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Gemma 2 9B Chatml by IntervitensInc

 »  All LLMs  »  IntervitensInc  »  Gemma 2 9B Chatml   URL Share it on

  32   
  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   Endpoints compatible   Gemma2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/IntervitensInc/gemma-2-9b-chatml 

Gemma 2 9B Chatml Benchmarks

MMLU Pro: 36.02
GPQA: 12.75
MUSR: 13.24
BBH: 35.32
IFEval: 12.75 vs 88 (so35)-85.5%
MATH Lvl 5: 6.42
LLME Score: 0.27423
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 Chatml (IntervitensInc/gemma-2-9b-chatml)

Gemma 2 9B Chatml Parameters and Internals

Model Type 
text-to-text, decoder-only, large language models
Use Cases 
Areas:
Research, Commercial Applications
Applications:
Text Generation, Chatbots and Conversational AI, Text Summarization
Primary Use Cases:
NLP Research, Language Learning Tools, Knowledge Exploration
Limitations:
Training data quality, Context and task complexity, Language ambiguity and nuance, Factual accuracy, Common sense application
Considerations:
Training data quality, scope, and context length influence capabilities.
Additional Notes 
These models provide high-performance open large language model implementations designed for responsible AI development.
Supported Languages 
English (high)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
8 trillion tokens
Hardware Used:
TPUs (TPUv5p)
Safety Evaluation 
Methodologies:
structured evaluations, internal red-teaming testing
Findings:
acceptable thresholds met for categories such as child safety, content safety, representational harms, memorization, large-scale harms.
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Ethical evaluation methods include structured evaluations and red-teaming testing.
Responsible Ai Considerations 
Fairness:
Careful scrutiny, input data pre-processing and evaluations for bias and fairness.
Transparency:
Model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
Accountability:
Google
Mitigation Strategies:
Continuous monitoring and de-biasing techniques.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
English-language text
Performance Tips:
Longer context generally leads to better outputs.
Release Notes 
Version:
2.0
Notes:
Version with added chatml tokens for finetuning.
Version:
Gemma PT 9B
Notes:
Initial release of the Gemma PT 9B model.
LLM NameGemma 2 9B Chatml
Repository 🤗https://huggingface.co/IntervitensInc/gemma-2-9b-chatml 
Model Size9b
Required VRAM37.1 GB
Updated2024-12-21
MaintainerIntervitensInc
Model Typegemma2
Model Files  4.8 GB: 1-of-8   5.0 GB: 2-of-8   5.0 GB: 3-of-8   4.9 GB: 4-of-8   5.0 GB: 5-of-8   5.0 GB: 6-of-8   5.0 GB: 7-of-8   2.4 GB: 8-of-8
Model ArchitectureGemma2ForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.42.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat32

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

Rank the Gemma 2 9B Chatml 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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