h3rb3rn / moe-expert-coder-4b

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

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

📌 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 GCDs (4× physical modules, 64GB HBM2e per GCD)).

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.

🎯 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.

🎯 Training Objectives & Intended Behavioral Specialization

📊 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 ):

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

🏋️ 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 #21190761 )
• 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): 0.03638
• Token Accuracy (Final): 99.62 %

⚠️ 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.

💻 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))

📑 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}
}