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README.md

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+---
+license: apache-2.0
+library_name: gguf
+base_model: allenai/OLMoE-1B-7B-0125-Instruct
+tags:
+  - moe
+  - mixture-of-experts
+  - expert-tuning
+  - domain-adaptation
+  - gguf
+  - q4_k_m
+  - olmoe
+  - japanese
+  - finance
+  - code
+  - knowledge-distillation
+model_name: GOBA-OLMoE-Expert-Tuned
+pipeline_tag: text-generation
+language:
+  - en
+  - ja
+datasets:
+  - izumi-lab/llm-japanese-dataset
+  - ronantakizawa/Finance-Instruct-500k-Japanese
+  - nvidia/OpenCodeReasoning-2
+---
+
+# GOBA-OLMoE-Expert-Tuned: Domain-Specialized MoE via Expert Tuning
+
+**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](https://huggingface.co/allenai/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](https://huggingface.co/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:**
+1. Compute importance scores for all experts across layers
+2. Select bottom-k experts as candidates for replacement
+3. Train domain-specific experts using cross-expert knowledge distillation with domain data
+4. 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](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) | 50,000 | CC BY-SA 4.0 |
+| **Finance** | [ronantakizawa/Finance-Instruct-500k-Japanese](https://huggingface.co/datasets/ronantakizawa/Finance-Instruct-500k-Japanese) + [y2lan/japan-law](https://huggingface.co/datasets/y2lan/japan-law) | ~250,000 | Apache 2.0 / Public Domain |
+| **Code** | [nvidia/OpenCodeReasoning-2](https://huggingface.co/datasets/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
+
+```bash
+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}
+}
+```
+
+## Related Models
+
+- [PrunedHub-GPT-OSS-20B-28x](https://huggingface.co/goba-ai-labs/PrunedHub-GPT-OSS-20B-28x) — Lossless expert pruning for GPT-OSS-20B
+- [PrunedHub-Qwen3-30B-A3B-EN-MxMoE](https://huggingface.co/goba-ai-labs/PrunedHub-Qwen3-30B-A3B-EN-MxMoE) — Mixed-quantization MoE pruning
+- [PrunedHub-Qwen3-30B-A3B-JA-MxMoE](https://huggingface.co/goba-ai-labs/PrunedHub-Qwen3-30B-A3B-JA-MxMoE) — Language-aware MoE pruning
+
+---
+
+*Built by [GOBA AI Labs](https://goba-ai-labs.github.io) — Making large MoE models practical on consumer hardware.*