Instructions to use MiniLLM/MiniPLM-Qwen-200M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniLLM/MiniPLM-Qwen-200M with Transformers:

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

pipe = pipeline("text-generation", model="MiniLLM/MiniPLM-Qwen-200M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MiniLLM/MiniPLM-Qwen-200M")
model = AutoModelForCausalLM.from_pretrained("MiniLLM/MiniPLM-Qwen-200M")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MiniLLM/MiniPLM-Qwen-200M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniLLM/MiniPLM-Qwen-200M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MiniLLM/MiniPLM-Qwen-200M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniLLM/MiniPLM-Qwen-200M

SGLang

How to use MiniLLM/MiniPLM-Qwen-200M 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 "MiniLLM/MiniPLM-Qwen-200M" \
    --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": "MiniLLM/MiniPLM-Qwen-200M",
		"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 "MiniLLM/MiniPLM-Qwen-200M" \
        --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": "MiniLLM/MiniPLM-Qwen-200M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniLLM/MiniPLM-Qwen-200M with Docker Model Runner:
```
docker model run hf.co/MiniLLM/MiniPLM-Qwen-200M
```

nielsr HF Staff commited on Oct 27, 2024

Commit

e41e649

verified ·

1 Parent(s): 543ee2a

Add link to paper

Browse files

This PR ensures the model can be viewed at https://huggingface.co/papers/2410.17215.

Feel free to update the other model cards, and add the paper to the collection :)

Files changed (1) hide show

README.md +11 -1

README.md CHANGED Viewed

@@ -37,4 +37,14 @@ MiniPLM models achieves better performance given the same computation and scales
 ## Citation
-TODO

 ## Citation
+```bibtex
+@misc{gu2024miniplmknowledgedistillationpretraining,
+      title={MiniPLM: Knowledge Distillation for Pre-Training Language Models},
+      author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang},
+      year={2024},
+      eprint={2410.17215},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.17215},
+}
+```