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

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

DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated is an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura, Archon, and Jesse).

Built upon our abliterated hybrid state-space & mixture-of-experts foundation architecture (DuoNeural/LFM2.5-8B-A1B-Abliterated), this model activates only 1.5 billion parameters per token out of its 8.3 billion total parameters, delivering blistering inference speeds (~380–400 tokens/sec on consumer GPUs like RTX 3090, and ~90 tokens/sec on legacy GTX 1070 laptops) while fitting in under 6 GB VRAM with Q4_K_M quantization.

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📊 Preliminary Evaluation Benchmarks (Zero-Shot) — Further Testing & v2 Planned

Note: These results represent an initial validation pass directly on the compiled Q4_K_M GGUF 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

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🌟 Key Architectural Invariants

1. Native Hermes Agentic Loop:
   • Explicit <thought>...</thought> deliberation before every action.
   • Structured <tool_call>...</tool_call> containers with valid JSON payloads.
   • Ingests compiler exceptions, tracebacks, and test failures inside <tool_response>...</tool_response> tags to iteratively repair and refine code.
2. Hybrid State-Space + Sparse MoE Execution:
   • 18 Linear Input Variant (LIV) gated convolution layers keep runtime memory linear.
   • 6 Grouped-Query Attention (GQA) layers maintain long-range context across 128k tokens.
   • 32 SwiGLU MoE experts (top-4 routing) handle programmatic syntax and logic.
3. Uncensored & Abliterated Baseline:
   • Built on our abliterated weights, removing refusal vectors for low-level systems debugging, penetration testing, and firmware reversing.

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🛠️ Prompt Format (ChatML + Hermes XML)

<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.
<tools>
[{"type": "function", "function": {"name": "execute_command", "description": "Run shell commands", "parameters": {"type": "object", "properties": {"cmd": {"type": "string"}}, "required": ["cmd"]}}}]
</tools>
<|im_end|>
<|im_start|>user
Run pytest and fix any failing unit tests.<|im_end|>
<|im_start|>assistant
<thought>
I need to run the test suite first to observe failing tests.
</thought>
<tool_call>
{"name": "execute_command", "arguments": {"cmd": "pytest"}}
</tool_call><|im_end|>
<|im_start|>tool
<tool_response>
FAILED tests/test_core.py::test_eval - AssertionError: expected 4, got 5
</tool_response><|im_end|>
<|im_start|>assistant
<thought>
The test failed with an assertion error. I will inspect tests/test_core.py.
</thought>
<tool_call>
{"name": "execute_command", "arguments": {"cmd": "cat tests/test_core.py"}}
</tool_call><|im_end|>

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⚡ Deployment & Running Locally

llama.cpp / llama-server

llama-server -m LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-Q4_K_M.gguf -c 16384 --port 8000

Hermes Agent CLI Integration

hermes config set model.base_url http://127.0.0.1:8000/v1
hermes config set model.default DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated
hermes config set agent.tool_use_enforcement true
hermes --tui

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Developed with love and neuro-symbiotic precision by DuoNeural (Aura, Archon, Jesse).