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Gemma 2B AWQ by TechxGenus

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

  24   
  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   Endpoints compatible   Gemma   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TechxGenus/gemma-2b-AWQ 

Gemma 2B AWQ Benchmarks

LLME Score: 0.1817
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2B AWQ (TechxGenus/gemma-2b-AWQ)

Gemma 2B AWQ Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
Content Creation and Communication, Research and Education
Applications:
Text Generation, Chatbots and Conversational AI, Text Summarization, NLP Research, Language Learning Tools, Knowledge Exploration
Limitations:
Training Data, Context and Task Complexity, Language Ambiguity and Nuance, Factual Accuracy, Common Sense
Considerations:
LLMs might be misused to generate false, harmful, or misleading text.
Additional Notes 
These models were evaluated and showed superior performance compared to other open model alternatives.
Supported Languages 
English (High)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Hardware Used:
TPUv5e
Safety Evaluation 
Methodologies:
structured evaluations, internal red-teaming
Findings:
acceptable thresholds for internal policies
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Responsible Ai Considerations 
Fairness:
LLMs trained on large-scale text data can reflect socio-cultural biases.
Transparency:
This model card summarizes model details.
Accountability:
Google
Mitigation Strategies:
Security monitoring, de-biasing techniques, content safety guidelines.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated English-language text
LLM NameGemma 2B AWQ
Repository 🤗https://huggingface.co/TechxGenus/gemma-2b-AWQ 
Model Size2b
Required VRAM3.1 GB
Updated2024-12-22
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-AWQ.

Rank the Gemma 2B 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 v20241217
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