SOLAR Platypus 10.7B V2 AWQ by TheBloke

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  4-bit   Autotrain compatible   Awq Base model:kyujinpy/solar-plat... Base model:quantized:kyujinpy/... Dataset:garage-baind/open-plat...   En   Llama   Quantized   Region:us   Safetensors

SOLAR Platypus 10.7B V2 AWQ Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
SOLAR Platypus 10.7B V2 AWQ (TheBloke/SOLAR-Platypus-10.7B-v2-AWQ)

SOLAR Platypus 10.7B V2 AWQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
Model quantized by TheBloke using AWQ method, supported on Linux and Windows with NVidia GPUs.
Supported Languages 
en (English)
Training Details 
Data Sources:
garage-bAInd/Open-Platypus
Methodology:
Q-LoRA with LoRA_r: 64, LoRA_alpha: 16, LoRA_dropout: 0.05
Model Architecture:
auto-regressive language model based on the Llama2 architecture
Input Output 
Input Format:
text
Accepted Modalities:
text
Output Format:
text
LLM NameSOLAR Platypus 10.7B V2 AWQ
Repository ๐Ÿค—https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-AWQ 
Model NameSolar Platypus 10.7B v2
Model CreatorKyujinHan
Base Model(s)  kyujinpy/SOLAR-Platypus-10.7B-v2   kyujinpy/SOLAR-Platypus-10.7B-v2
Model Size10.7b
Required VRAM6 GB
Updated2024-12-21
MaintainerTheBloke
Model Typesolar
Model Files  6.0 GB
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licensecc-by-nc-sa-4.0
Context Length4096
Model Max Length4096
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/SOLAR-Platypus-10.7B-v2-AWQ.

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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  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217