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Vicuna 13B V1.5 AWQ by TheBloke

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Vicuna 13B V1.5 AWQ is an open-source language model by TheBloke. Features: 13b LLM, VRAM: 7.2GB, Context: 4K, License: llama2, Quantized, LLM Explorer Score: 0.1.

  Arxiv:2306.05685   Arxiv:2307.09288   4-bit   Awq Base model:lmsys/vicuna-13b-v1... Base model:quantized:lmsys/vic...   Llama   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/vicuna-13B-v1.5-AWQ 

Vicuna 13B V1.5 AWQ Benchmarks

LLME Score: 0.09521
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Vicuna 13B V1.5 AWQ (TheBloke/vicuna-13B-v1.5-AWQ)
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Vicuna 13B V1.5 AWQ Parameters and Internals

Model Type 
llama
Additional Notes 
The model uses AWQ, an efficient, accurate, and fast low-bit weight quantization, supporting 4-bit quantization. This results in faster inference and the ability to use smaller GPUs for deployment.
Training Details 
Data Sources:
ShareGPT
Methodology:
Finetuned with supervised instruction on user-shared conversations.
Context Length:
4096
Model Architecture:
Transformer
Input Output 
Input Format:
The assistant gives helpful, detailed, and polite answers to the user''s questions. USER: {prompt} ASSISTANT:
LLM NameVicuna 13B V1.5 AWQ
Repository 🤗https://huggingface.co/TheBloke/vicuna-13B-v1.5-AWQ 
Model NameVicuna 13B v1.5
Model Creatorlmsys
Base Model(s)  lmsys/vicuna-13b-v1.5   lmsys/vicuna-13b-v1.5
Model Size13b
Required VRAM7.2 GB
Updated2026-03-29
MaintainerTheBloke
Model Typellama
Model Files  7.2 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.31.0
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 TheBloke/vicuna-13B-v1.5-AWQ.

Rank the Vicuna 13B V1.5 AWQ Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
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