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Distilgpt2 Nepali by Sakonii

 »  All LLMs  »  Sakonii  »  Distilgpt2 Nepali   URL Share it on

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  Arxiv:1911.02116   Autotrain compatible Dataset:sakonii/nepalitext-lan...   Endpoints compatible   Generated from trainer   Gpt2   Pytorch   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/Sakonii/distilgpt2-nepali 

Distilgpt2 Nepali Benchmarks

LLME Score: 0.01072
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Distilgpt2 Nepali (Sakonii/distilgpt2-nepali)

Distilgpt2 Nepali Parameters and Internals

Model Type 
Causal Language Model
Use Cases 
Areas:
Nepali text generation
Applications:
Fine-tuning on Nepali language focused downstream tasks
Primary Use Cases:
Nepali text generation
Limitations:
May not perform satisfactorily on shorter sequences due to training on text blocks of size 512 tokens
Supported Languages 
Nepali (native)
Training Details 
Data Sources:
nepalitext-language-model-dataset, OSCAR, cc100, Wikipedia
Data Volume:
13 million text sequences
Methodology:
Causal language modeling (CLM) objective using Sentence Piece Model (SPM) for text tokenization similar to XLM-ROBERTa
Context Length:
512
Training Time:
around 188.8K training steps
Input Output 
Input Format:
text sequence up to 512 tokens
Accepted Modalities:
text
Output Format:
text generation
Performance Tips:
Set a seed for reproducibility due to reliance on randomness in text generation.
LLM NameDistilgpt2 Nepali
Repository 🤗https://huggingface.co/Sakonii/distilgpt2-nepali 
Model Size88.2m
Required VRAM0.3 GB
Updated2024-12-22
MaintainerSakonii
Model Typegpt2
Model Files  0.3 GB   0.3 GB   0.0 GB
Model ArchitectureGPT2LMHeadModel
Licenseapache-2.0
Model Max Length512
Transformers Version4.17.0
Tokenizer ClassXLMRobertaTokenizer
Padding Token<pad>
Vocabulary Size50257
Torch Data Typefloat32
Activation Functiongelu_new

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Note: green Score (e.g. "73.2") means that the model is better than Sakonii/distilgpt2-nepali.

Rank the Distilgpt2 Nepali Capabilities

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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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