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GOBA-OLMoE-Expert-Tuned: Domain-Specialized MoE via Expert Tuning

Name: GOBA-OLMoE-Expert-Tuned
Availability: InStock

3 domain-specialized variants | JA / Finance / Code | No general performance loss | GGUF Q4_K_M | Apache 2.0

Domain-adapted variants of OLMoE-1B-7B-0125-Instruct created using Expert Tuning — a novel approach that repurposes low-importance expert slots in Mixture-of-Experts models for domain-specific adaptation, as an alternative to LoRA or full fine-tuning.

Highlights

3 domain variants: Japanese, Finance, and Code — each tuned independently
Zero general performance loss: MMLU and GSM8K scores remain within ±2pp of the original
Domain improvements confirmed: JMMLU +4.5pp, HumanEval+ +10pp, EDINET-Bench Fraud Detection +16pp
Drop-in replacement: works with llama.cpp with no code changes
Apache 2.0: fully open for commercial use

Included Models

File	Domain	Size	Description
`OLMoE-ja-tuned.gguf`	Japanese	3.9 GB	Japanese language comprehension and generation
`OLMoE-finance-tuned.gguf`	Finance	3.9 GB	Financial analysis, fraud detection, regulatory knowledge
`OLMoE-code-tuned.gguf`	Code	3.9 GB	Python code generation and reasoning

Benchmark Results

General Benchmarks (no domain bias)

Benchmark	Original	JA-tuned	Finance-tuned	Code-tuned
MMLU (0-shot, 100Q)	53%	54% (+1pp)	51% (-2pp)	52% (-1pp)
GSM8K (0-shot, 50Q)	66%	66% (=)	66% (=)	66% (=)

Domain-Specific Benchmarks

Benchmark	Original	Tuned	Delta	Verdict
JMMLU (200Q, stratified from 53 subjects)	30.0%	34.5%	+4.5pp	POSITIVE
EDINET-Bench (100Q, earnings + fraud)	45.0%	46.0%	+1.0pp	NEUTRAL
— Fraud detection subset	34.0%	50.0%	+16.0pp	POSITIVE
— Earnings forecast subset	56.0%	42.0%	-14.0pp	Regression
HumanEval+ (20Q subset)	20.0%	30.0%	+10.0pp	POSITIVE

Note: OLMoE-1B-7B has 1.3B active parameters, so absolute scores are lower than larger models. The relative improvements from Expert Tuning are the key result.

Model Details

Property	Value
Base model	allenai/OLMoE-1B-7B-0125-Instruct
Architecture	Transformer with Sparse MoE (SwiGLU experts)
Total / Active parameters	6.9B / 1.3B
MoE layers	16
Experts per layer	64 (top-8 routing)
Hidden dimension	2048
Expert FFN dimension	1024 (SwiGLU: gate + up + down)
Context length	4096 tokens
Quantization	Q4_K_M GGUF
License	Apache 2.0

What is Expert Tuning?

Expert Tuning is a novel domain adaptation technique for Mixture-of-Experts (MoE) models. Instead of adding external adapters (LoRA) or fine-tuning all parameters, it identifies low-importance experts within the existing MoE architecture and replaces them with domain-specialized experts trained via knowledge distillation.

Key advantages over LoRA:

No additional parameters at inference time (experts replace existing slots)
Native MoE routing automatically directs domain-relevant tokens to specialized experts
Compatible with quantized GGUF inference — no adapter merging needed

Method overview:

Compute importance scores for all experts across layers
Select bottom-k experts as candidates for replacement
Train domain-specific experts using cross-expert knowledge distillation with domain data
Insert trained experts back into the GGUF with lossless Q4_K/Q6_K quantization

Training Data

Domain	Dataset	Records	License
Japanese	izumi-lab/llm-japanese-dataset	50,000	CC BY-SA 4.0
Finance	ronantakizawa/Finance-Instruct-500k-Japanese + y2lan/japan-law	~250,000	Apache 2.0 / Public Domain
Code	nvidia/OpenCodeReasoning-2 (Python subset)	50,000	CC BY 4.0

How to Use

With llama.cpp

bash

# Run with llama-server (e.g., Japanese-tuned variant)
llama-server \
  -m OLMoE-ja-tuned.gguf \
  --port 8090 \
  -ngl 99 \
  -c 4096

# Query via OpenAI-compatible API
curl http://localhost:8090/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "olmoe",
    "messages": [{"role": "user", "content": "日本の金融政策について説明してください"}],
    "max_tokens": 512
  }'

With moe-stream

moe-stream OLMoE-ja-tuned.gguf --server --preload-gates --preload-attn

Technical Notes

Q4_K/Q6_K quantizer: Custom Python implementation matching llama.cpp reference, with correct sub-block min clamping, qs packing order, and symmetric signed encoding
Insertion CosSim: 0.9974 (quantized vs. original trained weights), confirming lossless insertion
Training CosSim: 0.831 average (teacher-student similarity after KD), indicating meaningful domain adaptation while preserving expert structure
64 trained experts per model: 16 layers x 4 experts/layer replaced

Limitations

OLMoE-1B-7B has only 1.3B active parameters, limiting absolute performance on complex tasks
Domain benchmarks use moderate sample sizes (20-200 questions); larger evaluations may show different effect sizes
Finance-tuned model shows prediction bias toward fraud detection, with regression on earnings forecasting tasks
Expert Tuning effectiveness scales with the number of experts per layer; models with fewer experts (e.g., 8-16) have less capacity for domain injection

Citation

bibtex

@misc{goba2026expert,
  title={Expert Tuning: Domain Adaptation via Expert Slot Repurposing in Mixture-of-Experts Models},
  author={GOBA AI Labs},
  year={2026},
  url={https://huggingface.co/goba-ai-labs/GOBA-OLMoE-Expert-Tuned}
}

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PrunedHub-Qwen3-30B-A3B-JA-MxMoE — Language-aware MoE pruning

Built by GOBA AI Labs — Making large MoE models practical on consumer hardware.

GOBA-AI-Labs/GOBA-OLMoE-Expert-Tuned