| --- |
| language: |
| - it |
| - en |
| tags: |
| - pretrained |
| - pytorch |
| - causal-lm |
| - minerva |
| - autoround |
| - intel-autoround |
| - woq |
| - gptq |
| - intel |
| license: apache-2.0 |
| model_name: Minerva 7B instruct v1.0 |
| base_model: |
| - sapienzanlp/Minerva-7B-instruct-v1.0 |
| inference: false |
| model_creator: sapienzanlp |
| datasets: |
| - uonlp/CulturaX |
| pipeline_tag: text-generation |
| prompt_template: '{prompt} |
| ' |
| quantized_by: fbaldassarri |
| --- |
| |
| ## Model Information |
|
|
| Quantized version of [sapienzanlp/Minerva-7B-instruct-v1.0](https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0) using torch.float32 for quantization tuning. |
| - 4 bits (INT4) |
| - group size = 128 |
| - Asymmetrical Quantization |
| - Method WoQ (AutoRound format) |
|
|
| Fast and low memory, 2-3X speedup (slight accuracy drop at W4G128) |
|
|
| Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.3 |
|
|
| Note: this INT4 version of Minerva-7B-instruct-v1.0 has been quantized to run inference through CPU. |
|
|
| ## Replication Recipe |
|
|
| ### Step 1 Install Requirements |
|
|
| I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. |
|
|
| ``` |
| wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.3.tar.gz |
| tar -xvzf v0.4.3.tar.gz |
| cd auto-round-0.4.3 |
| pip install -r requirements-cpu.txt --upgrade |
| ``` |
|
|
| ### Step 2 Build Intel AutoRound wheel from sources |
|
|
| ``` |
| pip install -vvv --no-build-isolation -e .[cpu] |
| ``` |
|
|
| ### Step 3 Script for Quantization |
|
|
| ``` |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| model_name = "sapienzanlp/Minerva-7B-instruct-v1.0" |
| model = AutoModelForCausalLM.from_pretrained(model_name) |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| from auto_round import AutoRound |
| bits, group_size, sym, device, amp = 4, 128, False, 'cpu', False |
| 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) |
| autoround.quantize() |
| output_dir = "./AutoRound/sapienzanlp_Minerva-7B-instruct-v1.0-autoround-int4-gs128-asym" |
| autoround.save_quantized(output_dir, format='auto_round', inplace=True) |
| ``` |
|
|
| ## License |
|
|
| [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) |
|
|
| ## Disclaimer |
|
|
| This quantized model comes with no warranty. It has been developed only for research purposes. |
|
|
|
|
