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Gemma 7B GPTQ by TechxGenus

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

  316   
  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:2305.14314   Arxiv:2312.11805   4-bit   Autotrain compatible   Endpoints compatible   Gemma   Gptq   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TechxGenus/gemma-7b-GPTQ 

Gemma 7B GPTQ Benchmarks

LLME Score: 0.18834
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 7B GPTQ (TechxGenus/gemma-7b-GPTQ)

Gemma 7B GPTQ Parameters and Internals

Model Type 
text generation, decoder-only
Use Cases 
Areas:
Content Creation, Research, Education
Applications:
Text Generation, Chatbots, Text Summarization, NLP Research, Language Learning Tools, Knowledge Exploration
Primary Use Cases:
Generating creative text formats, Powering conversational interfaces, Text summarization
Limitations:
Potential biases in training data, Complexity in open-ended tasks, Challenges in grasping language nuances
Considerations:
Responsible use with reference to Google’s Toolkit for Responsible Generative AI.
Additional Notes 
The models allow deployment in varied environments, enhancing accessibility and innovation.
Supported Languages 
English (Native)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Hardware Used:
TPUv5e
Safety Evaluation 
Methodologies:
red-teaming, structured evaluations
Risk Categories:
child sexual abuse, harassment, violence, hate speech, representational harms, memorization, large-scale harm
Ethical Considerations:
Ethical issues were addressed in model development.
Responsible Ai Considerations 
Fairness:
Models were evaluated to reduce bias.
Transparency:
Details on models' architecture, capabilities, limitations, and evaluation processes are provided.
Accountability:
Developers are encouraged to maintain content safety.
Mitigation Strategies:
Technical limitations and education for developers and end-users are provided.
Input Output 
Input Format:
Text string (question, prompt, document).
Accepted Modalities:
text
Output Format:
Generated text (response, summary).
Performance Tips:
Enhancing context can improve output quality.
LLM NameGemma 7B GPTQ
Repository 🤗https://huggingface.co/TechxGenus/gemma-7b-GPTQ 
Model Size7b
Required VRAM7.2 GB
Updated2024-12-22
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

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

Rank the Gemma 7B 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 v20241217
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