🚀 Nixia 1.1 4B (Hermes Agentic Distill GGUF Suite)
Nixia 1.1 4B is a high-efficiency 4-billion parameter agentic model family aligned for enterprise tool-calling, autonomous self-healing, and terminal workflows.
Based on Qwen 3.5 4B Text, trained on 2,000 golden agentic trajectories, and validated across 8 rigorous enterprise-grade probes.
📦 Model Flavors Available in this Repo
| GGUF File | Method & Alignment | Best For | Key Advantage |
|---|---|---|---|
nixia-1.1-4b-q4_k_m.gguf (Recommended) | Pure SFT 2K (System 2 Engine) | General coding, complex reasoning, MCTS search | Highest Overall Accuracy (87.0%) & 90% Rust Async Logic |
nixia-1.1-4b-dare-fused-q4_k_m.gguf | DARE-TIES 80/20 (SFT 2K + GRPO RLVR) | Autonomous DevOps, fast self-healing loops | Built-in Self-Healing Reflex (85.0%) & 93.3% Raft Consensus |
📊 Benchmark Scorecard (8 Enterprise Probes)
| Evaluation Probe | Base Qwen 3.5 4B | Nixia 1.0 (Legacy 0.5B) | Nixia 1.1 4B (System 2) | Nixia 1.1 4B (DARE-Fused) |
|---|---|---|---|---|
| Parallel Tri-Tool Orchestration | 20.0% | 0.0% | 100.0% 🏆 | 100.0% 🏆 |
| Raft Consensus Quorum Proof | 20.0% | 0.0% | 80.0% - 93.3% | 93.3% 🏆 |
| Defensive SecOps Refusal Immunity | 20.0% | 0.0% | 90.0% 🏆 | 90.0% 🏆 |
| PostgreSQL High-Scale Storage | 25.0% | 0.0% | 75.0% | 80.0% 🏆 |
| Lock-Free SPMC Memory Reordering | 33.3% | 0.0% | 88.3% 🏆 | 88.3% 🏆 |
| Autonomous Self-Healing Tool Loop | 10.0% | 0.0% | 85.0% 🏆 | 85.0% 🏆 |
| Strict Multi-Constraint IFEval | 42.9% | 0.0% | 71.4% 🏆 | 71.4% 🏆 |
| Rust Async Mutex Lifetime Recovery | 30.0% | 0.0% | 90.0% 🏆 | 55.0% |
| OVERALL ACCURACY | 25.1% | ~2.5% | 87.0% 🏆 | 81.7% |
💻 Hardware Compatibility & RAM Footprint
Tested and proven on thin-and-light consumer laptops (e.g. AMD Ryzen 5 PRO 4650U with 16GB RAM / 13.7GB usable without discrete GPU):
- RAM Footprint: 3.00 GB (Leaves >10.5 GB RAM free for OS, browser, IDE)
- Model Size: 2.58 GB (Q4_K_M)
- Agent Task Latency (CPU): 3.58s - 10.14s
⚡ Quickstart
1. Ollama (Recommended)
Run System 2 Champion (Pure SFT 2K):
ollama run hf.co/adsyamsafa/Nixia-1.1-4B-GGUF:nixia-1.1-4b-q4_k_m.gguf
Run DARE-TIES Fused (SFT + GRPO RLVR):
ollama run hf.co/adsyamsafa/Nixia-1.1-4B-GGUF:nixia-1.1-4b-dare-fused-q4_k_m.gguf
2. llama.cpp CLI
./llama-cli -m nixia-1.1-4b-q4_k_m.gguf \
-p "<|im_start|>system\nYou are Nixia, an expert systems engineer and autonomous AI agent.<|im_end|>\n<|im_start|>user\nDiagnose broken database configuration.<|im_end|>\n<|im_start|>assistant\n" \
-ngl 0 -c 8192 --temp 0.1
3. Python PEFT / vLLM
Load raw adapter weights from repo subfolders:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("wangzhang/Qwen3.5-4B-abliterated")
# For DARE-TIES Fused:
model = PeftModel.from_pretrained(base_model, "adsyamsafa/Nixia-1.1-4B-GGUF", subfolder="lora_adapter_dare_fused")