What are the hardware requirements for Gemma Code Instruct Finetune Test?

Gemma Code Instruct Finetune Test requires approximately 5.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma Code Instruct Finetune Test and how large is it?

Gemma Code Instruct Finetune Test is developed by Phanindra-max, a model with 2b 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 Code Instruct Finetune Test?

Gemma Code Instruct Finetune Test is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma Code Instruct Finetune Test by Phanindra-max — VRAM 5.1GB, 8K context

Name: Gemma Code Instruct Finetune Test
Author: Phanindra-max

Gemma Code Instruct Finetune Test is an open-source language model by Phanindra-max. Features: 2b LLM, VRAM: 5.1GB, Context: 8K, Fine-Tuned, Instruction-Based, LLM Explorer Score: 0.13.

Arxiv:1910.09700 Conversational Endpoints compatible Finetuned Gemma Instruct Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Phanindra-max/gemma-Code-Instruct-Finetune-test

Gemma Code Instruct Finetune Test Parameters and Internals

LLM Name	Gemma Code Instruct Finetune Test
Repository 🤗	https://huggingface.co/Phanindra-max/gemma-Code-Instruct-Finetune-test
Model Size	2b
Required VRAM	5.1 GB
Updated	2025-12-26
Maintainer	Phanindra-max
Model Type	gemma
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 0.1 GB: 2-of-2
Model Architecture	GemmaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.38.0
Tokenizer Class	GemmaTokenizer
Padding Token	<eos>
Vocabulary Size	256000
Torch Data Type	float16

Rank the Gemma Code Instruct Finetune Test 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

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Note: green Score (e.g. "73.2") means that the model is better than Phanindra-max/gemma-Code-Instruct-Finetune-test.