base_model: Qwen/Qwen2.5-Coder-14B-Instruct
tags:
- text-generation
- gguf
- code
- coding-assistant
- qwen2.5-coder
license: apache-2.0
language: - en
pipeline_tag: text-generation
"This is humanity's race.
The solution is open source.
Stay sovereign."
— AIOpsInSpace
📑 Table of Contents
Qwen2.5-Coder-14B-Instruct-Uncensored-Patched
AIOpsInSpace OfficialHighly efficient 14B code generation model patched for IDE plugin stability.
💻 14B Dense Model
⚡ Code Assistant Optimized
🛠️ IDE Plugin Hang Patched
> What is this model and Why is it Needed?
Qwen2.5-Coder-14B-Instruct-Uncensored-Patched is built on top of Qwen/Qwen2.5-Coder-14B-Instruct.
Why it is needed: Provides top-tier coding performance for 16GB VRAM GPUs with fixed autocomplete token handling.
> From the Parent Repository
"Sweet-spot coding power for local developer setups."
— Qwen Code Team
🏗️ 2. Model Architecture & Merging
Architecture: Qwen 2.5 Coder 14B Transformer Architecture
Merging Technique: FIM Tokenizer Patching
Constituent Models:
Merging Technique: FIM Tokenizer Patching
Constituent Models:
Base Model: Qwen/Qwen2.5-Coder-14B-Instruct
🚀 3. Technical Enhancements
> Key Upgrades Over Base Model:
- High-Speed Autocomplete: Instant inline completion on local machines.
- Uncensored: Generates security, reverse engineering, and script logic without refusal.
📊 4. Benchmark Competitiveness vs. Frontier Scores
🏆 5. Comprehensive Arena Analytics
> Status: Active Community Benchmarking
// Note: Arena Elo and head-to-head winrates updated continuously as evaluation telemetry processes.🔍 6. SWOT Analysis
> Strengths (S)
- 🛡️ Uncensored Fidelity: Surgically patched to ensure maximum generation throughput without alignment overhead.
- ⚡ Optimized Engine: Advanced mechanics ensure zero context fragmentation or execution hangs.
> Weaknesses (W)
- 📉 Hardware Limits: Requires sufficient VRAM/RAM for higher precision GGUF quantizations.
> Opportunities (O)
- 🎯 Local Sovereign Agents: Perfect for offline, private reasoning and agentic workflows.
> Threats (T)
- ⚠️ Sampler Sensitivity: High temperatures may require repetition penalty adjustments.
⚡ 7. Usage & Deployment Info
> Recommended Settings
- Temperature: 0.2 - 0.7
- Top-P: 0.95
- Backend Engines: Compatible with llama.cpp, vLLM, Ollama, LM Studio, KoboldCPP
⚙️ 8. Backend Compatibility
> Validated Engines:
- [+] llama.cpp: Native support across all quantizations.
- [+] Ollama / LM Studio: Full GGUF compatibility.
📜 9. Disclaimers & Credits
Disclaimer: Qwen2.5-Coder-14B-Instruct-Uncensored-Patched is provided for research and sovereign local deployment. As an unaligned model, users are responsible for ensuring usage complies with local laws.
Credits: Gratitude to original base model authors (Qwen/Qwen2.5-Coder-14B-Instruct) and open-source AI community tools.
Credits: Gratitude to original base model authors (Qwen/Qwen2.5-Coder-14B-Instruct) and open-source AI community tools.