Instructions to use WebOrganizer/LM-1b_1x-DCLMFasttext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WebOrganizer/LM-1b_1x-DCLMFasttext with Transformers:

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

pipe = pipeline("text-generation", model="WebOrganizer/LM-1b_1x-DCLMFasttext")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("WebOrganizer/LM-1b_1x-DCLMFasttext", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WebOrganizer/LM-1b_1x-DCLMFasttext with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WebOrganizer/LM-1b_1x-DCLMFasttext"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WebOrganizer/LM-1b_1x-DCLMFasttext",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WebOrganizer/LM-1b_1x-DCLMFasttext

SGLang

How to use WebOrganizer/LM-1b_1x-DCLMFasttext 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 "WebOrganizer/LM-1b_1x-DCLMFasttext" \
    --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": "WebOrganizer/LM-1b_1x-DCLMFasttext",
		"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 "WebOrganizer/LM-1b_1x-DCLMFasttext" \
        --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": "WebOrganizer/LM-1b_1x-DCLMFasttext",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WebOrganizer/LM-1b_1x-DCLMFasttext with Docker Model Runner:
```
docker model run hf.co/WebOrganizer/LM-1b_1x-DCLMFasttext
```

LM-1b_1x-DCLMFasttext

File size: 1,873 Bytes

fc4aea2

---
library_name: transformers
datasets:
- WebOrganizer/Corpus-200B
---
# WebOrganizer/LM-1b_1x-DCLMFasttext

[[Paper](https://arxiv.org/abs/2502.10341)] [[Website](https://weborganizer.allenai.org)] [[GitHub](https://github.com/CodeCreator/WebOrganizer)]

A 1.4B parameter model trained for 29B tokens from [WebOrganizer/Corpus-200B](https://huggingface.co/datasets/WebOrganizer/Corpus-200B). 

The training data for this model was selected via:
1. **Selection method**: Top scores from [DCLM-Fasttext Model](https://huggingface.co/mlfoundations/fasttext-oh-eli5)
2. **Domain definition**: n/a (global selection)
3. **Domain mixture**: n/a


## Repository Contents

Besides the HuggingFace model and tokenizer, the repository contains:
- `open_lm/`: Contains the OpenLM config and final checkpoint
- `evals/`: Evaluation results for various benchmarks
  - `core_9mcqa/`: Results of 9 multiple choice QA tasks with the OLMES evaluation framework
  - `mmlu/`: MMLU results with the OLMES evaluation framework
  - `dclm/`: Results using the DCLM evaluation framework
  - `perplexity/`: Perplexity results using the huggingface trainer
- `indices.tar.zst`: The indices for the selected documents in each shard of the Corpus-200B dataset used for training. The indices can be extracted with `tar --use-compress-program "zstd" -xf indices.tar.zst`.

## Usage

To use this model, you need to install the [open_lm](https://github.com/mlfoundations/open_lm) library and add `from open_lm.hf import *` before loading the model with `AutoModel.from_pretrained(...)`.


## Citation
```bibtex
@article{wettig2025organize,
  title={Organize the Web: Constructing Domains Enhances Pre-Training Data Curation},
  author={Alexander Wettig and Kyle Lo and Sewon Min and Hannaneh Hajishirzi and Danqi Chen and Luca Soldaini},
  journal={arXiv preprint arXiv:2502.10341},
  year={2025}
}
```