What are the hardware requirements for Data Engineering Mentor V2?

Data Engineering Mentor V2 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 Data Engineering Mentor V2 and how large is it?

Data Engineering Mentor V2 is developed by LoNebula, 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 Data Engineering Mentor V2?

Data Engineering Mentor V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Data Engineering Mentor V2 by LoNebula — VRAM 2GB, 32K context

Name: Data Engineering Mentor V2
Author: LoNebula

Data Engineering Mentor V2 is an open-source language model by LoNebula. Features: 0.5b LLM, VRAM: 2GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.29.

Base model:adapter:qwen/qwen2.... Base model:qwen/qwen2.5-0.5b-i... Conversational Data-engineering Dataset:lonebula/data-engineer... Education En Instruct Lora Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/LoNebula/data-engineering-mentor-v2

Data Engineering Mentor V2 Parameters and Internals

LLM Name	Data Engineering Mentor V2
Repository 🤗	https://huggingface.co/LoNebula/data-engineering-mentor-v2
Base Model(s)	Qwen/Qwen2.5-0.5B-Instruct Qwen/Qwen2.5-0.5B-Instruct
Model Size	0.5b
Required VRAM	2 GB
Updated	2026-10-15
Maintainer	LoNebula
Model Type	qwen2
Instruction-Based	Yes
Model Files	2.0 GB
Supported Languages	en
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.57.6
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
LoRA Model	Yes
Errors	replace

Rank the Data Engineering Mentor V2 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 Data Engineering Mentor V2

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2 0.5B Abyme Merge3	128K / 1.3 GB	34	1
Qwen2 0.5B Abyme Merge2	128K / 0.3 GB	26	0
Qwen2.5 0.5B Instruct	64K / 1 GB	16	0
Switchboard Router 0.5B	32K / 1 GB	510	2
Qwen2.5 0.5B Instruct	32K / 1 GB	6324790	572
Qwen2.5 0.5B Abliterated	32K / 1.3 GB	1450	1
Seger Qwen2.5 0.5B	32K / 1 GB	599	0
Temper 1 0.5B	32K / 1 GB	241	2
Qwen2.5 0.5B Grounded Sft	32K / 1 GB	728	1
Qwen2.5 0.5B Grounded Rloo	32K / 1 GB	705	1

Note: green Score (e.g. "73.2") means that the model is better than LoNebula/data-engineering-mentor-v2.