What are the hardware requirements for Gemma Ei Oc Structured Train?

Gemma Ei Oc Structured Train requires approximately 0.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma Ei Oc Structured Train and how large is it?

Gemma Ei Oc Structured Train is developed by Holmeister, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Gemma Ei Oc Structured Train?

Gemma Ei Oc Structured Train is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma Ei Oc Structured Train by Holmeister — VRAM 0.1GB

Name: Gemma Ei Oc Structured Train
Author: Holmeister

Gemma Ei Oc Structured Train is an open-source language model by Holmeister. Features: 7b LLM, VRAM: 0.1GB, LLM Explorer Score: 0.12.

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 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 Name	Gemma Ei Oc Structured Train
Repository 🤗	https://huggingface.co/Holmeister/Gemma_ei_oc_structured_train
Base Model(s)	Gemma 7B google/gemma-7b
Model Size	7b
Required VRAM	0.1 GB
Updated	2024-08-15
Maintainer	Holmeister
Model Files	0.1 GB 0.0 GB
Model Architecture	Adapter
Is Biased	none
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	o_proj\|k_proj\|down_proj\|q_proj\|up_proj\|gate_proj\|v_proj
LoRA Alpha	16
LoRA Dropout	0.1
R Param	8

Rank the Gemma Ei Oc Structured Train 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

Quantized Models of the Gemma Ei Oc Structured Train

Model	Likes	Downloads	VRAM
Gemma 7B	3388	29051	17 GB
Gemma 7B	0	39	17 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.