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Gemma 2 9B It 4bit by rainjay

 »  All LLMs  »  rainjay  »  Gemma 2 9B It 4bit   URL Share it on

  80   
  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:2103.03874   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   4-bit   4bit   Autotrain compatible   Bitsandbytes   Conversational   Endpoints compatible   Gemma2   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/rainjay/gemma-2-9b-it-4bit 

Gemma 2 9B It 4bit Benchmarks

LLME Score: 0.19918
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2 9B It 4bit (rainjay/gemma-2-9b-it-4bit)

Gemma 2 9B It 4bit 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
Primary Use Cases:
Generate creative text formats, Power conversational interfaces, Generate concise summaries
Limitations:
Training Data Influences, Context and Task Complexity, Language Ambiguity and Nuance, Factual Accuracy Limitations, Common Sense Limitations
Considerations:
LLMs are better at tasks that can be framed with clear prompts and instructions. Factual accuracy should be verified as LLMs are not knowledge bases.
Additional Notes 
Gemma models are designed for responsible AI development. They include open weights for pre-trained and instruction-tuned variants, enabling wide accessibility and innovation.
Supported Languages 
English (high)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
8 trillion tokens for 9B model
Hardware Used:
TPUv5p
Model Architecture:
Gemma models are built from the same research and technology used to create the Gemini models.
Safety Evaluation 
Methodologies:
Red-teaming, Benchmark testing
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Models were evaluated for child safety, content safety, representational harms, memorization, large-scale harms.
Responsible Ai Considerations 
Fairness:
Models underwent careful scrutiny, input data pre-processing, and posterior evaluations to address socio-cultural biases.
Transparency:
Details on the models' architecture, capabilities, limitations, and evaluation processes are summarized in the model card.
Accountability:
Responsible use guidelines are provided, see the Responsible Generative AI Toolkit.
Mitigation Strategies:
Continuous monitoring and exploration of de-biasing techniques during model training, fine-tuning, and other use cases are encouraged for mitigating perpetuation of biases.
Input Output 
Input Format:
Text string, such as a question, a prompt, or a document to be summarized.
Accepted Modalities:
text
Output Format:
Generated English-language text in response to the input.
Performance Tips:
Models perform better with clear prompts and sufficient context.
LLM NameGemma 2 9B It 4bit
Repository 🤗https://huggingface.co/rainjay/gemma-2-9b-it-4bit 
Model Size9b
Required VRAM6.1 GB
Updated2025-02-22
Maintainerrainjay
Model Typegemma2
Model Files  4.0 GB: 1-of-2   2.1 GB: 2-of-2
Quantization Type4bit
Model ArchitectureGemma2ForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.42.3
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than rainjay/gemma-2-9b-it-4bit.

Rank the Gemma 2 9B It 4bit Capabilities

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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 v20241227
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