Octopus V2 Gguf AWQ by NexaAIDev

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  4-bit   Android   Autotrain compatible   Awq   Base model:google/gemma-2b Base model:quantized:google/ge...   Conversational   En   Function calling   Gemma   Gguf   On-device language model   Q2   Quantized   Region:us   Safetensors

Octopus V2 Gguf AWQ Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Octopus V2 Gguf AWQ (NexaAIDev/Octopus-v2-gguf-awq)

Octopus V2 Gguf AWQ Parameters and Internals

Model Type 
function calling, on-device language model
Use Cases 
Areas:
Research, Commercial applications
Additional Notes 
The model is suitable for Android on-device use.
Supported Languages 
en (High proficiency)
Input Output 
Input Format:
Plain text queries
Accepted Modalities:
text
Output Format:
Generated function parameters as text
LLM NameOctopus V2 Gguf AWQ
Repository ๐Ÿค—https://huggingface.co/NexaAIDev/Octopus-v2-gguf-awq 
Base Model(s)  Gemma 2B   google/gemma-2b
Model Size2b
Required VRAM1.2 GB
Updated2024-12-22
MaintainerNexaAIDev
Model Typegemma
Model Files  3.1 GB   5.0 GB   1.2 GB   1.4 GB   1.5 GB   1.4 GB   1.3 GB   1.6 GB   1.7 GB   1.6 GB   1.6 GB   1.6 GB   1.8 GB   1.9 GB   1.8 GB   1.8 GB   1.8 GB   2.1 GB   2.7 GB   5.0 GB
Supported Languagesen
GGUF QuantizationYes
AWQ QuantizationYes
Quantization Typegguf|awq|q2|q4_k|q5_k
Model ArchitectureGemmaForCausalLM
Licensecc-by-nc-4.0
Context Length8192
Model Max Length8192
Transformers Version4.40.1
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256022
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than NexaAIDev/Octopus-v2-gguf-awq.

Rank the Octopus V2 Gguf AWQ 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  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217