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README.md

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----
-license: bigscience-openrail-m
----
+# Amkyaw-Core-L3
+
+A high-quality 3-step reasoning dataset for training AI models on systematic thinking processes.
+
+## Dataset Description
+
+Amkyaw-Core-L3 is a reasoning-focused dataset designed to train AI models on 3-step thinking processes:
+1. Problem understanding
+2. Solution derivation  
+3. Verification
+
+## Structure
+
+```
+Amkyaw-Core-L3/
+├── data/
+│   ├── brain/
+│   │   ├── reasoning_steps.jsonl    # Main reasoning data
+│   │   ├── conversations.jsonl     # User-Model dialogues
+│   │   └── metadata.json           # Training data metadata
+│   ├── validation/
+│   │   └── validation.jsonl       # Validation data
+│   └── raw_logs/
+│       └── logs-*.jsonl            # Raw logs from Gradio Space
+├── scripts/
+│   ├── preprocess.py
+│   ├── validate_data.py
+│   ├── push_to_hub.py
+│   └── augment_reasoning.py
+└── configs/
+    ├── dataset_config.yaml
+    └── reasoning_prompts.yaml
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("amkyawdev/Amkyaw-Core-L3")
+```
+
+## License
+
+MIT License
+
+## Citation
+
+Please cite this dataset if used in research.

configs/dataset_config.yaml

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+dataset_name: Amkyaw-Core-L3
+version: "1.0.0"
+description: 3-step reasoning dataset for AI training
+
+# Data configuration
+data:
+  brain:
+    reasoning_steps: data/brain/reasoning_steps.jsonl
+    conversations: data/brain/conversations.jsonl
+    metadata: data/brain/metadata.json
+  validation:
+    path: data/validation/validation.jsonl
+
+# Processing
+processing:
+  num_reasoning_steps: 3
+  languages:
+    - en
+  tasks:
+    - reasoning
+    - math
+
+# License
+license: MIT

configs/reasoning_prompts.yaml

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+# 3-Step Reasoning Prompt Templates
+
+templates:
+  step1:
+    name: "Problem Understanding"
+    description: "Break down and understand the problem"
+    prompt: |
+      Step 1: Understand the problem
+      - Identify what is being asked
+      - Determine the operation needed
+      - Extract relevant numbers
+
+  step2:
+    name: "Solution Derivation"
+    description: "Apply the operation to find the answer"
+    prompt: |
+      Step 2: Derive the solution
+      - Apply the operation step by step
+      - Show intermediate calculations
+      - Get the final result
+
+  step3:
+    name: "Verification"
+    description: "Verify the answer is correct"
+    prompt: |
+      Step 3: Verify
+      - Check by doing the reverse operation
+      - Confirm the answer makes sense
+
+# Example prompts
+examples:
+  math_add: |
+    Let me solve this step by step:
+    Step 1: Add {a} + {b}
+    Step 2: Compute the sum
+    Step 3: Verify by subtracting
+
+  math_multiply: |
+    Let me solve this step by step:
+    Step 1: Break down the problem
+    Step 2: Multiply step by step
+    Step 3: Verify by dividing

data/brain/conversations.jsonl

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+{"user": "What is 15 + 27?", "assistant": "Let me solve this step by step:\n\nStep 1: Add 15 + 20 = 35\nStep 2: Add 35 + 7 = 42\nStep 3: Verify: 42 - 27 = 15 ✓\n\nAnswer: 42"}
+{"user": "What is 8 × 6?", "assistant": "Let me solve this step by step:\n\nStep 1: Break 6 into 2 × 3: 8 × 2 = 16\nStep 2: Multiply 16 × 3 = 48\nStep 3: Verify: 48 ÷ 6 = 8 ✓\n\nAnswer: 48"}
+{"user": "What is 100 - 37?", "assistant": "Let me solve this step by step:\n\nStep 1: Subtract 100 - 30 = 70\nStep 2: Subtract 70 - 7 = 63\nStep 3: Verify: 63 + 37 = 100 ✓\n\nAnswer: 63"}

