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Gemma 2 9B It by google

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  326539   
  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   Autotrain compatible Base model:finetune:google/gem...   Base model:google/gemma-2-9b   Conversational   Endpoints compatible   Gemma2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/google/gemma-2-9b-it 

Gemma 2 9B It Benchmarks

MMLU Pro: 31.95
GPQA: 14.77
MUSR: 9.74
BBH: 42.14
IFEval: 74.36 vs 88 (so35)-15.5%
MATH Lvl 5: 0.23
LLME Score: 0.48932
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 (google/gemma-2-9b-it)

Gemma 2 9B It Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Research, Commercial applications
Applications:
Content Creation, Communication, Chatbots, Conversational AI, Text Summarization, NLP Research, Language Learning Tools, Knowledge Exploration
Primary Use Cases:
Text Generation
Limitations:
Bias in training data, Context and task complexity, Language ambiguity and nuance, Factual inaccuracy, Lack of common sense
Considerations:
Aware of potential biases and misuse.
Supported Languages 
English (high proficiency)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
8 trillion tokens for the 9B model
Hardware Used:
Tensor Processing Unit (TPU)
Model Architecture:
text-to-text, decoder-only large language model
Safety Evaluation 
Methodologies:
structured evaluations, internal red-teaming testing
Risk Categories:
Text-to-Text Content Safety, Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Met acceptable thresholds for safety.
Responsible Ai Considerations 
Fairness:
Efforts to address biases through curriculum and evaluation.
Transparency:
Model card provides details on architecture, capabilities, limitations, and evaluation processes.
Accountability:
Google
Mitigation Strategies:
Continuous monitoring, de-biasing techniques, guidelines for responsible use.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated English-language text
Performance Tips:
Use appropriate prompts for improved context.
LLM NameGemma 2 9B It
Repository 🤗https://huggingface.co/google/gemma-2-9b-it 
Base Model(s)  Gemma 2 9B   google/gemma-2-9b
Model Size9b
Required VRAM18.6 GB
Updated2024-12-26
Maintainergoogle
Model Typegemma2
Model Files  4.9 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   3.7 GB: 4-of-4
Model ArchitectureGemma2ForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.42.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

Quantized Models of the Gemma 2 9B It

Model
Likes
Downloads
VRAM
Gemma 2 9B It Bnb 4bit20404556 GB
Gemma 2 9B It AWQ INT4625476 GB
... Russian Function Calling GGUF16166118 GB
Gemma 2 9B It GGUF42683 GB
Gemma 2 9B Instruct 4Bit GPTQ32226 GB
Gemma 2 9B It GGUF0313 GB
Gemma 2 9B It Bnb 4bit0276 GB

Best Alternatives to Gemma 2 9B It

Best Alternatives
Context / RAM
Downloads
Likes
Gemma 2 9B It SimPO8K / 18.6 GB16322137
Gemma 2 9B8K / 37.1 GB138252616
...2 9B Cpt Sahabatai V1 Instruct8K / 18.6 GB327627
MT Merge4 Gemma 2 9B8K / 20.4 GB321
SILMA 9B Instruct V1.08K / 18.6 GB1098255
MT3 Gen4 Gemma 2 9B8K / 20.4 GB391
Darkest Muse V18K / 20.4 GB26828
MT4 Gen2 Gemma 2 9B8K / 20.4 GB1763
Casuar 9B Model Stock8K / 18.6 GB182
Gemma Evo 10B8K / 20.4 GB282
Note: green Score (e.g. "73.2") means that the model is better than google/gemma-2-9b-it.

Rank the Gemma 2 9B It 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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