Instructions to use luiscalisto/Minerva-7B-Instruct-v1.0-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use luiscalisto/Minerva-7B-Instruct-v1.0-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Minerva-7B-Instruct-v1.0-MLX-4bit luiscalisto/Minerva-7B-Instruct-v1.0-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Minerva-7B-Instruct-v1.0-MLX-4bit
MLX 4-bit quantisation of sapienzanlp/Minerva-7B-instruct-v1.0,
converted for use on Apple Silicon via mlx-lm.
Source model
- Repository:
sapienzanlp/Minerva-7B-instruct-v1.0 - Release: 2024-11
- Family: minerva
- Origin: eu
- Languages / coverage: Italian + English + code, ~50/50 IT/EN training mix
- License: apache-2.0 (inherited)
Notes from upstream
Sapienza NLP, FAIR, CINECA. Italy's first LLM family pretrained from scratch on Italian. ~2.5T tokens (1.14T IT, 1.14T EN, 200B code).
Conversion details
- Tool:
mlx-lm0.31.3 - Quantisation: 4-bit (defaults from
mlx_lm.convert) - Converted on: 2026-05-05
Usage
from mlx_lm import load, generate
model, tokenizer = load("luiscalisto/Minerva-7B-Instruct-v1.0-MLX-4bit")
prompt = "Hello, who are you?"
print(generate(model, tokenizer, prompt=prompt, max_tokens=128, verbose=False))
License and attribution
This is a quantised redistribution of sapienzanlp/Minerva-7B-instruct-v1.0. The original model and
its license terms (apache-2.0) carry through unchanged. Please cite the
upstream authors when using this model. See the source repository for the
authoritative model card and citation.
Conversion provenance
Produced by llm-mlx-conversions,
a small utility for publishing community MLX 4-bit quants of open-weight LLMs.
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Model size
1B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
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Model tree for luiscalisto/Minerva-7B-Instruct-v1.0-MLX-4bit
Base model
sapienzanlp/Minerva-7B-base-v1.0 Finetuned
sapienzanlp/Minerva-7B-instruct-v1.0