data/brain/metadata.json

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+{
+  "dataset_name": "Amkyaw-Core-L3",
+  "version": "1.0.0",
+  "description": "3-step reasoning training data",
+  "num_samples": 3,
+  "languages": ["en"],
+  "tasks": ["reasoning", "math"],
+  "created_date": "2026-04-14",
+  "license": "MIT"
+}

data/brain/reasoning_steps.jsonl

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+{"id": "reasoning_001", "problem": "What is 15 + 27?", "steps": [{"step": 1, "description": "Add 15 + 20", "result": "35"}, {"step": 2, "description": "Add 35 + 7", "result": "42"}, {"step": 3, "description": "Verify 42 - 27 = 15", "result": "15 ✓"}], "final_answer": "42"}
+{"id": "reasoning_002", "problem": "What is 8 × 6?", "steps": [{"step": 1, "description": "Break 6 into 2 × 3", "result": "8 × 2 = 16"}, {"step": 2, "description": "Multiply 16 × 3", "result": "48"}, {"step": 3, "description": "Verify 48 ÷ 6 = 8", "result": "8 ✓"}], "final_answer": "48"}
+{"id": "reasoning_003", "problem": "What is 100 - 37?", "steps": [{"step": 1, "description": "Subtract 100 - 30", "result": "70"}, {"step": 2, "description": "Subtract 70 - 7", "result": "63"}, {"step": 3, "description": "Verify 63 + 37 = 100", "result": "100 ✓"}], "final_answer": "63"}

data/validation/validation.jsonl

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+{"id": "val_001", "problem": "What is 25 + 18?", "steps": [{"step": 1, "description": "Add 25 + 10", "result": "35"}, {"step": 2, "description": "Add 35 + 8", "result": "43"}, {"step": 3, "description": "Verify 43 - 18 = 25", "result": "25 ✓"}], "final_answer": "43"}

requirements.txt

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+datasets>=2.14.0
+huggingface_hub>=0.19.0
+pyyaml>=6.0

scripts/augment_reasoning.py

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+#!/usr/bin/env python3
+"""
+Augment reasoning data with additional examples.
+"""
+import json
+import random
+import sys
+from pathlib import Path
+
+
+def generate_examples(num_samples: int = 100):
+    """Generate synthetic reasoning examples."""
+   operators = [
+        ("+", lambda a, b: a + b, "add"),
+        ("-", lambda a, b: a - b, "subtract"),
+        ("×", lambda a, b: a * b, "multiply"),
+    ]
+    
+    examples = []
+    for i in range(num_samples):
+        op_func, op_name = random.choice(operators)
+        a = random.randint(1, 100)
+        b = random.randint(1, 20)
+        result = op_func(a, b)
+        
+        example = {
+            "id": f"reasoning_{i+100:03d}",
+            "problem": f"What is {a} {op_name} {b}?",
+            "steps": [
+                {"step": 1, "description": f"Apply {op_name}", "result": str(result)},
+                {"step": 2, "description": "Verify", "result": "✓"},
+                {"step": 3, "description": "Final answer", "result": str(result)},
+            ],
+            "final_answer": str(result),
+        }
+        examples.append(example)
+    
+    return examples
+
+
+def augment_data(input_file: str, output_file: str, num_new: int = 100):
+    """Augment existing data with new examples."""
+    input_path = Path(input_file)
+    
+    # Read existing
+    existing = []
+    if input_path.exists():
+        with open(input_path) as f:
+            for line in f:
+                existing.append(json.loads(line))
+    
+    # Generate new
+    new_examples = generate_examples(num_new)
+    
+    # Write combined
+    output_path = Path(output_file)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    
+    with open(output_path, "w") as f:
+        for item in existing + new_examples:
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
+    
+    print(f"Saved {len(existing) + len(new_examples)} examples to {output_file}")
+
+
+if __name__ == "__main__":
+    input_file = sys.argv[1] if len(sys.argv) > 1 else "data/brain/reasoning_steps.jsonl"
+    output_file = sys.argv[2] if len(sys.argv) > 2 else "data/brain/reasoning_steps.jsonl"
+    num_new = int(sys.argv[3]) if len(sys.argv) > 3 else 100
+    augment_data(input_file, output_file, num_new)

