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VicUnlocked Alpaca 65B 4bit by Aeala

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  4bit   Autotrain compatible   Endpoints compatible   Llama   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/Aeala/VicUnlocked-alpaca-65b-4bit 

VicUnlocked Alpaca 65B 4bit Benchmarks

LLME Score: 0.0881
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
VicUnlocked Alpaca 65B 4bit (Aeala/VicUnlocked-alpaca-65b-4bit)
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VicUnlocked Alpaca 65B 4bit Parameters and Internals

Model Type 
text generation
Additional Notes 
It is important to note that this model was trained similar to Vicuna but utilizes the Alpaca format for prompting instructions.
Training Details 
Data Sources:
cleaned ShareGPT dataset
Data Volume:
Not specified
Methodology:
Trained in the Alpaca format with 4-bit GPTQ quantization
Training Time:
Not specified
Model Architecture:
Alpaca
Input Output 
Input Format:
### Instruction: (without the <>
Accepted Modalities:
text
Output Format:
### Response:
LLM NameVicUnlocked Alpaca 65B 4bit
Repository 🤗https://huggingface.co/Aeala/VicUnlocked-alpaca-65b-4bit 
Model Size65b
Required VRAM35.7 GB
Updated2025-09-13
MaintainerAeala
Model Typellama
Model Files  35.7 GB
Quantization Type4bit
Model ArchitectureLlamaForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.30.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than Aeala/VicUnlocked-alpaca-65b-4bit.

Rank the VicUnlocked Alpaca 65B 4bit 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 v20241124
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