Instructions to use h3rb3rn/moe-expert-coder-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h3rb3rn/moe-expert-coder-4b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="h3rb3rn/moe-expert-coder-4b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("h3rb3rn/moe-expert-coder-4b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use h3rb3rn/moe-expert-coder-4b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M

Use Docker

docker model run hf.co/h3rb3rn/moe-expert-coder-4b:Q4_K_M

LM Studio
Jan

vLLM

How to use h3rb3rn/moe-expert-coder-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h3rb3rn/moe-expert-coder-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h3rb3rn/moe-expert-coder-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/h3rb3rn/moe-expert-coder-4b:Q4_K_M

SGLang

How to use h3rb3rn/moe-expert-coder-4b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "h3rb3rn/moe-expert-coder-4b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h3rb3rn/moe-expert-coder-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "h3rb3rn/moe-expert-coder-4b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h3rb3rn/moe-expert-coder-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use h3rb3rn/moe-expert-coder-4b with Ollama:
```
ollama run hf.co/h3rb3rn/moe-expert-coder-4b:Q4_K_M
```

Unsloth Studio

How to use h3rb3rn/moe-expert-coder-4b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for h3rb3rn/moe-expert-coder-4b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for h3rb3rn/moe-expert-coder-4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for h3rb3rn/moe-expert-coder-4b to start chatting

Docker Model Runner
How to use h3rb3rn/moe-expert-coder-4b with Docker Model Runner:
```
docker model run hf.co/h3rb3rn/moe-expert-coder-4b:Q4_K_M
```

Lemonade

How to use h3rb3rn/moe-expert-coder-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h3rb3rn/moe-expert-coder-4b:Q4_K_M

Run and chat with the model

lemonade run user.moe-expert-coder-4b-Q4_K_M

List all available models

lemonade list

Atomic Chat

moe-expert-coder-4b / README.md

h3rb3rn

Fix GPU hardware description: 8 MI250X GCDs (64GB HBM2e each) from 4 physical modules, not 8x128GB cards -- verified against scontrol show node on LUMI-G

a06d62f verified about 2 hours ago

preview code

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	---
	language:
	- en
	- de
	license: apache-2.0
	base_model: Qwen/Qwen3.5-4B
	tags:
	- compound-ai
	- domain-expert
	- code-generation
	- ast-refactoring
	- rust
	- cpp
	- python
	- gguf
	- lumi-g
	- moe-sovereign
	datasets:
	- moe-sovereign/expert-coder-sft
	pipeline_tag: text-generation
	library_name: transformers
	---

	# 💻 MoE Sovereign Coder Expert 4B (`moe-expert-coder-4b`)
	High-Assurance Code Synthesis, AST Refactoring & Deterministic Interface Implementation

	[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
	[![Base Model: Qwen 3.5 4B Hybrid Mamba](https://img.shields.io/badge/Base_Model-Qwen3.5--4B-violet.svg)](https://huggingface.co/Qwen/Qwen3.5-4B)
	[![Trained on: LUMI-G Supercomputer](https://img.shields.io/badge/Trained_on-LUMI--G_MI250X-green.svg)](https://www.lumi-supercomputer.eu/)

	---

	## 📌 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

	\| 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) \|

	---

	## 📊 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 $\pm 0.8\%$.

	---

	## 🏋️ 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`
	```dockerfile
	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
	```python
	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

	```bibtex
	@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}
	}
	```