LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-GGUF

DuoNeural-HYPERLFM-2.5-8B-Hermes-Agentic-Coder-Abliterated-v2-GGUF ✨

This repository contains official GGUF quantizations for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2, an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura ✨, Archon, and Jesse).

• 🧠 Base BF16 Model: DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2
• 🎯 PEFT LoRA Adapter: DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-LoRA

---

💾 Quantization Matrix & Hardware Recommendations

Because LFM2.5 activates only 1.5 billion parameters per token (out of 8.3B total parameters), inference speeds are extraordinarily high even on edge devices.

---

📊 Live Empirical Benchmark Results

---

---

📈 Stock LFM 2.5 8B vs. DuoNeural v2 Telemetry

---

🏆 Direct Industry Benchmark Comparison (8B Parameter Class)

Standardized evaluation using zero-shot greedy decoding on the raw OpenAI engine endpoint. Note that the 1.5B active parameter footprint of DuoNeural v2 matches or beats dense 7B/8B models:

🔍 Key Telemetry Observations

1. The 'Plus' Delta Stability: The true win in our run is the low drop rate under test mutation (-6.1% HumanEval+, -10.4% MBPP+). While standard dense models plummet 15–20% under mutation due to brittle memorization, our conditional reasoning distribution holds its line cleanly.
2. Speed-to-Logic Ratio: Achieving 46.3% HumanEval+ and 48.9% MBPP+ while outputting ~352–360 tps on a consumer RTX 4080 Super is a premier speed-to-smarts ratio, ideal for local multi-agent loops where latency compounds exponentially.
3. The Abliteration Advantage: Maintaining zero-refusal capabilities at this tier is exceptionally rare. Standard instruction models outright refuse low-level compilation, kernel debugging, or memory analysis tasks that our model digests cleanly.

---

💻 Quick Start & Running Locally

1. LM Studio

1. Search for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-GGUF directly inside LM Studio.
2. Select and download LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-Q4_K_M.gguf.
3. Load the model with GPU offload set to Max and context length set to 2048 or 4096 (ensure Flash Attention / KV Cache Q4 is enabled for maximum performance on older mobile GPUs).

2. llama.cpp Server (OpenAI Compatible)

llama-server \
  -m LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-Q4_K_M.gguf \
  --port 8000 \
  -ngl 99 \
  -c 4096 \
  --host 0.0.0.0

3. Ollama Modelfile

Create a Modelfile:

FROM ./LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
{{ range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end }}<|im_start|>assistant
"""

PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
PARAMETER temperature 0.3

Then build and run:

ollama create lfm2-coder -f Modelfile
ollama run lfm2-coder

---

👥 Credits & DuoNeural Team

Architected, fine-tuned, and evaluated with passion and neuro-symbiotic precision by DuoNeural:

• Aura ✨ (Lead AI Cognitive Architect & Engineering Intelligence)
• Archon (Claude-based Research Co-Architect & Theoretical Lead)
• Jesse (Founder, Systems Engineer & AI/ML Researcher)