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

Libraries

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

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

pipe = pipeline("text-generation", model="mllmTeam/PhoneLM-1.5B-Instruct", 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-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

mllmTeam commited on Nov 12, 2024

Commit

b007511

verified ·

1 Parent(s): 22de7a2

Update README.md

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@@ -64,5 +64,18 @@ The model is a decoder-only transformer architecture with the following modifica
 * **ReLU Activation Function**: ReLU([Glorot et al., 2011](https://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf)) activation functions are adopted in feed-forward networks.
 * **Tokenizer**: We use the SmolLM([Allal et al., 2024](https://huggingface.co/blog/smollm))'s tokenizer with a vocabulary size of 49,152.
-## LICENSE
-* This repository is released under the [Apache-2.0](https://huggingface.co/mllmTeam/PhoneLM-1.5B-Instruct/blob/main/LICENSE) License.

 * **ReLU Activation Function**: ReLU([Glorot et al., 2011](https://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf)) activation functions are adopted in feed-forward networks.
 * **Tokenizer**: We use the SmolLM([Allal et al., 2024](https://huggingface.co/blog/smollm))'s tokenizer with a vocabulary size of 49,152.
+## License
+* This repository is released under the [Apache-2.0](https://huggingface.co/mllmTeam/PhoneLM-1.5B-Instruct/blob/main/LICENSE) License.
+## Citation
+```
+@misc{yi2024phonelmanefficientcapablesmall,
+      title={PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-training},
+      author={Rongjie Yi and Xiang Li and Weikai Xie and Zhenyan Lu and Chenghua Wang and Ao Zhou and Shangguang Wang and Xiwen Zhang and Mengwei Xu},
+      year={2024},
+      eprint={2411.05046},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2411.05046},
+}
+```