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Alpaca 7B by vicgalle

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Alpaca 7B is an open-source language model by vicgalle. Features: 7b LLM, VRAM: 2.6GB, HF Score: 29.6, LLM Explorer Score: 0.12, Arc: 28.1, HellaSwag: 25.8, MMLU: 25.3, TruthfulQA: 48.5, WinoGrande: 49.7.

  Endpoints compatible   Llama   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/vicgalle/alpaca-7b 

Alpaca 7B Benchmarks

ARC: 28.07 vs 96.7 (so35)-71%
HellaSwag: 25.83 vs 95.3 (gpt4)-72.9%
MMLU: 25.31 vs 88.3 (so35)-71.3%
TruthfulQA: 48.49 vs 59 (gpt4)-17.8%
WinoGrande: 49.72 vs 87.5 (gpt4)-43.2%
LLME Score: 0.12088
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Alpaca 7B (vicgalle/alpaca-7b)
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Alpaca 7B Parameters and Internals

LLM NameAlpaca 7B
Repository 🤗https://huggingface.co/vicgalle/alpaca-7b 
Model Size7b
Required VRAM2.6 GB
Updated2026-03-30
Maintainervicgalle
Model Typellama
Model Files  0.4 GB: 1-of-39   0.4 GB: 2-of-39   0.4 GB: 3-of-39   0.4 GB: 4-of-39   0.4 GB: 5-of-39   0.3 GB: 6-of-39   0.3 GB: 7-of-39   0.3 GB: 8-of-39   0.4 GB: 9-of-39   0.4 GB: 10-of-39   0.4 GB: 11-of-39   0.4 GB: 12-of-39   0.3 GB: 13-of-39   0.3 GB: 14-of-39   0.3 GB: 15-of-39   0.4 GB: 16-of-39   0.4 GB: 17-of-39   0.4 GB: 18-of-39   0.4 GB: 19-of-39   0.3 GB: 20-of-39   0.3 GB: 21-of-39   0.3 GB: 22-of-39   0.4 GB: 23-of-39   0.4 GB: 24-of-39   0.4 GB: 25-of-39   0.4 GB: 26-of-39   0.3 GB: 27-of-39   0.3 GB: 28-of-39   0.3 GB: 29-of-39   0.4 GB: 30-of-39   0.4 GB: 31-of-39   0.4 GB: 32-of-39   0.4 GB: 33-of-39   0.3 GB: 34-of-39   0.3 GB: 35-of-39   0.3 GB: 36-of-39   0.4 GB: 37-of-39   0.3 GB: 38-of-39   0.3 GB: 39-of-39   0.4 GB: 1-of-39   0.4 GB: 2-of-39   0.4 GB: 3-of-39   0.4 GB: 4-of-39   0.4 GB: 5-of-39   0.3 GB: 6-of-39   0.3 GB: 7-of-39
Model ArchitectureLlamaForCausalLM
Transformers Version4.28.0.dev0
Vocabulary Size32000
Torch Data Typefloat16

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

Rank the Alpaca 7B Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
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Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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