What are the hardware requirements for Qwen 2.5 7B 1M RRP V1 Lora?

Qwen 2.5 7B 1M RRP V1 Lora requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen 2.5 7B 1M RRP V1 Lora and how large is it?

Qwen 2.5 7B 1M RRP V1 Lora is developed by bunnycore, 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 Qwen 2.5 7B 1M RRP V1 Lora?

Qwen 2.5 7B 1M RRP V1 Lora is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen 2.5 7B 1M RRP V1 Lora by bunnycore — VRAM 0.2GB

Name: Qwen 2.5 7B 1M RRP V1 Lora
Author: bunnycore

Qwen 2.5 7B 1M RRP V1 Lora is an open-source language model by bunnycore. Features: 7b LLM, VRAM: 0.2GB, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.16.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-7b-ins... Dataset:amead10/sky-t1 data 17... Dataset:chaser-cz/sonnet35-cha... En Endpoints compatible Instruct Lora Qwen2 Region:us Safetensors Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/bunnycore/Qwen-2.5-7B-1M-RRP-v1-lora

Qwen 2.5 7B 1M RRP V1 Lora Parameters and Internals

LLM Name	Qwen 2.5 7B 1M RRP V1 Lora
Repository 🤗	https://huggingface.co/bunnycore/Qwen-2.5-7B-1M-RRP-v1-lora
Base Model(s)	Qwen/Qwen2.5-7B-Instruct-1M Qwen/Qwen2.5-7B-Instruct-1M
Model Size	7b
Required VRAM	0.2 GB
Updated	2026-07-26
Maintainer	bunnycore
Instruction-Based	Yes
Model Files	0.2 GB
Supported Languages	en
Model Architecture	AutoModel
License	apache-2.0
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	down_proj\|v_proj\|up_proj\|q_proj\|o_proj\|gate_proj\|k_proj
LoRA Alpha	16
LoRA Dropout	0
R Param	16

Rank the Qwen 2.5 7B 1M RRP V1 Lora 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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...03T0110 Xfam Repl Poison Seed5	0K / 0.1 GB	15	1
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... Olmo Consciousness Control S3	0K / 0.1 GB	13	1
...03T0110 Xfam Repl Poison Seed4	0K / 0.1 GB	12	1
...03T0110 Xfam Repl Poison Seed3	0K / 0.1 GB	12	1
Personalcoder Dsa V0.1	0K / 0.2 GB	19	1

Note: green Score (e.g. "73.2") means that the model is better than bunnycore/Qwen-2.5-7B-1M-RRP-v1-lora.