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Llama2 Xs 460M Experimental by ahxt

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  Autotrain compatible   Dataset:redpajama   En   Endpoints compatible   Llama   Llama2   Llama2 architecture   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/ahxt/llama2_xs_460M_experimental 

Llama2 Xs 460M Experimental Benchmarks

ARC: 24.91 vs 96.7 (so35)-74.2%
HellaSwag: 38.47 vs 95.3 (gpt4)-59.6%
MMLU: 26.17 vs 88.3 (so35)-70.4%
TruthfulQA: 41.59 vs 59 (gpt4)-29.5%
WinoGrande: 49.88 vs 87.5 (gpt4)-43%
LLME Score: 0.16337
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama2 Xs 460M Experimental (ahxt/llama2_xs_460M_experimental)
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Llama2 Xs 460M Experimental Parameters and Internals

Model Type 
large language model
Training Details 
Data Sources:
RedPajama
LLM NameLlama2 Xs 460M Experimental
Repository 🤗https://huggingface.co/ahxt/llama2_xs_460M_experimental 
Model Size460m
Required VRAM0.9 GB
Updated2025-09-10
Maintainerahxt
Model Typellama
Model Files  0.9 GB
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Context Length1024
Model Max Length1024
Transformers Version4.31.0
Tokenizer ClassGPT2Tokenizer
Beginning of Sentence Token<|endoftext|>
End of Sentence Token<|endoftext|>
Unk Token<|endoftext|>
Vocabulary Size50304
Torch Data Typebfloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than ahxt/llama2_xs_460M_experimental.

Rank the Llama2 Xs 460M Experimental 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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