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Vi Gemma 2 9B Function Calling by ricepaper

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  4bit   Autotrain compatible Base model:finetune:unsloth/ge... Base model:unsloth/gemma-2-9b-...   Conversational   En   Endpoints compatible   Gemma2   Quantized   Region:us   Safetensors   Sharded   Tensorflow   Trl   Unsloth   Vn
Model Card on HF 🤗: https://huggingface.co/ricepaper/vi-gemma-2-9b-function-calling 

Vi Gemma 2 9B Function Calling Benchmarks

LLME Score: 0.20561
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Vi Gemma 2 9B Function Calling (ricepaper/vi-gemma-2-9b-function-calling)

Vi Gemma 2 9B Function Calling Parameters and Internals

Model Type 
text-generation-inference, transformers
Use Cases 
Applications:
Building chatbots, Creating automated question answering systems, Developing natural language processing applications, Building intelligent agents, Multi-agent systems
Considerations:
Model may require scaling in quality and hardware configuration to operate effectively.
Additional Notes 
Model fine-tuned using the TRL library and trained 2x faster using Unsloth.
Supported Languages 
Vietnamese (optimized), English (optimized)
Training Details 
Data Sources:
conversations containing function calls in ChatML format, multilingual data translated into Vietnamese
Input Output 
Input Format:
ChatML format
Accepted Modalities:
text
Performance Tips:
Results of function calls depend on the quality of the provided helper functions.
LLM NameVi Gemma 2 9B Function Calling
Repository 🤗https://huggingface.co/ricepaper/vi-gemma-2-9b-function-calling 
Base Model(s)  Gemma 2 9B It Bnb 4bit   unsloth/gemma-2-9b-it-bnb-4bit
Model Size9b
Required VRAM18.6 GB
Updated2025-02-22
Maintainerricepaper
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
Supported Languagesvn en
Quantization Type4bit
Model ArchitectureGemma2ForCausalLM
Licenseapache-2.0
Context Length8192
Model Max Length8192
Transformers Version4.43.3
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 ricepaper/vi-gemma-2-9b-function-calling.

Rank the Vi Gemma 2 9B Function Calling 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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