What are the hardware requirements for Gemma 3 1B Peft Codealpaca?

Gemma 3 1B Peft Codealpaca requires approximately 4 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 3 1B Peft Codealpaca and how large is it?

Gemma 3 1B Peft Codealpaca is developed by devichand, a model with 1b 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 3 1B Peft Codealpaca?

Gemma 3 1B Peft Codealpaca is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 3 1B Peft Codealpaca by devichand — VRAM 4GB, 32K context

Name: Gemma 3 1B Peft Codealpaca
Author: devichand

Gemma 3 1B Peft Codealpaca is an open-source language model by devichand. Features: 1b LLM, VRAM: 4GB, Context: 32K, LLM Explorer Score: 0.17.

Arxiv:1910.09700 Autotrain compatible Endpoints compatible Gemma3 text Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/devichand/gemma-3-1b-peft-Codealpaca

Gemma 3 1B Peft Codealpaca Parameters and Internals

LLM Name	Gemma 3 1B Peft Codealpaca
Repository 🤗	https://huggingface.co/devichand/gemma-3-1b-peft-Codealpaca
Model Size	1b
Required VRAM	4 GB
Updated	2025-08-01
Maintainer	devichand
Model Type	gemma3_text
Model Files	4.0 GB
Model Architecture	Gemma3ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.50.3
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	262144
Torch Data Type	float32

Rank the Gemma 3 1B Peft Codealpaca 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 devichand/gemma-3-1b-peft-Codealpaca.