Gemma2b Based Finetuned Using Ludwig With Tldrnews Summarization T4 4bit Notmerged by alexsherstinsky

Name: Gemma2b Based Finetuned Using Ludwig With Tldrnews Summarization T4 4bit Notmerged
Author: alexsherstinsky

Gemma2b Based Finetuned Using Ludwig With Tldrnews Summarization T4 4bit Notmerged is an open-source language model by alexsherstinsky. Features: 2b LLM, Quantized, Fine-Tuned, LLM Explorer Score: 0.11.

Arxiv:1910.09700 4bit Adapter Base model:adapter:google/gemm... Base model:google/gemma-2b-it Finetuned Lora Peft Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/alexsherstinsky/gemma2b-based-finetuned-using-ludwig-with-tldrnews-summarization-T4-4bit-notmerged

Gemma2b Based Finetuned Using Ludwig With Tldrnews Summarization T4 4bit Notmerged Parameters and Internals

LLM Name	Gemma2b Based Finetuned Using Ludwig With Tldrnews Summarization T4 4bit Notmerged
Repository 🤗	https://huggingface.co/alexsherstinsky/gemma2b-based-finetuned-using-ludwig-with-tldrnews-summarization-T4-4bit-notmerged
Base Model(s)	Gemma 2B It google/gemma-2b-it
Model Size	2b
Required VRAM	0 GB
Updated	2026-05-03
Maintainer	alexsherstinsky
Model Files	0.0 GB
Quantization Type	4bit
Model Architecture	Adapter
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|v_proj
LoRA Alpha	16
LoRA Dropout	0.05
R Param	8

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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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Note: green Score (e.g. "73.2") means that the model is better than alexsherstinsky/gemma2b-based-finetuned-using-ludwig-with-tldrnews-summarization-T4-4bit-notmerged.