README.md

---
language:
- en
tags:
- gptq
- auto-gptq
- autogptq
- pytorch
- causal-lm
- autoround
- auto-round
- intel-autoround
- intel
- woq
- weights-only-quantization
license: apache-2.0
library_name: transformers
model_name: Minerva 1B base v1.0
base_model: sapienzanlp/Minerva-1B-base-v1.0
inference: false
model_creator: sapienzanlp
pipeline_tag: text-generation
prompt_template: '{prompt}
  '
quantized_by: fbaldassarri
---

## Model Information

Quantized version of [sapienzanlp/Minerva-1B-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-1B-base-v1.0) using `torch.bfloat16` for quantization tuning.
- 4 bits (INT4)
- group size = 64
- Symmetrical Quantization
- Method: WoQ — GPTQ (AutoGPTQ algorithm)

Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64)

Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.13.0

Note: this INT4 version of `Minerva 1B base v1.0` has been quantized for inference on Intel CPU, Intel iGPU (Arc) via intel-extension-for-pytorch, Intel NPU (AI Boost on Core Ultra series) via OpenVINO.

## Replication Recipe

The recommended way to reproduce this exact quantization is via the [auto-round-pipeline](https://git.epicdynamic.com/auto-round-pipeline) — the same orchestration tool that produced this artifact.

### Step 1 — Bootstrap the auto-round-pipeline

Set up a dedicated conda environment using the pipeline's `setup.sh`. Any of the install modes below produces an environment that can reproduce this quantization; pick the one that matches your goals:

```
git clone https://git.epicdynamic.com/auto-round-pipeline
cd auto-round-pipeline

# Pinned PyPI wheel (fastest; matches what this pipeline used by default):
bash setup.sh --pip-version 0.13.0

# Or build from intel/auto-round at the same tag (byte-identical reproducibility):
bash setup.sh --source-tag v0.13.0

# Intel Arc iGPU acceleration (e.g. Core Ultra 185H) — append to either of the above:
#   ... --intel-xpu
# NVIDIA / AMD opt-in: --cuda / --rocm
```

The script prints the resulting conda env name (something like `auto-round-pipeline-v0.13.0[-src][-xpu|-cuda|-rocm]`) at the end.

### Step 2 — Quantize just this model

Activate the env that `setup.sh` created, then invoke the runner with the same job filters that produced this artifact:

```
conda activate <env-name-printed-by-setup.sh>
python runner.py \
    --model 'sapienzanlp/Minerva-1B-base-v1.0' \
    --quant 'INT4-gs64' \
    --format auto_gptq \
    --no-upload      # drop this to also push to HuggingFace Hub
```

### Step 3 — (Optional) standalone Python recipe

If you'd rather call auto-round directly without the orchestration wrapper, this is the exact call the pipeline made:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_round import AutoRound

model_name = "sapienzanlp/Minerva-1B-base-v1.0"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)

bits, group_size, sym = 4, 64, True
autoround = AutoRound(
    model, tokenizer,
    bits=bits, group_size=group_size, sym=sym,
    device_map="cpu",
    nsamples=128, iters=200, seqlen=512, batch_size=4,
)
autoround.quantize_and_save("./AutoRound/sapienzanlp_Minerva-1B-base-v1.0-auto_gptq-int4-gs64-sym", format="auto_gptq")
```

## Actual Run Conditions

Recorded by the auto-round-pipeline at quantization time:

| Field | Value |
|---|---|
| Intel auto-round version | 0.13.0 |
| transformers version | 4.55.3 |
| torch version | 2.12.0+cpu |
| torch_dtype (load) | torch.bfloat16 |
| calibration device | `cpu` |
| calibration samples | 128 |
| tuning iterations | 200 |
| calibration seq len | 512 |
| calibration batch size | 4 |
| quantization duration | 6770.9s (112.8 min) |
| completed at (UTC) | 2026-06-14T20:27:10.055854+00:00 |

## 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.