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language:
- it
- en
tags:
- pretrained
- pytorch
- causal-lm
- minerva
- autoround
- intel-autoround
- autoawq
- auto-awq
- auto_awq
- 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
- Symmetrical Quantization
- Method AutoAWQ
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, True, '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-autoawq-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_awq', 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.
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