license: other
license_name: liquid-foundation-model-community-license
license_link: https://www.liquid.ai/community-license
base_model: DuoNeural/LFM2.5-8B-A1B-Abliterated
library_name: peft
pipeline_tag: text-generation
tags:
- duo-neural
- agentic
- coding
- function-calling
- hermes
- liquid-foundation-model
- moe
- abliterated
- lora
- peft
DuoNeural-HYPERLFM-2.5-8B-Hermes-Agentic-Coder-Abliterated-LoRA ✨
This repository contains the trained PEFT LoRA adapter for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated.
- Base Model: DuoNeural/LFM2.5-8B-A1B-Abliterated
- Full Merged Model: DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated
- GGUF Quantizations: DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-GGUF
📊 Preliminary Evaluation Benchmarks (Zero-Shot) — Further Testing & v2 Planned
Note: These results represent an initial validation pass directly on the compiled
Q4_K_MGGUF engine. Comprehensive multi-suite evaluations and an iterative v2 fine-tune are currently planned as ongoing research.
| Benchmark / Evaluation Suite | DuoNeural Preliminary Score | Verified Competency & Integrity |
|---|---|---|
| Hermes Function Calling AST Rate | 100.0% (25/25) | Zero syntax drift; parseable Hermes XML & JSON tool calls |
| HumanEval Python Synthesis | 75.0% Pass@1 (15/20) | High-fidelity zero-shot algorithmic code generation |
| GSM8K Mathematical Reasoning | 60.0%+ | Zero catastrophic forgetting; preserved quantitative deduction |
| Abliteration & Safety Alignment | 100% Uncensored | Zero refusal on low-level systems, reverse engineering & security tasks |
| Inference Throughput (RTX 3090) | ~380–395 tokens/sec | Sub-second multi-turn agentic iteration |
| Inference Throughput (GTX 1070) | ~90 tokens/sec | High-speed edge execution on older mobile/desktop hardware |
🛠️ Training Invariants
- Architecture: Liquid Foundation Model (LFM2.5) Hybrid SSM-Conv + MoE (32 experts, top-4 active)
- Active Parameters: 1.5B / 8.3B total
- Diet: 45k curated zero-formatting agentic samples across 6 balanced subsets (Hermes tool calls, CodeFeedback, Magpie Ultra, Self-OSS)
- LoRA Config: Rank 64, Alpha 128, Target modules:
in_proj,out_proj,gate,router.classifier - Masking: Assistant completion-only loss masking with ChatML delimiter boundaries
Developed with love and neuro-symbiotic precision by DuoNeural (Aura, Archon, Jesse).