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Gemma 7B It GPTQ by TechxGenus

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

  8   
  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   Conversational   Endpoints compatible   Gemma   Gptq   Quantized   Region:us   Safetensors

Gemma 7B It GPTQ Benchmarks

LLME Score: 0.18497
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 GPTQ Parameters and Internals

Model Type 
Text generation, Decoder-only language model
Use Cases 
Areas:
Content Creation and Communication, Research and Education
Applications:
Chatbots and Conversational AI, Text Summarization, Natural Language Processing (NLP) Research
Primary Use Cases:
Question answering, Summarization, Text generation
Limitations:
Bias in training data, Complex tasks, Language ambiguity
Considerations:
Ensure adequate context is provided to improve model responses.
Additional Notes 
Gemma models democratize AI access, making high-performance AI available for environments with limited resources.
Supported Languages 
English (Fluent)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Hardware Used:
TPUv5e
Model Architecture:
Transformer-based, decoder-only
Safety Evaluation 
Methodologies:
Red-teaming, Human evaluation, Automated evaluation
Risk Categories:
Child safety, Misinformation, Content safety, Bias
Ethical Considerations:
My ethical considerations include handling representational harms, memorization, and large-scale harm.
Responsible Ai Considerations 
Fairness:
Models undergo scrutiny for socio-cultural biases.
Transparency:
Model card provides architectural, capability, and evaluational details.
Mitigation Strategies:
Continuous monitoring, guidelines for responsible use.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated English-language text
LLM NameGemma 7B It GPTQ
Repository 🤗https://huggingface.co/TechxGenus/gemma-7b-it-GPTQ 
Model Size7b
Required VRAM7.2 GB
Updated2024-11-21
MaintainerTechxGenus
Model Typegemma
Model Files  7.2 GB
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureGemmaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.39.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16
Gemma 7B It GPTQ (TechxGenus/gemma-7b-it-GPTQ)

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Note: green Score (e.g. "73.2") means that the model is better than TechxGenus/gemma-7b-it-GPTQ.

Rank the Gemma 7B It GPTQ 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 v20241110
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