What are the hardware requirements for Gemma9 On Korean Summary Events?

Gemma9 On Korean Summary Events requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma9 On Korean Summary Events and how large is it?

Gemma9 On Korean Summary Events is developed by ghost613, a model with 9b 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 Gemma9 On Korean Summary Events?

Gemma9 On Korean Summary Events is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma9 On Korean Summary Events by ghost613 — VRAM 0.2GB

Name: Gemma9 On Korean Summary Events
Author: ghost613

Gemma9 On Korean Summary Events is an open-source language model by ghost613. Features: 9b LLM, VRAM: 0.2GB, License: gemma, LLM Explorer Score: 0.14.

Adapter Base model:adapter:rtzr/ko-gem... Base model:rtzr/ko-gemma-2-9b-... Finetuned Generated from trainer Lora Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/ghost613/gemma9_on_korean_summary_events

Gemma9 On Korean Summary Events Parameters and Internals

Model Type

fine-tuned

Use Cases

Considerations:

More information needed

Training Details

Model Architecture:

More information needed

LLM Name	Gemma9 On Korean Summary Events
Repository 🤗	https://huggingface.co/ghost613/gemma9_on_korean_summary_events
Base Model(s)	Ko Gemma 2 9B It rtzr/ko-gemma-2-9b-it
Model Size	9b
Required VRAM	0.2 GB
Updated	2025-04-05
Maintainer	ghost613
Model Files	0.2 GB 0.0 GB
Model Architecture	Adapter
License	gemma
Is Biased	none
Tokenizer Class	GemmaTokenizer
Padding Token	<eos>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	k_proj\|down_proj\|gate_proj\|q_proj\|v_proj\|up_proj\|o_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16

Rank the Gemma9 On Korean Summary Events capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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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Note: green Score (e.g. "73.2") means that the model is better than ghost613/gemma9_on_korean_summary_events.