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Nitishsharma9/CyberCoder-Mobile-7B-GGUF overview

CyberCoder Mobile 7B GGUF GGUF quantizations of Qwen2.5 Coder 7B Instruct, a powerful 7B parameter model specifically fine tuned for incredibly fast Python scr…

ggufqwen7bcybersecurityethical-hackingcode-completionfimlicense:apache-2.0endpoints_compatibleregion:usimatrixconversational

Runs locally from ~1.77 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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CyberCoder-Mobile-7B-IQ1_S.ggufGGUFIQ1_S1.77 GBDownload

Model Details

Model IDNitishsharma9/CyberCoder-Mobile-7B-GGUF
AuthorNitishsharma9
Pipeline
Licenseapache-2.0
Base model
Last modified2026-07-15T10:04:42.000Z

Model README

---

license: apache-2.0

tags:

  • qwen
  • 7b
  • gguf
  • cybersecurity
  • ethical-hacking
  • code-completion
  • fim

---

CyberCoder-Mobile-7B-GGUF

GGUF quantizations of Qwen2.5-Coder-7B-Instruct, a powerful 7B parameter model specifically fine-tuned for incredibly fast Python scripting, Fill-in-the-Middle (FIM) code completion, Ethical Hacking, and Cybersecurity operations.

Background

This model serves as a general-purpose reasoning distill specifically tailored for offensive and defensive security contexts. By taking the state-of-the-art Qwen 2.5 Coder 7B base, this model delivers massive reasoning capabilities compressed into a footprint that can run natively on mobile devices (via PocketPal) and PCs with 6GB+ RAM.

Core Capabilities:

  • Lightning Fast Python Scripting: Optimized to generate robust, production-ready Python tools in milliseconds.
  • 🛡️ Ethical Hacking & Cyber Security: Deep knowledge of vulnerability assessment, penetration testing patterns, and defensive engineering.
  • 🔄 Fill-in-the-Middle (FIM): Native support for seamless code completion right inside your IDE.

Hardware compatibility (Ultra-Compressed IQ Formats)

| Quantization | Bits | Exact File Size | RAM Required |

| --- | --- | --- | --- |

| IQ1_S | 1.56-bit | ~1.5 GB | ~2.5 GB |

| IQ2_XXS | 2.06-bit | ~1.9 GB | ~3.0 GB |

| IQ2_S | 2.50-bit | ~2.3 GB | ~3.5 GB |

| IQ3_XXS | 3.06-bit | ~2.8 GB | ~4.0 GB |

| Q4_K_M | 4.00-bit | 4.68 GB | ~5.7 GB |

🚀 How to Use

📱 PocketPal (Mobile)

  1. Download the PocketPal app on your iOS or Android device.
  2. Navigate to Models -> Add Model -> Hugging Face.
  3. Search for Nitishsharma9/CyberCoder-Mobile-7B-GGUF.
  4. Download the Q2_K or Q3_K_M file (these are the best sizes for mobile RAM limits).
  5. Load the model and start chatting entirely offline!

🦙 Ollama (PC/Mac/Linux)

You can run this natively in Ollama. Download your preferred GGUF file (e.g., CyberCoder-Mobile-7B-Q4_K_M.gguf), then create a file named Modelfile with this content:

FROM ./CyberCoder-Mobile-7B-Q4_K_M.gguf

Then build and run:

ollama create CyberCoder -f Modelfile
ollama run CyberCoder

💻 LM Studio / AnythingLLM

  1. Open LM Studio or your preferred desktop application.
  2. Search for Nitishsharma9/CyberCoder-Mobile-7B-GGUF in the search bar.
  3. Select your desired quantization (Q4_K_M is highly recommended for 6GB RAM PCs).
  4. Click Download and Load the model!

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Source: Hugging Face · Compare models