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ActionGemma 9B by KishoreK

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  Arxiv:1910.09700   Autotrain compatible   Conversational Dataset:salesforce/xlam-functi...   Endpoints compatible   Function calling   Gemma2   Lam   Region:us   Safetensors   Sharded   Tensorflow   Tooluse
Model Card on HF 🤗: https://huggingface.co/KishoreK/ActionGemma-9B 

ActionGemma 9B Benchmarks

LLME Score: 0.20716
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
ActionGemma 9B (KishoreK/ActionGemma-9B)

ActionGemma 9B Parameters and Internals

Model Type 
Function calling
Use Cases 
Areas:
Action/function calling
Applications:
Multi-lingual messaging
Limitations:
Uncertain use cases without proper parameters
Additional Notes 
ActionGemma is inspired by Salesforce/xLAM, combining multi-lingual capabilities with function calling.
Training Details 
Data Sources:
xLAM dataset
Methodology:
Fine-tuning
LLM NameActionGemma 9B
Repository 🤗https://huggingface.co/KishoreK/ActionGemma-9B 
Model Size9b
Required VRAM37.1 GB
Updated2025-02-22
MaintainerKishoreK
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
Licensemit
Context Length8192
Model Max Length8192
Transformers Version4.43.3
Tokenizer ClassGemmaTokenizer
Padding Token<eos>
Vocabulary Size256000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than KishoreK/ActionGemma-9B.

Rank the ActionGemma 9B 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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Release v20241227
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