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Google Codegemma 2B AWQ 4bit Smashed by PrunaAI

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Google Codegemma 2B AWQ 4bit Smashed is an open-source language model by PrunaAI. Features: 2b LLM, VRAM: 3.1GB, Context: 8K, Quantized, LLM Explorer Score: 0.14.

  4-bit   4bit   Awq   Base model:google/codegemma-2b Base model:quantized:google/co...   Gemma   Pruna-ai   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/PrunaAI/google-codegemma-2b-AWQ-4bit-smashed 

Google Codegemma 2B AWQ 4bit Smashed Benchmarks

LLME Score: 0.13583
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Google Codegemma 2B AWQ 4bit Smashed (PrunaAI/google-codegemma-2b-AWQ-4bit-smashed)
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Google Codegemma 2B AWQ 4bit Smashed Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research, commercial applications
Considerations:
Efficiency results may vary depending on settings, recommend testing in use-case conditions.
Additional Notes 
PrunaAI offers to compress models to be cheaper, smaller, faster, and greener.
Input Output 
Input Format:
Text prompt.
Accepted Modalities:
text
Output Format:
Text output.
Performance Tips:
Metrics like memory and speed may vary by settings like hardware and batch size. Recommend testing in specific use-case conditions.
LLM NameGoogle Codegemma 2B AWQ 4bit Smashed
Repository 🤗https://huggingface.co/PrunaAI/google-codegemma-2b-AWQ-4bit-smashed 
Base Model(s)  Codegemma 2B   google/codegemma-2b
Model Size2b
Required VRAM3.1 GB
Updated2025-09-23
MaintainerPrunaAI
Model Typegemma
Model Files  3.1 GB
AWQ QuantizationYes
Quantization Typeawq|4bit
Model ArchitectureGemmaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.42.4
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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Vi Gemma 2B RAG8K / 5.1 GB1513
Gemma 2B Bnb 4bit8K / 2.1 GB247016
Note: green Score (e.g. "73.2") means that the model is better than PrunaAI/google-codegemma-2b-AWQ-4bit-smashed.

Rank the Google Codegemma 2B AWQ 4bit Smashed 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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