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--- |
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language: |
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- en |
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tags: |
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- pytorch |
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- causal-lm |
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- pythia |
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- autoround |
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- intel-autoround |
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- gptq |
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- autogptq |
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- auto-gptq |
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- woq |
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- intel |
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license: apache-2.0 |
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model_name: Pythia 6.9b deduped |
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base_model: EleutherAI/pythia-6.9b-deduped |
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inference: false |
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model_creator: EleutherAI |
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datasets: |
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- EleutherAI/pile |
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pipeline_tag: text-generation |
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prompt_template: '{prompt} |
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' |
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quantized_by: fbaldassarri |
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--- |
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## Model Information |
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Quantized version of [EleutherAI/pythia-6.9b-deduped](https://huggingface.co/EleutherAI/pythia-6.9b-deduped) using torch.float32 for quantization tuning. |
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- 4 bits (INT4) |
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- group size = 128 |
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- Symmetrical Quantization |
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- Method AutoGPTQ |
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Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.1 |
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Note: this INT4 version of pythia-6.9b-deduped has been quantized to run inference through CPU. |
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## Replication Recipe |
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### Step 1 Install Requirements |
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I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. |
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``` |
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wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.1.tar.gz |
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tar -xvzf v0.4.1.tar.gz |
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cd auto-round-0.4.1 |
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pip install -r requirements-cpu.txt --upgrade |
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``` |
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### Step 2 Build Intel AutoRound wheel from sources |
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``` |
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pip install -vvv --no-build-isolation -e .[cpu] |
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``` |
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### Step 3 Script for Quantization |
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``` |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "EleutherAI/pythia-6.9b-deduped" |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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from auto_round import AutoRound |
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bits, group_size, sym, device, amp = 4, 128, True, 'cpu', False |
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autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) |
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autoround.quantize() |
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output_dir = "./AutoRound/EleutherAI_pythia-6.9b-deduped-autogptq-int4-gs128-sym" |
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autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) |
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``` |
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## License |
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[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) |
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## Disclaimer |
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This quantized model comes with no warranty. It has been developed only for research purposes. |
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