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Gemma 7B It AWQ by TechxGenus

 »  All LLMs  »  TechxGenus  »  Gemma 7B It AWQ   URL Share it on

  30   
  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   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TechxGenus/gemma-7b-it-AWQ 

Gemma 7B It AWQ Benchmarks

LLME Score: 0.13989
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 7B It AWQ (TechxGenus/gemma-7b-it-AWQ)
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Gemma 7B It AWQ Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
content creation, research, communication
Applications:
text generation, chatbots, conversational AI, text summarization
Primary Use Cases:
question answering, summarization, reasoning
Limitations:
bias, factual inaccuracy, common sense issues
Considerations:
Developers are encouraged to apply privacy-preserving techniques and adhere to the Responsible Generative AI Toolkit.
Additional Notes 
These models are optimized for performance and responsible AI use, providing accessibility to advanced AI models.
Supported Languages 
English (high)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
Training was done using JAX and ML Pathways
Hardware Used:
TPUv5e
Model Architecture:
not specified in the data
Safety Evaluation 
Methodologies:
structured evaluations, internal red-teaming testing
Findings:
Within acceptable thresholds for meeting internal policies
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Focused on safety, fairness, and privacy.
Responsible Ai Considerations 
Fairness:
Scrutiny and pre-processing of input data to handle biases.
Transparency:
Model card published.
Accountability:
Responsibility lies with the developers using the model.
Mitigation Strategies:
Continuous monitoring and the exploration of de-biasing techniques are encouraged.
Input Output 
Input Format:
text
Accepted Modalities:
text
Output Format:
Generated English-language text
LLM NameGemma 7B It AWQ
Repository 🤗https://huggingface.co/TechxGenus/gemma-7b-it-AWQ 
Model Size7b
Required VRAM7.2 GB
Updated2025-09-23
MaintainerTechxGenus
Model Typegemma
Model Files  6.6 GB: 1-of-2   0.6 GB: 2-of-2
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-7b-it-AWQ.

Rank the Gemma 7B 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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