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Keya 560M by papahawk

 »  All LLMs  »  papahawk  »  Keya 560M   URL Share it on

  98   
  Arxiv:1909.08053   Arxiv:2108.12409   Arxiv:2110.02861   Ak   Ar   As   Autotrain compatible   Bloom   Bm   Bn   Ca   Code   En   Endpoints compatible   Es   Eu   Fon   Fr   Gu   Hi   Id   Ig   Jax   Ki   Kn   Lg   Ln   Ml   Mr   Ne   Nso   Ny   Or   Pa   Pt   Pytorch   Region:us   Rn   Rw   Safetensors   Sn   St   Sw   Ta   Te   Tn   Ts   Tum   Tw   Ur   Vi   Wo   Xh   Yo   Zh   Zhs   Zht   Zu
Model Card on HF 🤗: https://huggingface.co/papahawk/keya-560m 

Keya 560M Benchmarks

LLME Score: 0.112
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Keya 560M (papahawk/keya-560m)

Keya 560M Parameters and Internals

Model Type 
Transformer-based Language Model
Use Cases 
Areas:
public research on large language models
Primary Use Cases:
Text generation, Exploring characteristics of language generated by a language model
Limitations:
Usage in biomedical, political, legal, or finance domains, Critical automatic decisions, Generating reliable factual predictions
Considerations:
Intentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model.
Supported Languages 
akel (/), ar (/), as (/), bm (/), bn (/), ca (/), code (text), en (/), es (/), eu (/), fon (/), fr (/), gu (/), hi (/), id (/), ig (/), ki (/), kn (/), lg (/), ln (/), ml (/), mr (/), ne (/), nso (/), ny (/), or (/), pa (/), pt (/), rn (/), rw (/), sn (/), st (/), sw (/), ta (/), te (/), tn (/), ts (/), tum (/), tw (/), ur (/), vi (/), wo (/), xh (/), yo (/), zh (/), zhs (/), zht (/), zu (/)
Training Details 
Data Sources:
45 natural languages, 12 programming languages
Data Volume:
1.5TB of pre-processed text, converted into 350B unique tokens
Context Length:
2048
Training Time:
Started 11th March, 2022 11:42am PST Ended 5th July, 2022
Hardware Used:
Jean Zay Public Supercomputer
Model Architecture:
Modified from Megatron-LM GPT2
Responsible Ai Considerations 
Fairness:
Model may overrepresent some viewpoints and underrepresent others.
Transparency:
Indirect users should be made aware when the content they're working with is created by the LLM.
Accountability:
Users of the model should provide mechanisms for those affected to provide feedback.
Mitigation Strategies:
Users should be aware of risks and limitations, and include an appropriate age disclaimer or blocking interface as necessary.
Release Notes 
Version:
0.1
Date:
22.June.2023
Notes:
Initial release.
LLM NameKeya 560M
Repository 🤗https://huggingface.co/papahawk/keya-560m 
Model Size560m
Required VRAM0 GB
Updated2025-02-22
Maintainerpapahawk
Model Typebloom
Model Files  0.0 GB   0.0 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 papahawk/keya-560m.

Rank the Keya 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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