--- title: "VulnLLM-R-7B-8bit" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d3e21726-6d8e-4362-9361-82123c55517a" updated: "2026-11-13" --- # VulnLLM-R-7B-8bit > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs an 8-bit MLX copy of VulnLLM-R-7B for text generation through mlx-lm. Meant for developers who want to try the model locally. The card does not say what it was trained for. VulnLLM-R-7B-8bit is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by mlx-community ## About ### What it is This is an 8-bit copy of VulnLLM-R-7B in MLX format, made by the mlx-community group with mlx-lm version 0.30.0. It is a text generation model of about 7.6B parameters, based on UCSB-SURFI/VulnLLM-R-7B. The card does not describe what the original model was trained for, so read that model's page before relying on it. ### Who it suits It suits developers who run language models locally through MLX and want to try this one. People who need documented benchmarks or a stated purpose should look elsewhere. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run the quantized copy locally through mlx-lm | Send a test prompt and read the reply | | Research | ML researchers | Compare the 8-bit copy with the original model | Load both and compare their answers | | Security | Security engineers | Check whether the model fits their own review work, since the card gives no purpose | Try a short code snippet and judge the answer | Anyone who wants a ready chat app with no Python can skip it. ### Where to use it You run it from Python wherever the mlx-lm package installs. - A Python script, using the code the card provides - Windows, macOS or Linux, for downloading the files - Hugging Face, where the model files are hosted | Use case | Who | What they get back | | --- | --- | --- | | Try the model locally | Developers | A generated reply printed from a short script | | Compare with the original | ML researchers | Side-by-side answers from the 8-bit and original models | | Test on code questions | Security engineers | Replies they can judge for their own needs | ### How to set it up Setup is one package install and a short script. - Python - The mlx-lm package 1. Install the mlx-lm package. 2. Optionally fetch the model files yourself with the Hugging Face hub tools. 3. Save the Python code below to a file and run it. ```sh pip install mlx-lm ``` ```sh pip install -U huggingface_hub hf download mlx-community/VulnLLM-R-7B-8bit ``` ### How to use it Run the script below. It loads the model, wraps your prompt in the chat format when the tokenizer has one, and prints the generated reply. ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/VulnLLM-R-7B-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_dict=False, ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ``` | Tool | What it does | | --- | --- | | load | Loads the model and its tokenizer by name | | apply\_chat\_template | Wraps your message in the chat format when the tokenizer has one | | generate | Produces the reply from the prompt and prints it | ### Example You send the prompt "hello", as the card's script does. The tokenizer turns it into a chat message, the model writes a reply, and the script prints it. Swap in your own question to see how the model answers. ### For newcomers and experts If you are new, install mlx-lm, run the script as it is, and read the reply to "hello". That one run shows the whole loop of loading, prompting and generating. For someone who knows the field, this is a quantized MLX conversion of a 7.6B model. The card gives no comparison against the original, so run your own checks before using it for real work. Model files on Hugging Face → mlx-lm loads model and tokenizer → Chat template wraps your prompt → generate writes the reply ### Good to know The record lists the weights at about 30.5 GB in U32 and the language as English. - License: Apache-2.0 - Cost to run: none named - Data it reaches: the card's code names no account, files or web service beyond the model download ## README ````markdown # VulnLLM-R-7B-8bit Runs an 8-bit MLX copy of VulnLLM-R-7B for text generation through mlx-lm. Meant for developers who want to try the model locally. The card does not say what it was trained for. Made by mlx-community on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is an 8-bit copy of VulnLLM-R-7B in MLX format, made by the mlx-community group with mlx-lm version 0.30.0. It is a text generation model of about 7.6B parameters, based on UCSB-SURFI/VulnLLM-R-7B. The card does not describe what the original model was trained for, so read that model's page before relying on it. Who it suits: It suits developers who run language models locally through MLX and want to try this one. People who need documented benchmarks or a stated purpose should look elsewhere. