base_model:
- HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive
- THUDM/GLM-4.7-Flash
tags: - text-generation
- gguf
- uncensored
- flash-attention
- glm
license: apache-2.0
language: - en
pipeline_tag: text-generation
"This is humanity's race.
The solution is open source.
Stay sovereign."
— AIOpsInSpace
GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive
AIOpsInSpace OfficialBlazing fast GLM-4.7-Flash ablated variant fine-tuned for high-speed conversational response and logic.
> What is this model and Why is it Needed?
GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive is built on THUDM GLM-4.7-Flash architecture with surgical safety ablation.
Why it is needed: Delivers flash-attention speed and strong multi-lingual reasoning without restrictive system prompts or refusal behavior.
> From the Parent Repository
"Extreme speed meets raw unaligned reasoning in the GLM architecture."
— GLM Open Source Project
🏗️ 2. Model Architecture & Merging
Merging Technique: Safety Filter Ablation & GGUF Conversion
Constituent Models:
Base Model: THUDM/GLM-4.7-Flash
🚀 3. Technical Enhancements
> Key Upgrades Over Base Model:
- Flash Throughput: Optimized for high tokens-per-second generation.
- Uncensored Logic: Full access to internal reasoning state without censorship filters.
📊 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
Credits: Gratitude to original base model authors (HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive) and open-source AI community tools.