/phi-3.5-moe-tiny-random", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use optimum-intel-internal-testing/phi-3.5-moe-tiny-random with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "optimum-intel-internal-testing/phi-3.5-moe-tiny-random"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "optimum-intel-internal-testing/phi-3.5-moe-tiny-random",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/optimum-intel-internal-testing/phi-3.5-moe-tiny-random

SGLang

How to use optimum-intel-internal-testing/phi-3.5-moe-tiny-random 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 "optimum-intel-internal-testing/phi-3.5-moe-tiny-random" \
    --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": "optimum-intel-internal-testing/phi-3.5-moe-tiny-random",
		"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 "optimum-intel-internal-testing/phi-3.5-moe-tiny-random" \
        --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": "optimum-intel-internal-testing/phi-3.5-moe-tiny-random",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use optimum-intel-internal-testing/phi-3.5-moe-tiny-random with Docker Model Runner:
```
docker model run hf.co/optimum-intel-internal-testing/phi-3.5-moe-tiny-random
```

Add reference OpenVINO IRs Generated with: - transformers==5.15.1 - optimum-intel==2.3.0.dev0+402227e5 - openvino==2026.5.0-23665

by h0witended - opened 6 days ago

base: refs/heads/ov

←

from: refs/pr/3

Discussion Files changed

+1303

-895

Files changed (3) hide show

metadata.json +4 -4
openvino_model.bin +2 -2
openvino_model.xml +0 -0

metadata.json CHANGED Viewed

@@ -1,10 +1,10 @@
 {
   "model_id": "optimum-intel-internal-testing/phi-3.5-moe-tiny-random",
   "model_class": "OVModelForCausalLM",
-  "transformers_version": "5.10.4",
-  "optimum_intel_version": "2.3.0.dev0+e1c31ae1",
-  "openvino_version": "2026.4.0-22959-99c81491cc3-releases/2026/4",
-  "generated_date": "2026-09-23",
   "components": [
     "model"
   ]

 {
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   "model_class": "OVModelForCausalLM",
+  "transformers_version": "5.15.1",
+  "optimum_intel_version": "2.3.0.dev0+402227e5",
+  "openvino_version": "2026.5.0-23665",
+  "generated_date": "2026-10-29",
   "components": [
     "model"
   ]

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