Update README.md

README.md

@@ -1,3 +1,180 @@
 ---
-license: mit
+language:
+  - code
+license: apache-2.0
+task_categories:
+  - text-generation
+tags:
+  - code
+  - coding
+  - synthetic
+  - instruction-tuning
+  - sharegpt
+  - alpaca
+  - multi-language
+  - 2b-model
+  - fine-tuning
+size_categories:
+  - 1M<n<10M
 ---
+
+# Synthetic Coding Dataset v1
+
+A large-scale synthetic coding dataset designed for training and fine-tuning ~2B parameter language models. Contains **~1.5M instruction-response pairs** spanning **22 programming languages** and **10 task categories**, formatted in the ShareGPT/Alpaca hybrid conversation schema.
+
+## Dataset Summary
+
+| Property | Value |
+|---|---|
+| **Total Entries** | ~1,500,000 |
+| **Total Size** | ~4.48 GB |
+| **Format** | JSONL (63 part files) |
+| **Schema** | ShareGPT/Alpaca hybrid (conversations array) |
+| **Languages** | 22 programming languages |
+| **Task Categories** | 10 types |
+| **Difficulty Levels** | 4 (beginner to expert) |
+| **Generated** | 2026-06-22 |
+
+## Supported Languages
+
+Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#, Ruby, PHP, Kotlin, Swift, Scala, C, Lua, Julia, Elixir, Haskell, OCaml, Dart, R, Bash
+
+## Task Categories
+
+| Category | Description |
+|---|---|
+| `code_generation` | Write functions, classes, and complete programs |
+| `debugging` | Find and fix bugs in existing code |
+| `explanation` | Explain programming concepts and code behavior |
+| `refactoring` | Improve code quality, readability, and structure |
+| `algorithm` | Implement classic and advanced algorithms |
+| `system_design` | Design scalable systems and architectures |
+| `code_review` | Review and critique code for issues |
+| `best_practices` | Language-specific idioms and best practices |
+| `design_pattern` | Explain and implement design patterns |
+| `data_structure` | Implement and manipulate data structures |
+
+## Difficulty Levels
+
+- **beginner** — Basic syntax, simple loops, conditionals, and introductory concepts
+- **intermediate** — Standard design patterns, common algorithms, and typical development tasks
+- **advanced** — Complex algorithms, performance optimization, and non-trivial problem solving
+- **expert** — System design, architecture decisions, and large-scale engineering challenges
+
+## Data Format
+
+Each line in the JSONL files is a JSON object with the following structure:
+
+```json
+{
+  "id": "unique_identifier",
+  "source": "synthetic_coding_dataset_v1",
+  "category": "code_generation",
+  "language": "Python",
+  "difficulty": "intermediate",
+  "conversations": [
+    {"from": "human", "value": "Write a function that..."},
+    {"from": "gpt", "value": "Here is the implementation..."}
+  ],
+  "metadata": {
+    "task_type": "code_generation",
+    "has_code": true,
+    "tokens_approx": 1234
+  }
+}
+```
+
+### Field Descriptions
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | `string` | Unique identifier for the entry |
+| `source` | `string` | Dataset source identifier (always `synthetic_coding_dataset_v1`) |
+| `category` | `string` | One of the 10 task categories listed above |
+| `language` | `string` | Programming language for the task |
+| `difficulty` | `string` | One of: `beginner`, `intermediate`, `advanced`, `expert` |
+| `conversations` | `array` | Array of message objects with `from` (`human`/`gpt`) and `value` fields |
+| `metadata.task_type` | `string` | Mirrors the `category` field |
+| `metadata.has_code` | `boolean` | Whether the response contains code blocks |
+| `metadata.tokens_approx` | `integer` | Approximate token count for the entry |
+
+## Dataset Structure
+
+The dataset is distributed as 63 JSONL part files:
+
+```
+data/
+├── coding_dataset_part_001.jsonl
+├── coding_dataset_part_002.jsonl
+├── ...
+└── coding_dataset_part_063.jsonl
+```
+
+> **Note:** Two part files (043 and 050) are empty (0 bytes) and can be safely ignored.
+
+## Usage
+
+### Loading with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("your-username/synthetic_coding_dataset_v1", split="train")
+print(dataset[0])
+```
+
+### Loading Manually
+
+```python
+import json
+
+entries = []
+for i in range(1, 64):
+    filepath = f"data/coding_dataset_part_{i:03d}.jsonl"
+    try:
+        with open(filepath, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if line:
+                    entries.append(json.loads(line))
+    except FileNotFoundError:
+        continue
+
+print(f"Loaded {len(entries)} entries")
+```
+
+### Filtering by Language or Category
+
+```python
+# Filter Python entries
+python_entries = [e for e in dataset if e["language"] == "Python"]
+
+# Filter debugging tasks at advanced level
+debugging_advanced = [
+    e for e in dataset
+    if e["category"] == "debugging" and e["difficulty"] == "advanced"
+]
+```
+
+## Intended Use
+
+This dataset is designed for:
+
+- **Instruction tuning** of code-generation language models in the ~2B parameter range
+- **Fine-tuning** existing base models for coding tasks
+- **Research** on multi-language code understanding and generation
+- **Benchmarking** code model performance across languages and difficulty levels
+
+## Limitations
+
+- This is a **synthetically generated** dataset. While it covers a broad range of coding tasks, it may not fully represent the complexity and nuance of real-world developer interactions or production codebases.
+- The responses are generated by an AI model and may occasionally contain suboptimal solutions, outdated APIs, or minor inaccuracies.
+- The dataset has not been manually verified at scale; users are encouraged to perform their own quality filtering based on their specific requirements.
+
+## License
+
+This dataset is released under the **MIT** license.
+
+## Acknowledgements
+
+Generated using large language models for synthetic data creation. Designed to support open-source code model training and research.