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--- |
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license: mit |
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datasets: |
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- wikitext |
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--- |
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[pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) quantized to 4-bit using [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ). |
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To use, first install AutoGPTQ: |
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```shell |
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pip install auto-gptq |
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``` |
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Then load the model from the hub: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig |
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model_name = "smpanaro/pythia-1b-AutoGPTQ-4bit-128g" |
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model = AutoGPTQForCausalLM.from_quantized(model_name) |
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``` |
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|Model|4-Bit Perplexity|16-Bit Perplexity|Delta| |
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|--|--|--|--| |
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|[smpanaro/pythia-160m-AutoGPTQ-4bit-128g](https://huggingface.co/smpanaro/pythia-160m-AutoGPTQ-4bit-128g)|33.4375|23.3024|10.1351| |
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|[smpanaro/pythia-410m-AutoGPTQ-4bit-128g](https://huggingface.co/smpanaro/pythia-410m-AutoGPTQ-4bit-128g)|21.4688|13.9838|7.485| |
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|smpanaro/pythia-1b-AutoGPTQ-4bit-128g|12.0391|11.6178|0.4213| |
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<sub>Wikitext perplexity measured as in the [huggingface docs](https://huggingface.co/docs/transformers/en/perplexity), lower is better</sub> |
