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Bloomz 560M by bigscience

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  9274337   
  Arxiv:2211.01786   Ak   Ar   As   Autotrain compatible   Bloom   Bm   Bn   Ca   Code   Dataset:bigscience/xp3   En   Endpoints compatible   Es   Eu   Fon   Fr   Gu   Hi   Id   Ig   Ki   Kn   Lg   Ln   Ml   Model-index   Mr   Ne   Nso   Ny   Or   Pa   Pt   Pytorch   Region:us   Rn   Rw   Safetensors   Sn   St   Sw   Ta   Te   Tensorboard   Tn   Ts   Tum   Tw   Ur   Vi   Wo   Xh   Yo   Zh   Zu
Model Card on HF 🤗: https://huggingface.co/bigscience/bloomz-560m 

Bloomz 560M Benchmarks

ARC: 23.55 vs 96.7 (so35)-75.6%
HellaSwag: 36.31 vs 95.3 (gpt4)-61.9%
MMLU: 25.1 vs 88.3 (so35)-71.6%
TruthfulQA: 45.69 vs 59 (gpt4)-22.6%
WinoGrande: 53.12 vs 87.5 (gpt4)-39.3%
LLME Score: 0.24943
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bloomz 560M (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 NameBloomz 560M
Repository 🤗https://huggingface.co/bigscience/bloomz-560m 
Model Size560m
Required VRAM1.1 GB
Updated2024-12-26
Maintainerbigscience
Model Typebloom
Model Files  1.1 GB   1.1 GB
Supported Languagesak 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 ArchitectureBloomForCausalLM
Licensebigscience-bloom-rail-1.0
Transformers Version4.20.0
Tokenizer ClassBloomTokenizerFast
Padding Token<pad>
Vocabulary Size250880

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Note: green Score (e.g. "73.2") means that the model is better than bigscience/bloomz-560m.

Rank the Bloomz 560M Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217
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