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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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+
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+ [gpt2-medium](https://huggingface.co/openai-community/gpt2-medium) quantized to 4-bit using [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ).
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+
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+ To use, first install AutoGPTQ:
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+
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+ ```shell
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+ pip install auto-gptq
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+ ```
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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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+
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+ model_name = "smpanaro/gpt2-medium-AutoGPTQ-4bit-128g"
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+ model = AutoGPTQForCausalLM.from_quantized(model_name)
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+ ```
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+
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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/gpt2-AutoGPTQ-4bit-128g](https://huggingface.co/smpanaro/gpt2-AutoGPTQ-4bit-128g)|26.5000|25.1875|1.3125|
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+ |smpanaro/gpt2-medium-AutoGPTQ-4bit-128g|19.1719|18.4739|0.698|
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+ |[smpanaro/gpt2-large-AutoGPTQ-4bit-128g](https://huggingface.co/smpanaro/gpt2-large-AutoGPTQ-4bit-128g)|16.6875|16.4541|0.2334|
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+ |[smpanaro/gpt2-xl-AutoGPTQ-4bit-128g](https://huggingface.co/smpanaro/gpt2-xl-AutoGPTQ-4bit-128g)|14.9297|14.7951|0.1346|
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+ <sub>Wikitext perplexity measured as in the [huggingface docs](https://huggingface.co/docs/transformers/en/perplexity)</sub>