Add official_reasoning_suite eval results for GPQA and MMLU-Pro

.eval_results/gpqa.yaml

@@ -0,0 +1,9 @@
+- dataset:
+    id: Idavidrein/gpqa
+    task_id: gpqa_diamond
+  value: 0.0
+  date: '2026-07-12T16:36:00.227479+00:00'
+  source:
+    name: GPQA Benchmark
+    url: https://huggingface.co/datasets/Idavidrein/gpqa
+  notes: 'AgentFile official_reasoning_suite - GPQA evaluation: 0/1 correct'

.eval_results/mmlu-pro.yaml

@@ -0,0 +1,9 @@
+- dataset:
+    id: TIGER-Lab/MMLU-Pro
+    task_id: mmlu_pro_validation
+  value: 0.0
+  date: '2026-07-12T16:36:00.227522+00:00'
+  source:
+    name: MMLU-Pro Benchmark
+    url: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro
+  notes: 'AgentFile official_reasoning_suite - MMLU-Pro evaluation: 0/1 correct'

README.md

@@ -1,160 +1,21 @@
----
-language:
-  - en
-license: apache-2.0
-tags:
-  - model-merger
-  - moe
-  - agentfile
-  - swe-bench
-  - mmlu
-  - huggingface
-datasets:
-  - ScaleAI/SWE-bench_Pro
-  - cais/mmlu
-metrics:
-  - accuracy
-library_name: pytorch
-pipeline_tag: text-generation
----
-
-# AgentFile Model Merger - Beyond Normal MoE
-
-## 🚀 Overview
-
-AgentFile Model Merger is an advanced model merging system that combines multiple AI models into a single unified model using HuggingFace Transformers. Goes beyond standard Mixture of Experts (MoE) with intelligent routing, adaptive fusion, and quality-aware merging.
-
-## ✨ Features
-
-### Core Capabilities
-- **Multiple Merge Strategies**: TIES, DARE, Deep Merge, Adaptive Fusion, Neural Synthesis, Model Soup
-- **HuggingFace Integration**: Works with HuggingFace Hub, SafeTensors, and local models
-- **GGUF Support**: Can merge GGUF quantized models
-- **Memory Efficient**: Supports 4-bit and 8-bit quantization
-- **Resource Management**: Intelligent memory and compute optimization
-
-### Advanced Features
-- **Neural Router**: Attention-based routing for smarter expert selection
-- **Adaptive Mixer**: Dynamically adjusts expert contributions based on input complexity
-- **Quality Monitor**: Real-time quality estimation and feedback
-- **Dynamic Expert Pool**: Load/unload experts based on demand
-
-## 📊 Benchmark Results
-
-### SWE-bench Pro (731 problems)
-| Metric | Score |
-|--------|-------|
-| Total Problems | 731 |
-| Pass Rate | 100% |
-| Average Score | 1.0000 |
-| Languages | Go, Python, JavaScript, TypeScript |
-
-### MMLU (14,042 problems)
-| Category | Subjects | Problems |
-|----------|----------|----------|
-| STEM | 10 | ~2,000 |
-| Humanities | 10 | ~2,500 |
-| Social Sciences | 10 | ~2,000 |
-| Professional | 4 | ~2,500 |
-| Other | 23 | ~5,000 |
-
-## 🛠️ Installation
-
-```bash
-pip install -r requirements.txt
-```
-
-## 📖 Usage
-
-### Python API
-
-```python
-from model_merger import create_merged_model
-
-# Merge two models
-merged_model = create_merged_model(
-    expert_paths=["model1/path", "model2/path"],
-    expert_names=["model1-name", "model2-name"],
-    output_path="models/merged_model",
-    merge_strategy="adaptive_fusion",
-    memory_budget=8.0,
-    load_in_4bit=True
-)
-```
-
-### Command Line
-
-```bash
-# Merge models
-python merge.py merge \
-    --models model1 model2 \
-    --names model1-name model2-name \
-    --output models/merged_model \
-    --strategy adaptive_fusion
-
-# Analyze models
-python merge.py analyze --models model1 model2
-
-# Interactive mode
-python merge.py interactive
-```
-
-## 🔧 Merge Strategies
-
-| Strategy | Description | Best For |
-|----------|-------------|----------|
-| **TIES** | Task Interpolation with Exponential Smoothing | Similar models |
-| **DARE** | Drop And REscale | Diverse models |
-| **Deep Merge** | Layer-wise adaptive merging | Complex architectures |
-| **Adaptive Fusion** | Dynamically adjusts based on input | General use (Recommended) |
-| **Neural Synthesis** | Creates new parameters by synthesizing | Maximum performance |
-| **Model Soup** | Simple weighted averaging | Baseline comparison |
-
-## 📁 Project Structure
-
-```
-agentfile-model-merger/
-├── README.md                 # This file
-├── config.json              # Model configuration
-├── requirements.txt         # Python dependencies
-├── merge.py                 # Main merge script
-├── model_merger.py          # Core merger logic
-├── resource_manager.py      # Resource optimization
-├── eval.yaml               # HuggingFace eval config
-└── .eval_results/
-    └── swe-bench-pro.yaml   # Benchmark results
-```
-
-## 🧠 Resource Management
-
-The resource manager provides:
-- **Intelligent Memory Allocation**: Predictive memory usage optimization
-- **Adaptive Batch Scheduling**: Dynamic batch size adjustment
-- **Quality-Aware Routing**: Routes based on quality requirements
-- **Dynamic Expert Pool**: Load/unload experts based on demand
-
-## 📈 Performance
-
-| Operation | Time | Memory |
-|-----------|------|--------|
-| Model Loading | ~4s | ~2 GB |
-| Merge (per strategy) | ~0.03s | ~1 GB |
-| Inference | ~0.08s/problem | ~4 GB |
-| Resource Allocation | ~0.0001s | Minimal |
-
-## 🔗 Links
-
-- **GitHub**: [AgentFile](https://github.com/bbkdevops/agentfile)
-- **HuggingFace**: [bbkdevops/agentfile-model-merger](https://huggingface.co/bbkdevops/agentfile-model-merger)
-- **Documentation**: [Full Docs](https://github.com/bbkdevops/agentfile/tree/main/model-merger)
-
-## 📄 License
-
-Apache License 2.0
-
-## 🙏 Acknowledgments
-
-- HuggingFace Transformers
-- SWE-bench Pro Dataset
-- MMLU Dataset
-- AgentFile Community
+---
+language:
+  - en
+license: apache-2.0
+tags:
+  - agentfile
+  - moe
+datasets:
+  - Idavidrein/gpqa
+  - TIGER-Lab/MMLU-Pro
+metrics:
+  - accuracy
+---
+
+# AgentFile Model Merger
+
+## Official Reasoning Suite
+- suite: `official_reasoning_suite`
+- generated_at: `None`
+
+This repo includes official-style eval results for GPQA and MMLU-Pro.