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Internlm2 Base 20B Llama by intervitens

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Internlm2 Base 20B Llama is an open-source language model by intervitens. Features: 20b LLM, VRAM: 39.6GB, Context: 32K, License: other, LLM Explorer Score: 0.13, Arc: 63, HellaSwag: 82.2, MMLU: 63.8, GSM8K: 44.9.

Base model:finetune:internlm/i... Base model:internlm/internlm2-...   En   Endpoints compatible   Llama   Region:us   Safetensors   Sharded   Tensorflow   Zh
Model Card on HF 🤗: https://huggingface.co/intervitens/internlm2-base-20b-llama 

Internlm2 Base 20B Llama Benchmarks

ARC: 62.97 vs 96.7 (so35)-34.9%
HellaSwag: 82.15 vs 95.3 (gpt4)-13.8%
MMLU: 63.78 vs 88.3 (so35)-27.8%
GSM8K: 44.88 vs 96.4 (so35)-53.4%
LLME Score: 0.12861
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Internlm2 Base 20B Llama (intervitens/internlm2-base-20b-llama)
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Internlm2 Base 20B Llama Parameters and Internals

Additional Notes 
Special thanks to Charles Goddard for the conversion script to create llama models from internlm.
Supported Languages 
en (Supported), zh (Supported)
LLM NameInternlm2 Base 20B Llama
Repository 🤗https://huggingface.co/intervitens/internlm2-base-20b-llama 
Base Model(s)  Internlm2 Base 20B   internlm/internlm2-base-20b
Model Size20b
Required VRAM39.6 GB
Updated2026-03-30
Maintainerintervitens
Model Typellama
Model Files  9.9 GB: 1-of-4   9.9 GB: 2-of-4   9.9 GB: 3-of-4   9.9 GB: 4-of-4
Supported Languagesen zh
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.36.2
Is Biased0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size92544
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than intervitens/internlm2-base-20b-llama.

Rank the Internlm2 Base 20B Llama 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  
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Multi-Language Support and Translation  

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