--- license: apache-2.0 datasets: - gair-prox/FineWeb-pro language: - en tags: - llama pipeline_tag: text-generation library_name: transformers --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/FW-ProX-1.7B-GGUF This is quantized version of [gair-prox/FW-ProX-1.7B](https://huggingface.co/gair-prox/FW-ProX-1.7B) created using llama.cpp # Original Model Card # FW-ProX-1.7B
[ArXiv](https://arxiv.org/abs/2409.17115) | [Models](https://huggingface.co/gair-prox/FW-ProX-1.7B) | [Data](https://huggingface.co/datasets/gair-prox/FineWeb-pro) | [Code](https://github.com/GAIR-NLP/program-every-example) **FW-ProX-1.7B** is a small language model. It was and trained on the [FineWeb-pro](https://huggingface.co/datasets/gair-prox/FineWeb-pro) for 50B tokens. ## Evaluations ProX models are evaluated over 10 language model benchmarks in zero-shot setting. | | ArC-c | ARC-e | CSQA | HellaS | MMLU | OBQA | PiQA | SIQA | WinoG | SciQ | AVG | |-----------------------|-------|-------|-------|-----------|-------|-------|-------|-------|-------|-------|------| | raw | 28.5 | 52.6 | 33.9 | 53.2 | 29.8 | 32.6 | 72.9 | 40.2 | 53.0 | 77.1 | 47.4 | | ours | 34.4 | 63.9 | 32.6 | 53.0 | 33.1 | 34.4 | 73.1 | 39.3 | 52.7 | 81.5 | 49.8 | ### Citation ``` @article{zhou2024programming, title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale}, author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei}, journal={arXiv preprint arXiv:2409.17115}, year={2024} } ```