What are the hardware requirements for LeoPARD Coder 0.5.9?

LeoPARD Coder 0.5.9 requires approximately 9.9 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed LeoPARD Coder 0.5.9 and how large is it?

LeoPARD Coder 0.5.9 is developed by VortexHunter23, a model with 15.2b 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 LeoPARD Coder 0.5.9?

LeoPARD Coder 0.5.9 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LeoPARD Coder 0.5.9 by VortexHunter23 — VRAM 9.9GB, 128K context

Name: LeoPARD Coder 0.5.9
Author: VortexHunter23

LeoPARD Coder 0.5.9 is an open-source language model by VortexHunter23. Features: 15.2b LLM, VRAM: 9.9GB, Context: 128K, License: apache-2.0, Code Generating, LLM Explorer Score: 0.18.

4-bit Base model:quantized:vortexhun... Base model:vortexhunter23/leop... Bitsandbytes Codegen Conversational En Endpoints compatible Qwen2 Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/VortexHunter23/LeoPARD-Coder-0.5.9

LeoPARD Coder 0.5.9 Parameters and Internals

LLM Name	LeoPARD Coder 0.5.9
Repository 🤗	https://huggingface.co/VortexHunter23/LeoPARD-Coder-0.5.9
Base Model(s)	VortexHunter23/LeoPARD-Coder-0.5.7 VortexHunter23/LeoPARD-Coder-0.5.7
Model Size	15.2b
Required VRAM	9.9 GB
Updated	2026-07-21
Maintainer	VortexHunter23
Model Type	qwen2
Model Files	5.0 GB: 1-of-2 4.9 GB: 2-of-2
Supported Languages	en
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.52.4
Tokenizer Class	LlamaTokenizerFast
Padding Token	<\|vision_pad\|>
Vocabulary Size	152064
Torch Data Type	bfloat16

Rank the LeoPARD Coder 0.5.9 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 LeoPARD Coder 0.5.9

Best Alternatives	Context / RAM	Downloads
LeoPARD Coder 0.6	128K / 9.9 GB	8
LeoPARD Coder 0.7.4	128K / 9.9 GB	8
LeoPARD Coder 0.8	128K / 9.9 GB	7
LeoPARD Coder 0.7	128K / 9.9 GB	5
LeoPARD Coder 0.5.7	128K / 9.9 GB	8
LeoPARD Coder 0.5.5	128K / 9.9 GB	6
LeoPARD Coder 0.4	128K / 9.9 GB	10
LeoPARD Coder 0.5	128K / 9.9 GB	8
LeoPARD Coder 0.3	128K / 9.9 GB	6
Shed Coder 0.3	128K / 9.9 GB	13

Note: green Score (e.g. "73.2") means that the model is better than VortexHunter23/LeoPARD-Coder-0.5.9.