Instructions to use Data-Selection/PDS-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Data-Selection/PDS-1B with Transformers:

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

pipe = pipeline("text-generation", model="Data-Selection/PDS-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Data-Selection/PDS-1B")
model = AutoModelForCausalLM.from_pretrained("Data-Selection/PDS-1B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Data-Selection/PDS-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Data-Selection/PDS-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Data-Selection/PDS-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Data-Selection/PDS-1B

SGLang

How to use Data-Selection/PDS-1B 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 "Data-Selection/PDS-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Data-Selection/PDS-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Data-Selection/PDS-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Data-Selection/PDS-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Data-Selection/PDS-1B with Docker Model Runner:
```
docker model run hf.co/Data-Selection/PDS-1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

PDS-1B

paper | code

PDS-1B is a 1B model with Mistral achitecture pre-trained from scratch on the data selected from the CC split of Redpajama, using the PDS framework.

The PDS framework is based on the Pontryagin's maximum principle for optimal pre-training data selection, which not only enjoy strong theoretical support but is also scalable for training large language models.

Please refer to our paper for more details.

Overview of the theory:

Overview of the PDS framework:

Evaluation

PDS-selected data improves the performance of language models pre-trained from scratch and saves pre-training comptation. The improvement scales up to large model sizes.

Baseline

Conventional Pre-training

Citation

@article{gu2024data,
  title={Data Selection via Optimal Control for Language Models},
  author={Gu, Yuxian and Dong, Li and Wang, Hongning and Hao, Yaru and Dong, Qingxiu and Wei, Furu and Huang, Minlie},
  journal={arXiv preprint arXiv:2410.07064},
  year={2024}
}

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Collection including Data-Selection/PDS-1B

PDS-Models

Collection

Models trained on PDS-Selected Data • 4 items • Updated Oct 28, 2024

Papers for Data-Selection/PDS-1B

Data Selection via Optimal Control for Language Models

Paper • 2410.07064 • Published Oct 9, 2024 • 9

Mistral 7B

Paper • 2310.06825 • Published Oct 10, 2023 • 58