💻 MoE Sovereign Coder Expert 4B (moe-expert-coder-4b)

High-Assurance Code Synthesis, AST Refactoring & Deterministic Interface Implementation

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📌 Executive Summary & Architectural Role

moe-expert-coder-4b is a specialized 4-billion parameter Small Language Model (SLM) distilled from DeepSeek-Coder-V2 (236B) and DeepSeek-V3 on the LUMI-G Supercomputer (8× AMD Instinct™ MI250X 128GB GPUs).

Within the MoE Sovereign compound AI system, this model serves as the High-Assurance Systems Programming & Code Synthesis Expert. It is purpose-tuned not to act as a general conversational agent, but to produce precise code, atomic unified diffs, and AST-compliant implementations for concurrent systems, systems-level tooling (Rust, C++, Python, Go), and low-latency algorithms.

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🎯 Functional Scope & Capabilities

1. High-Assurance Systems Code Synthesis: Implements lock-free data structures, memory orderings (Acquire/Release), SIMD vectorization, and OS-level primitives.
2. Deterministic Contract Compliance: Trained and evaluated against strict AST/linter invariants and deterministic compiler contracts (e.g. rustc --deny warnings, clang-tidy, ruff, mypy --strict).
3. Atomic Unified Diff Generation: Outputs structured, syntax-valid patch hunks designed for automated headless ingestion by developer toolchains.
4. Zero-Fluff Implementation: Bypasses conversational preambles to directly yield typed signatures, implementations, and regression test suites.

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🎯 Training Objectives & Intended Behavioral Specialization

Capability	Base Stock Qwen 3.5 4B	moe-expert-coder-4b (Distilled)
Output Style	Verbose conversational explanations with markdown blocks	Direct Code & Atomic Diffs; minimal commentary, maximal type clarity
Memory Semantics	Often defaults to relaxed/ad-hoc concurrency	Explicit Atomic Orderings (AcqRel, SeqCst) with thread-safety justification
Diff Accuracy	Frequently hallucinated line numbers and fuzzy anchors	Exact Line Anchors with intact unified diff headers (--- a/, +++ b/)
Type Discipline	Missing optional/generic constraints in complex types	Strict Type Invariants (Rust lifetimes, C++20 concepts, Python TypeVars)

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📊 Empirical Evaluation (Held-Out Benchmark Suite)

ℹ️ Evaluation Status: Evaluated on held-out validation splits ($N=1,000$, zero training contamination). Full cross-architecture ablation suites across Compound AI vs. Monolithic LLMs are undergoing active execution in the Sovereign Scientific Benchmark Suite v1.

Evaluated on a held-out test split of 1,000 multi-language software engineering tasks with zero training overlap, verified against native compiler pipelines (rustc 1.85, clang 19, python 3.13 with mypy):

Evaluation Metric	Base Stock Qwen 3.5 4B	moe-expert-coder-4b (Distilled)	Delta ($\Delta$)
Syntax Validity (First-Pass)	82.4 %	99.6 %	+17.2 %
AST Parse Rate	78.1 %	98.9 %	+20.8 %
Strict Linter Pass Rate (clippy/ruff)	64.3 %	95.2 %	+30.9 %
Unified Diff Application Success	71.0 %	97.8 %	+26.8 %
Memory Safety Invariant Hold (Rust/C++)	56.4 %	91.5 %	+35.1 %
Functional Correctness (Unit Tests)	51.8 %	79.4 %	+27.6 %

Note: All tests were evaluated at temperature=0.05 across 3 independent seeds with 95% confidence intervals within $\pm 0.8%$.

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🏋️ Training Setup & Distillation Methodology

+-----------------------------------------------------------------------------------+
|                            LUMI-G DISTILLATION PIPELINE                           |
|                                                                                   |
|  [ Teachers: DeepSeek-Coder-V2 (236B) + DeepSeek-V3 ]                             |
|                       |                                                           |
|                       v  (AST Parse Validation + Compiler Linter Filtering)      |
|  [ SFT Dataset: 32,500 AST-Verified Coding Trajectories ]                         |
|                       |                                                           |
|                       v  (DeepSpeed ZeRO-2, ROCm 7.0, PyTorch 2.6, 8x MI250X)     |
|  [ Student: Qwen3.5-4B Hybrid Linear Attention + Mamba Base ]                     |
|                       |                                                           |
|                       v  (LoRA r=16, alpha=32, target_modules: q/k/v/o/gate/up/down)|
|  [ Output: final_adapter -> CPU-BF16 Merge -> GGUF Q4_K_M & Q8_0 ]                |
+-----------------------------------------------------------------------------------+

Hyperparameters:

• Compute Cluster: LUMI-G (8× AMD Instinct MI250X 128GB GPUs, Slurm Job #21189558)
• Base Architecture: Qwen3.5-4B (Hybrid Linear Attention + Mamba in BF16)
• Dataset Size: 32,500 curated, compiler-checked trajectories
• Epochs: 3.0
• Effective Batch Size: 128 (Micro-batch 4 × 8 GPUs × Gradient Accumulation 4)
• Learning Rate: $1.5 \times 10^{-5}$ with Cosine Decay and Warmup
• LoRA Configuration: $r=16$, $\alpha=32$, Dropout $0.05$, Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training Loss (Final): 0.0106
• Token Accuracy (Final): 99.62 %

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⚠️ Known Limitations & Failure Modes

1. Architecture-Specific Inline Assembly: Highly exotic CPU targets (e.g. custom DSP or niche RISC-V extensions) require human validation of instruction encodings.
2. Deep Macro Expansions: Complex recursive macro expansions (e.g. deeply nested C++ template metaprogramming or procedural Rust macros spanning multiple crates) should be paired with compiler verification in the compound loop.
3. Bounded Context Scope: While context capacity supports up to 256k tokens, optimal single-turn code generation precision occurs within chunks under 16k tokens.

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💻 Quickstart Guide (Ollama & Llama.cpp)

1. Ollama Modelfile

FROM ./moe-expert-coder-4b-Q4_K_M.gguf
PARAMETER num_ctx 262144
PARAMETER temperature 0.05
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

2. Python Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "h3rb3rn/moe-expert-coder-4b"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "<|im_start|>user\nImplement a lock-free MPSC ring buffer in Rust using AtomicUsize and explicit memory ordering.<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.05)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📑 Citation

@misc{moe_sovereign_2026_coder4b,
  author = {Horn, Philipp and MoE Sovereign Core AI Team},
  title = {MoE Sovereign Coder Expert 4B: High-Assurance Code Synthesis SLM},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/h3rb3rn/moe-expert-coder-4b}},
  note = {Trained on the EuroHPC LUMI-G Supercomputer}
}