---
language:
- it
- en
tags:
- pretrained
- pytorch
- causal-lm
- minerva
- autoround
- intel-autoround
- woq
- gptq
- autogptq
- auto-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 AutoGPTQ

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-autogptq-int4-gs128-asym"
  autoround.save_quantized(output_dir, format='auto_gptq', 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.