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Gemma 2B GPTQ by TechxGenus

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

  1389   
  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   Endpoints compatible   Gemma   Gptq   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TechxGenus/gemma-2b-GPTQ 

Gemma 2B GPTQ Benchmarks

LLME Score: 0.20559
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2B GPTQ (TechxGenus/gemma-2b-GPTQ)

Gemma 2B GPTQ Parameters and Internals

Model Type 
text-to-text, large language model, decoder-only
Use Cases 
Areas:
Research, Commercial applications
Applications:
Text generation, Chatbots, Conversational AI, Text summarization, NLP research, Language learning tools, Knowledge exploration
Primary Use Cases:
Question answering, Summarization, Reasoning
Limitations:
Biases or gaps in training data, Context and task complexity, Language ambiguity, Factual accuracy, Common sense limitations
Considerations:
Guidelines for responsible use and exploration of de-biasing techniques.
Additional Notes 
The document also covers the ethical considerations and specific risks in developing open LLMs.
Supported Languages 
English (High proficiency)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
JAX and ML Pathways
Hardware Used:
TPUv5e
Model Architecture:
State-of-the-art open models from Google.
Safety Evaluation 
Methodologies:
Red-teaming, Human evaluation
Findings:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Risk Categories:
Harassment, Violence, Gore, Hate speech
Ethical Considerations:
Ensuring exclusion of harmful and illegal content.
Responsible Ai Considerations 
Fairness:
Careful scrutiny of input data pre-processing and posterior evaluations.
Transparency:
Model card provides architecture, capabilities, limitations, and evaluation processes.
Accountability:
Google is accountable for ensuring models are responsibly developed and maintained.
Mitigation Strategies:
Continuous monitoring and exploration of de-biasing techniques and content safeguards.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated English-language text
Performance Tips:
Ensure pre-installed libraries like transformers for optimal model operation.
LLM NameGemma 2B GPTQ
Repository 🤗https://huggingface.co/TechxGenus/gemma-2b-GPTQ 
Model Size2b
Required VRAM2.1 GB
Updated2024-12-22
MaintainerTechxGenus
Model Typegemma
Model Files  2.1 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-2b-GPTQ.

Rank the Gemma 2B 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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Release v20241217
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