What are the hardware requirements for Deita 500M?

Deita 500M requires approximately 1.9 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 Deita 500M and how large is it?

Deita 500M is developed by KnutJaegersberg, a model with 500m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Deita 500M perform on standard benchmarks?

Deita 500M has the following published scores: MMLU 39.41. Compare against reference models on this page or on the LLM Explorer leaderboards.

Deita 500M by KnutJaegersberg — VRAM 1.9GB, 32K context

Name: Deita 500M
Author: KnutJaegersberg

Deita 500M is an open-source language model by KnutJaegersberg. Features: 500m LLM, VRAM: 1.9GB, Context: 32K, License: other, LLM Explorer Score: 0.16, Arc: 29.3, HellaSwag: 50, MMLU: 39.4, GSM8K: 9.

Autotrain Conversational Endpoints compatible Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/KnutJaegersberg/Deita-500m

Deita 500M Parameters and Internals

Additional Notes

The prompt provides an instructional example for AI model usage.

Training Details

Methodology:

The prompt covers the general process of fine-tuning.

Input Output

Input Format:

Structured prompt format provided in the example.

Accepted Modalities:

text

Output Format:

Model's response as text.

LLM Name	Deita 500M
Repository 🤗	https://huggingface.co/KnutJaegersberg/Deita-500m
Model Size	500m
Required VRAM	1.9 GB
Updated	2026-08-03
Maintainer	KnutJaegersberg
Model Type	qwen2
Model Files	1.9 GB
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.38.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151646
Torch Data Type	float32
Errors	replace

Rank the Deita 500M 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 Deita 500M

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2 Deita 500M	128K / 2.5 GB	10	4
Sozkz Fix Qwen 500M Kk Gec V3	32K / 0.9 GB	28	0
QwQ Math IO 500M	32K / 1 GB	30	2

Note: green Score (e.g. "73.2") means that the model is better than KnutJaegersberg/Deita-500m.