Simply make AI models cheaper, smaller, faster, and greener!

• Give a thumbs up if you like this model!
• Contact us and tell us which model to compress next here.
• Request access to easily compress your own AI models here.
• Read the documentations to know more here
• Join Pruna AI community on Discord here to share feedback/suggestions or get help.

Results

• How does the compression work? The model is compressed with hqq.
• How does the model quality change? The quality of the model output might vary compared to the base model.
• How is the model efficiency evaluated? These results were obtained with configuration described in model/smash_config.json and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
• What is the model format? We use safetensors.
• What calibration data has been used? If needed by the compression method, we used WikiText as the calibration data.
• What is the naming convention for Pruna Huggingface models? We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
• How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.
• What are "first" metrics? Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
• What are "Sync" and "Async" metrics? "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.

Setup

1. Check requirements from the original repo ibm-granite/granite-4.1-8b installed. In particular, check python, cuda, and transformers versions.
2. Make sure that you have installed quantization related packages.
   pip install hqq
3. Load & run the model.
   python

   1from transformers import AutoModelForCausalLM, AutoTokenizer 2from pruna import PrunaModel 3 4from hqq.engine.hf import HQQModelForCausalLM

Configurations

Credits & License

Want to compress other models?

• Contact us and tell us which model to compress next here.
• Request access to easily compress your own AI models here.