What are the hardware requirements for Qwen2.5 7B Indonesian Alpaca Sft?

Qwen2.5 7B Indonesian Alpaca Sft requires approximately 15.2 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 Qwen2.5 7B Indonesian Alpaca Sft and how large is it?

Qwen2.5 7B Indonesian Alpaca Sft is developed by jikatakiri45, 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 Qwen2.5 7B Indonesian Alpaca Sft?

Qwen2.5 7B Indonesian Alpaca Sft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 7B Indonesian Alpaca Sft by jikatakiri45 — VRAM 15.2GB, 32K context

Name: Qwen2.5 7B Indonesian Alpaca Sft
Author: jikatakiri45

Qwen2.5 7B Indonesian Alpaca Sft is an open-source language model by jikatakiri45. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.32.

4bit Conversational En Endpoints compatible Instruct Quantized Qwen2 Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/jikatakiri45/qwen2.5-7b-indonesian-alpaca-sft

Qwen2.5 7B Indonesian Alpaca Sft Parameters and Internals

LLM Name	Qwen2.5 7B Indonesian Alpaca Sft
Repository 🤗	https://huggingface.co/jikatakiri45/qwen2.5-7b-indonesian-alpaca-sft
Base Model(s)	unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
Model Size	7b
Required VRAM	15.2 GB
Updated	2026-09-05
Maintainer	jikatakiri45
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-4 4.9 GB: 2-of-4 4.3 GB: 3-of-4 1.1 GB: 4-of-4
Supported Languages	en
Quantization Type	4bit
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	152064
Torch Data Type	bfloat16

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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
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Multi-Language Support and Translation

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