Instructions to use MiniLLM/Ref-Pretrain-Qwen-104M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniLLM/Ref-Pretrain-Qwen-104M with Transformers:

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

pipe = pipeline("text-generation", model="MiniLLM/Ref-Pretrain-Qwen-104M")
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("MiniLLM/Ref-Pretrain-Qwen-104M")
model = AutoModelForCausalLM.from_pretrained("MiniLLM/Ref-Pretrain-Qwen-104M", 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 MiniLLM/Ref-Pretrain-Qwen-104M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniLLM/Ref-Pretrain-Qwen-104M"
# 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/Ref-Pretrain-Qwen-104M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniLLM/Ref-Pretrain-Qwen-104M

SGLang

How to use MiniLLM/Ref-Pretrain-Qwen-104M 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/Ref-Pretrain-Qwen-104M" \
    --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/Ref-Pretrain-Qwen-104M",
		"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/Ref-Pretrain-Qwen-104M" \
        --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/Ref-Pretrain-Qwen-104M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniLLM/Ref-Pretrain-Qwen-104M with Docker Model Runner:
```
docker model run hf.co/MiniLLM/Ref-Pretrain-Qwen-104M
```

Ref-Pretrain-Qwen-104M

paper | code

Ref-Pretrain-Qwen-104M is a 104M model with Qwen achitecture conventionally pre-trained from scratch on the Pile for 5B tokens.

We also open-source the tokenized pre-training corpus for reproducibility.

It is used as the reference model in the MiniPLM knwoledge distillation framework to construct the refined pre-training corpus. The data is then used to train MiniPLM models.

Evaluation

MiniPLM models achieves better performance given the same computation and scales well across model sizes:

Citation

@article{miniplm,
    title={MiniPLM: Knowledge Distillation for Pre-Training Language Models}, 
    author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang},
    journal={arXiv preprint arXiv:2410.17215},
    year={2024}
}

Downloads last month: 683

Safetensors

Model size

0.1B params

Tensor type

F32

Datasets used to train MiniLLM/Ref-Pretrain-Qwen-104M

Paper for MiniLLM/Ref-Pretrain-Qwen-104M

MiniPLM: Knowledge Distillation for Pre-Training Language Models

Paper • 2410.17215 • Published Oct 22, 2024 • 16