scripts/preprocess.py

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+#!/usr/bin/env python3
+"""
+Preprocess raw logs and convert to training data format.
+"""
+import json
+import sys
+from pathlib import Path
+
+
+def preprocess_logs(raw_dir: str, output_dir: str):
+    """Process raw logs and create training data."""
+    raw_path = Path(raw_dir)
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+    
+    processed = []
+    for log_file in raw_path.glob("logs-*.jsonl"):
+        with open(log_file) as f:
+            for line in f:
+                data = json.loads(line)
+                # Transform to training format
+                processed.append(data)
+    
+    # Write processed data
+    output_file = output_path / "reasoning_steps.jsonl"
+    with open(output_file, "w") as f:
+        for item in processed:
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
+    
+    print(f"Processed {len(processed)} records to {output_file}")
+    return len(processed)
+
+
+if __name__ == "__main__":
+    raw_dir = sys.argv[1] if len(sys.argv) > 1 else "data/raw_logs"
+    output_dir = sys.argv[2] if len(sys.argv) > 2 else "data/brain"
+    preprocess_logs(raw_dir, output_dir)

scripts/push_to_hub.py

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+#!/usr/bin/env python3
+"""
+Push local dataset to Hugging Face Hub.
+"""
+import sys
+from pathlib import Path
+
+
+def push_to_hub(repo_id: str, data_dir: str):
+    """Push dataset to Hugging Face Hub."""
+    import os
+    from huggingface_hub import HfApi, login
+    
+    # Get token from environment
+    token = os.environ.get("HF_TOKEN")
+    if not token:
+        print("Error: HF_TOKEN not set")
+        return
+    
+    # Login and upload
+    api = HfApi()
+    api.create_repo(repo_id=repo_id, token=token, repo_type="dataset", exist_ok=True)
+    
+    # Upload folder
+    api.upload_folder(
+        folder_path=data_dir,
+        repo_id=repo_id,
+        repo_type="dataset",
+        token=token,
+    )
+    
+    print(f"Pushed to https://huggingface.co/datasets/{repo_id}")
+
+
+if __name__ == "__main__":
+    repo_id = sys.argv[1] if len(sys.argv) > 1 else "amkyawdev/Amkyaw-Core-L3"
+    data_dir = sys.argv[2] if len(sys.argv) > 2 else "."
+    push_to_hub(repo_id, data_dir)

scripts/validate_data.py

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+#!/usr/bin/env python3
+"""
+Validate JSONL data format and integrity.
+"""
+import json
+import sys
+from pathlib import Path
+
+
+def validate_jsonl(file_path: str) -> bool:
+    """Validate a JSONL file format."""
+    path = Path(file_path)
+    if not path.exists():
+        print(f"Error: File not found: {file_path}")
+        return False
+    
+    valid = True
+    line_num = 0
+    with open(path) as f:
+        for line in f:
+            line_num += 1
+            try:
+                data = json.loads(line)
+                # Basic validation
+                if not isinstance(data, dict):
+                    print(f"Line {line_num}: Not a JSON object")
+                    valid = False
+            except json.JSONDecodeError as e:
+                print(f"Line {line_num}: Invalid JSON - {e}")
+                valid = False
+    
+    print(f"Validated {path}: {line_num} lines, valid={valid}")
+    return valid
+
+
+if __name__ == "__main__":
+    file_path = sys.argv[1] if len(sys.argv) > 1 else "data/brain/reasoning_steps.jsonl"
+    validate_jsonl(file_path)