What are the hardware requirements for Qwen2 0.5B Fncl GGUF?

Qwen2 0.5B Fncl GGUF requires approximately 1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2 0.5B Fncl GGUF and how large is it?

Qwen2 0.5B Fncl GGUF is developed by haripritam, a model with 0.5b 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 0.5B Fncl GGUF?

Qwen2 0.5B Fncl GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2 0.5B Fncl GGUF by haripritam — VRAM 1GB

Name: Qwen2 0.5B Fncl GGUF
Author: haripritam

Qwen2 0.5B Fncl GGUF is an open-source language model by haripritam. Features: 0.5b LLM, VRAM: 1GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.13.

Conversational Endpoints compatible Gguf Quantized Qwen2 Region:us Unsloth

Model Card on HF 🤗: https://huggingface.co/haripritam/Qwen2-0.5B-fncl-GGUF

Qwen2 0.5B Fncl GGUF Parameters and Internals

LLM Name	Qwen2 0.5B Fncl GGUF
Repository 🤗	https://huggingface.co/haripritam/Qwen2-0.5B-fncl-GGUF
Base Model(s)	unsloth/qwen2-0.5b-bnb-4bit unsloth/qwen2-0.5b-bnb-4bit
Model Size	0.5b
Required VRAM	1 GB
Updated	2024-10-16
Maintainer	haripritam
Model Type	qwen2
Model Files	1.0 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	AutoModel
License	apache-2.0

Rank the Qwen2 0.5B Fncl GGUF 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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