What are the hardware requirements for VulnLLM R 7B 8bit?

VulnLLM R 7B 8bit requires approximately 8.1 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 VulnLLM R 7B 8bit and how large is it?

VulnLLM R 7B 8bit is developed by mlx-community, 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 VulnLLM R 7B 8bit?

VulnLLM R 7B 8bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

VulnLLM R 7B 8bit by mlx-community — VRAM 8.1GB, 32K context

Name: VulnLLM R 7B 8bit
Author: mlx-community

VulnLLM R 7B 8bit is an open-source language model by mlx-community. Features: 7b LLM, VRAM: 8.1GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.22.

8-bit 8bit Base model:quantized:virtue-ai... Base model:virtue-ai-hub/vulnl... Code Code-analysis Conversational En Mlx Quantized Qwen2 Reasoning Region:us Safetensors Security Sharded Tensorflow Vulnerability-detection

Model Card on HF 🤗: https://huggingface.co/mlx-community/VulnLLM-R-7B-8bit

VulnLLM R 7B 8bit Parameters and Internals

LLM Name	VulnLLM R 7B 8bit
Repository 🤗	https://huggingface.co/mlx-community/VulnLLM-R-7B-8bit
Base Model(s)	VulnLLM R 7B UCSB-SURFI/VulnLLM-R-7B
Model Size	7b
Required VRAM	8.1 GB
Updated	2026-08-10
Maintainer	mlx-community
Model Type	qwen2
Model Files	5.3 GB: 1-of-2 2.8 GB: 2-of-2
Supported Languages	en code
Quantization Type	8bit
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.51.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the VulnLLM R 7B 8bit 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 VulnLLM R 7B 8bit

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Instruct 1M 4bit	986K / 4.3 GB	444	11
...wen2.5 7B Instruct 1M Bnb 4bit	986K / 5.5 GB	267	1
Qwen2.5 7B Instruct 1M 8bit	986K / 8.1 GB	63	4
Qwen2.5 7B Instruct 1M 6bit	986K / 6.2 GB	52	2
...B Instruct 1M Unsloth Bnb 4bit	986K / 7.5 GB	37	3
Legal Qwen2.5 7B Sft	128K / 15.2 GB	668	0
A1 V002	128K / 15.2 GB	7	0
Mini Pathfinder	128K / 15.2 GB	16	0
A1 V0.0.1	128K / 15.2 GB	8	0
CogitoDistil	128K / 15.2 GB	14	0

Note: green Score (e.g. "73.2") means that the model is better than mlx-community/VulnLLM-R-7B-8bit.