DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-LoRA

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

This repository contains the trained PEFT LoRA adapter for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2.

• —Base Model: DuoNeural/LFM2.5-8B-A1B-Abliterated
• —Full Merged Model: DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2
• —GGUF Quantizations: DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-GGUF

📊 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. 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. 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. 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.

🛠️ Training Invariants & LoRA Configuration

• —Base Model: DuoNeural/LFM2.5-8B-A1B-Abliterated (8.3B total params, 1.5B active per token)
• —Sequence Length: 2,048 tokens
• —LoRA Rank ($r$): 64
• —LoRA Alpha ($\alpha$): 128
• —LoRA Dropout: 0.05
• —Target Modules: ['q_proj', 'k_proj', 'v_proj', 'out_proj', 'in_proj', 'w1', 'w2', 'w3', 'classifier']
• —Diet: 47,185 balanced samples across 7 domains with strict assistant completion loss masking and Bespoke-Stratos-17k reasoning injections to eliminate the early EOS anomaly.

💻 How to Load the Adapter

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "DuoNeural/LFM2.5-8B-A1B-Abliterated"
lora_model_id = "DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-LoRA"

tokenizer = AutoTokenizer.from_pretrained(lora_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(base_model, lora_model_id)
model = model.merge_and_unload() # Optional: merge weights for zero-overhead inference

👥 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)