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Gemma 2B It AWQ by TechxGenus

 »  All LLMs  »  TechxGenus  »  Gemma 2B It AWQ   URL Share it on

  69   
  Arxiv:1705.03551   Arxiv:1804.06876   Arxiv:1804.09301   Arxiv:1809.02789   Arxiv:1811.00937   Arxiv:1904.09728   Arxiv:1905.07830   Arxiv:1905.10044   Arxiv:1907.10641   Arxiv:1911.01547   Arxiv:1911.11641   Arxiv:2009.03300   Arxiv:2009.11462   Arxiv:2101.11718   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2312.11805   4-bit   Autotrain compatible   Awq   Conversational   Endpoints compatible   Gemma   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TechxGenus/gemma-2b-it-AWQ 

Gemma 2B It AWQ Benchmarks

LLME Score: 0.1407
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2B It AWQ (TechxGenus/gemma-2b-it-AWQ)
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Gemma 2B It AWQ Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
Research, Commercial applications
Applications:
Chatbots and Conversational AI, Text Generation, Text Summarization
Primary Use Cases:
Question answering, Summarization, Reasoning
Limitations:
Potential bias in responses, Might generate incorrect or outdated factual statements
Considerations:
Models are suitable for environments with limited resources.
Additional Notes 
Models help democratize access to state-of-the-art AI technology.
Supported Languages 
english (fluent)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
Instruction-tuning
Hardware Used:
TPUv5e
Model Architecture:
Large language model, text-to-text, decoder-only
Safety Evaluation 
Methodologies:
Red-teaming, Human evaluation
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Models were filtered for sensitive data and personal information.
Responsible Ai Considerations 
Fairness:
Models underwent input data pre-processing for bias control.
Transparency:
Model card provides architecture and evaluation details.
Accountability:
Google is responsible for model outputs.
Mitigation Strategies:
Data filtering and safety guidelines provided.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated English text
Performance Tips:
Use longer context for better outputs.
LLM NameGemma 2B It AWQ
Repository 🤗https://huggingface.co/TechxGenus/gemma-2b-it-AWQ 
Model Size2b
Required VRAM3.1 GB
Updated2025-09-23
MaintainerTechxGenus
Model Typegemma
Model Files  3.1 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureGemmaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.39.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TechxGenus/gemma-2b-it-AWQ.

Rank the Gemma 2B It AWQ Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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