Instructions to use mllmTeam/PhoneLM-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mllmTeam/PhoneLM-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="mllmTeam/PhoneLM-1.5B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mllmTeam/PhoneLM-1.5B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mllmTeam/PhoneLM-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mllmTeam/PhoneLM-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mllmTeam/PhoneLM-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mllmTeam/PhoneLM-1.5B

SGLang

How to use mllmTeam/PhoneLM-1.5B 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 "mllmTeam/PhoneLM-1.5B" \
    --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": "mllmTeam/PhoneLM-1.5B",
		"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 "mllmTeam/PhoneLM-1.5B" \
        --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": "mllmTeam/PhoneLM-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mllmTeam/PhoneLM-1.5B with Docker Model Runner:
```
docker model run hf.co/mllmTeam/PhoneLM-1.5B
```

Update README.md

by zhuzeyuan - opened Jun 18, 2025

base: refs/heads/main

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@@ -67,7 +67,7 @@ The training dataset PhoneLM used is comprised of a filtered mixture of open-sou
 | MobileLLaMA-1.4B | 56.1 | 59.4 | 73.0 | 81.9 | 56.7 | 55.8 | 30.3 | 59.03 |
 | MobiLlama-1B | 62.2 | 59.3 | 74.8 | 82.8 | 60.3 | 56.4 | 31.7 | 61.07 |
 | OpenELM-1.1B | 64.8 | 61.7 | 75.6 | 83.6 | 63.6 | 55.4 | 32.3 | 62.43 |
-| DCLM-1.4B | 53.6 | 66.3 | 77.0 | 94.0 | 71.4 | 74.8 | 41.2 | 68.33 |
 | SmolLM-1.7B | 49.6 | 60.9 | 75.8 | 93.2 | 66.0 | 76.4 | 43.5 | 66.49 |
 | Qwen 1.5-1.8B | 60.9 | 60.5 | 74.2 | 89.4 | 66.5 | 59.1 | 34.7 | 63.61 |
 | Galactica-1.3B | 41.0 | 54.4 | 63.8 | 87.7 | 62.0 | 58.6 | 30.5 | 56.86 |

 | MobileLLaMA-1.4B | 56.1 | 59.4 | 73.0 | 81.9 | 56.7 | 55.8 | 30.3 | 59.03 |
 | MobiLlama-1B | 62.2 | 59.3 | 74.8 | 82.8 | 60.3 | 56.4 | 31.7 | 61.07 |
 | OpenELM-1.1B | 64.8 | 61.7 | 75.6 | 83.6 | 63.6 | 55.4 | 32.3 | 62.43 |
+| DCLM-1.4B | 71.7 | 66.3 | 77.0 | 94.0 | 71.4 | 74.8 | 41.2 | 68.33 |
 | SmolLM-1.7B | 49.6 | 60.9 | 75.8 | 93.2 | 66.0 | 76.4 | 43.5 | 66.49 |
 | Qwen 1.5-1.8B | 60.9 | 60.5 | 74.2 | 89.4 | 66.5 | 59.1 | 34.7 | 63.61 |
 | Galactica-1.3B | 41.0 | 54.4 | 63.8 | 87.7 | 62.0 | 58.6 | 30.5 | 56.86 |