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Gemma Ko 7B by beomi

 »  All LLMs  »  beomi  »  Gemma Ko 7B   URL Share it on

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  Autotrain compatible   Doi:10.57967/hf/1859   En   Endpoints compatible   Gemma   Ko   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/beomi/gemma-ko-7b 

Gemma Ko 7B Benchmarks

LLME Score: 0.20035
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma Ko 7B (beomi/gemma-ko-7b)

Gemma Ko 7B Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Content Creation and Communication, Research and Education
Applications:
Text Generation, NLP Research, Language Learning Tools, Knowledge Exploration
Primary Use Cases:
Question answering, Summarization, Reasoning
Limitations:
Biases or gaps in the training data can lead to limitations in the model's responses., LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language., They may generate incorrect or outdated factual statements., Lack the ability to apply common sense reasoning in certain situations.
Considerations:
Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques.
Additional Notes 
Open Large Language Models (LLMs) have a wide range of applications across various industries and domains.
Supported Languages 
ko (fluent), en (fluent)
Training Details 
Context Length:
2048
Hardware Used:
TPU
Model Architecture:
Text-to-text decoder-only
Responsible Ai Considerations 
Fairness:
LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material.
Transparency:
This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
Accountability:
Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases.
Mitigation Strategies:
Educational resources and reporting mechanisms for users to flag misuse are provided.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated Korean/English-language text
Performance Tips:
Longer context generally leads to better outputs, up to a certain point.
Release Notes 
Version:
7B
Date:
2024-03-08
Notes:
First release of Gemma-Ko 7B model
LLM NameGemma Ko 7B
Repository 🤗https://huggingface.co/beomi/gemma-ko-7b 
Model Size7b
Required VRAM17 GB
Updated2024-12-22
Maintainerbeomi
Model Typegemma
Model Files  2.9 GB: 1-of-6   2.9 GB: 2-of-6   3.0 GB: 3-of-6   2.9 GB: 4-of-6   2.9 GB: 5-of-6   2.4 GB: 6-of-6
Supported Languagesko en
Model ArchitectureGemmaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.38.1
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than beomi/gemma-ko-7b.

Rank the Gemma Ko 7B 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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Release v20241217
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