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Gemma Ei Oc Structured Train by Holmeister

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  Adapter Base model:adapter:google/gemm...   Base model:google/gemma-7b   Finetuned   Generated from trainer   Llama-factory   Lora   Peft   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/Holmeister/Gemma_ei_oc_structured_train 

Gemma Ei Oc Structured Train Benchmarks

LLME Score: 0.15683
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma Ei Oc Structured Train (Holmeister/Gemma_ei_oc_structured_train)
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Gemma Ei Oc Structured Train Parameters and Internals

Additional Notes 
Training used hyperparameters including a learning rate of 0.0003, train batch size of 32, and evaluation batch size of 8.
Training Details 
Data Sources:
emollms_ei_oc_structured
Methodology:
Fine-tuning with LoRA
LLM NameGemma Ei Oc Structured Train
Repository 🤗https://huggingface.co/Holmeister/Gemma_ei_oc_structured_train 
Base Model(s)  Gemma 7B   google/gemma-7b
Model Size7b
Required VRAM0.1 GB
Updated2024-08-15
MaintainerHolmeister
Model Files  0.1 GB   0.0 GB
Model ArchitectureAdapter
Is Biasednone
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
PEFT TypeLORA
LoRA ModelYes
PEFT Target Moduleso_proj|k_proj|down_proj|q_proj|up_proj|gate_proj|v_proj
LoRA Alpha16
LoRA Dropout0.1
R Param8

Quantized Models of the Gemma Ei Oc Structured Train

Model
Likes
Downloads
VRAM
Gemma 7B32282974017 GB
Gemma 7B01517 GB

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Note: green Score (e.g. "73.2") means that the model is better than Holmeister/Gemma_ei_oc_structured_train.

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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