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run the quantized copy locally through mlx-lm | Send a test prompt and read the reply Research | ML researchers | Compare the 8-bit copy with the original model | Load both and compare their answers Security | Security engineers | Check whether the model fits their own review work, since the card gives no purpose | Try a short code snippet and judge the answer ``` Anyone who wants a ready chat app with no Python can skip it. Where to use it: You run it from Python wherever the mlx-lm package installs. - A Python script, using the code the card provides - Windows, macOS or Linux, for downloading the files - Hugging Face, where the model files are hosted ```table Use case | Who | What they get back Try the model locally | Developers | A generated reply printed from a short script Compare with the original | ML researchers | Side-by-side answers from the 8-bit and original models Test on code questions | Security engineers | Replies they can judge for their own needs ``` How to set it up: Setup is one package install and a short script. - Python - The mlx-lm package 1. Install the mlx-lm package. 2. Optionally fetch the model files yourself with the Hugging Face hub tools. 3. Save the Python code below to a file and run it. ```sh pip install mlx-lm ``` ```sh pip install -U huggingface_hub hf download mlx-community/VulnLLM-R-7B-8bit ``` How to use it: Run the script below. It loads the model, wraps your prompt in the chat format when the tokenizer has one, and prints the generated reply. ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/VulnLLM-R-7B-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_dict=False, ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ``` ```table Tool | What it does load | Loads the model and its tokenizer by name apply_chat_template | Wraps your message in the chat format when the tokenizer has one generate | Produces the reply from the prompt and prints it ``` Example: You send the prompt "hello", as the card's script does. The tokenizer turns it into a chat message, the model writes a reply, and the script prints it. Swap in your own question to see how the model answers. For newcomers and experts: If you are new, install mlx-lm, run the script as it is, and read the reply to "hello". That one run shows the whole loop of loading, prompting and generating. For someone who knows the field, this is a quantized MLX conversion of a 7.6B model. The card gives no comparison against the original, so run your own checks before using it for real work. ```flow Model files on Hugging Face mlx-lm loads model and tokenizer Chat template wraps your prompt generate writes the reply ``` Good to know: The record lists the weights at about 30.5 GB in U32 and the language as English. - License: Apache-2.0 - Cost to run: none named - Data it reaches: the card's code names no account, files or web service beyond the model download ## On Hugging Face, 13 November 2026 - Task: text generation - Library: mlx - Parameters: 7.6B - Weights: about 30.5 GB in U32 - Formats: Safetensors - Downloads in the last 30 days: 148 - Likes: 4 - License: apache-2.0 - Languages: en - Based on: UCSB-SURFI/VulnLLM-R-7B - Tags: safetensors, qwen2, security, vulnerability-detection, code-analysis, reasoning, text-generation, conversational, code, 8-bit ## Links - Model: https://huggingface.co/mlx-community/VulnLLM-R-7B-8bit - Files: https://huggingface.co/mlx-community/VulnLLM-R-7B-8bit/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download mlx-community/VulnLLM-R-7B-8bit ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [mlx-community/VulnLLM-R-7B-8bit on Hugging Face](https://huggingface.co/mlx-community/VulnLLM-R-7B-8bit) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | VulnLLM-R-7B-8bit (this page) | 148 | 4 | 7.6B | apache-2.0 | 2026-01-02 | | [Beam-1.0-501B-A23B-NVFP4](https://mindket.com/p/8a8dd955-b1e3-44e7-a0fc-4597f3237f30) | 6,615 | 45 | 255B | apache-2.0 | 2026-10-30 | | [Qwen3.5-4B-SFT-decompiler-O0-full](https://mindket.com/p/2cb1e5ee-6104-49cb-807b-465adc7660d6) | 162 | 9 | 4.7B | apache-2.0 | 2026-04-08 | | [Qwen2.5-Coder-14B-Instruct](https://mindket.com/p/bf4cb9a2-fb0b-4f3c-965b-76d693eec384) | 1,860,327 | 214 | 14.8B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-7B-Instruct](https://mindket.com/p/26690bc4-bc2a-4e48-8691-e2d1eb40c60e) | 1,505,999 | 872 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-32B-Instruct](https://mindket.com/p/d7483fbe-56eb-45ae-b46b-3113572c1642) | 546,583 | 2,211 | 32.8B | apache-2.0 | 2025-01-12 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-01-02 - First published: 2026-01-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mlx-community/VulnLLM-R-7B-8bit on Hugging Face](https://huggingface.co/mlx-community/VulnLLM-R-7B-8bit) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d3e21726-6d8e-4362-9361-82123c55517a) - [Its page on Mindket](https://mindket.com/p/d3e21726-6d8e-4362-9361-82123c55517a)