What are the hardware requirements for Qwen2.5 Prompt Varient?

Qwen2.5 Prompt Varient requires approximately 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 Prompt Varient and how large is it?

Qwen2.5 Prompt Varient is developed by andro-flock, a model with 493.8m 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 Prompt Varient?

Qwen2.5 Prompt Varient is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 Prompt Varient by andro-flock — VRAM 2GB, 32K context

Name: Qwen2.5 Prompt Varient
Author: andro-flock

Qwen2.5 Prompt Varient is an open-source language model by andro-flock. Features: 493.8m LLM, VRAM: 2GB, Context: 32K, LLM Explorer Score: 0.17.

Autotrain compatible Base model:andro-flock/qwen2.5... Base model:finetune:andro-floc... Conversational Endpoints compatible Generated from trainer Qwen2 Region:us Safetensors Sft Tensorboard Trl

Model Card on HF 🤗: https://huggingface.co/andro-flock/Qwen2.5-prompt-varient

Qwen2.5 Prompt Varient Parameters and Internals

LLM Name	Qwen2.5 Prompt Varient
Repository 🤗	https://huggingface.co/andro-flock/Qwen2.5-prompt-varient
Model Name	prompt-varient
Base Model(s)	Prompt Varient andro-flock/prompt-varient
Model Size	493.8m
Required VRAM	2 GB
Updated	2025-03-15
Maintainer	andro-flock
Model Type	qwen2
Model Files	2.0 GB 0.0 GB
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.48.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151669
Torch Data Type	float32
Errors	replace

Rank the Qwen2.5 Prompt Varient 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

Best Alternatives to Qwen2.5 Prompt Varient

Best Alternatives	Context / RAM	Downloads
SoRu 0009	32K / 1 GB	8
Prompt Varient	32K / 2 GB	20
SoRu 0005	32K / 1 GB	10
SoRu 0003	32K / 1 GB	9
SoRu 0006	32K / 1 GB	8
SoRu 0004	32K / 1 GB	7
SoRu 0007	32K / 1 GB	5

Note: green Score (e.g. "73.2") means that the model is better than andro-flock/Qwen2.5-prompt-varient.