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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

Vi Gemma 2 9B Function Calling Benchmarks

LLME Score: 0.23358
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 Parameters and Internals

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
Updated2024-10-18
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
Vi Gemma 2 9B Function Calling (ricepaper/vi-gemma-2-9b-function-calling)

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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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Release v2024072803
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