What are the hardware requirements for Bloomz 560M?

Bloomz 560M requires approximately 1.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Bloomz 560M and how large is it?

Bloomz 560M is developed by bigscience, a model with 560m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Bloomz 560M perform on standard benchmarks?

Bloomz 560M has the following published scores: MMLU 25.1. Compare against reference models on this page or on the LLM Explorer leaderboards.

Bloomz 560M by bigscience — VRAM 1.1GB

Name: Bloomz 560M
Author: bigscience

Bloomz 560M is an open-source language model by bigscience. Features: 560m LLM, VRAM: 1.1GB, License: bigscience-bloom-rail-1.0, LLM Explorer Score: 0.18, Arc: 23.6, HellaSwag: 36.3, MMLU: 25.1.

Model Card on HF 🤗: https://huggingface.co/bigscience/bloomz-560m

Bloomz 560M Parameters and Internals

Model Type

text generation, multimodal

Use Cases

Areas:

research, multilingual tasks

Applications:

Coreference resolution, Natural language inference, Program synthesis, Sentence completion

Primary Use Cases:

Text generation, Multimodal tasks, Instruction following in multiple languages

Limitations:

Performance varies with prompt clarity, Recommended for English prompting

Considerations:

Ensure input clarity for optimal performance.

Supported Languages

ak (unknown), ar (unknown), as (unknown), bm (unknown), bn (unknown), ca (unknown), code (unknown), en (unknown), es (unknown), eu (unknown), fon (unknown), fr (unknown), gu (unknown), hi (unknown), id (unknown), ig (unknown), ki (unknown), kn (unknown), lg (unknown), ln (unknown), ml (unknown), mr (unknown), ne (unknown), nso (unknown), ny (unknown), or (unknown), pa (unknown), pt (unknown), rn (unknown), rw (unknown), sn (unknown), st (unknown), sw (unknown), ta (unknown), te (unknown), tn (unknown), ts (unknown), tum (unknown), tw (unknown), ur (unknown), vi (unknown), wo (unknown), xh (unknown), yo (unknown), zh (unknown), zu (unknown)

Training Details

Data Sources:

bigscience/xP3

Data Volume:

3.67 billion tokens

Methodology:

Multitask finetuning

Hardware Used:

64 A100 80GB GPUs with 8 GPUs per node (8 nodes)

Model Architecture:

Same as bloom-560m

Input Output

Input Format:

Natural language prompts

Accepted Modalities:

text

Output Format:

Textual responses

Performance Tips:

Ensure prompts are clear with a distinct stopping point.

LLM Name	Bloomz 560M
Repository 🤗	https://huggingface.co/bigscience/bloomz-560m
Model Size	560m
Required VRAM	1.1 GB
Updated	2026-08-05
Maintainer	bigscience
Model Type	bloom
Model Files	1.1 GB 1.1 GB
Supported Languages	ak ar as bm bn ca code en es eu fr gu hi id ig ki kn lg ln ml mr ne ny or pa pt rn rw sn st sw ta te tn ts tw ur vi wo xh yo zh zu
Model Architecture	BloomForCausalLM
License	bigscience-bloom-rail-1.0
Transformers Version	4.20.0
Tokenizer Class	BloomTokenizerFast
Padding Token	<pad>
Vocabulary Size	250880

Rank the Bloomz 560M capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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