---
license: apache-2.0
task_categories:
  - text-generation
  - question-answering
  - conversational
language:
  - id
  - en
tags:
  - pretraining
  - continual-pretraining
  - sft
  - safety-alignment
  - code
  - mathematics
  - powershell
  - mesosfer-bear
pretty_name: Mesosfer Bear AI Multi-Stage Foundation Dataset
size_categories:
  - 10B<n<100B
---

# 🐻 Mesosfer Bear AI - Multi-Stage Foundation Corpus

This repository contains the curated, structured-shuffled (`seed=42`), and 95% Train / 5% Validation split corpus used to pretrain and align **Mesosfer Bear AI** (a 16-layer transformer LLM optimized for high-efficiency training on AMD Instinct MI300X).

## 📊 Dataset Structure & Stage Breakdown

### Stage 1: Pre-training (Foundational Language & General Knowledge)
| Domain | Proposer / Source Repo | Weight | Target Format | Focus & Scope |
| :--- | :--- | :--- | :--- | :--- |
| `bahasa_umum_id` | `wikimedia/wikipedia` | **25%** | Parquet / JSONL | Indonesian Wikipedia full encyclopedic raw articles |
| `bahasa_umum_en` | `karpathy/climbmix-400b-shuffle` | **25%** | Parquet / JSONL | High-quality curated pre-training corpus by NVIDIA & Andrej Karpathy |
| `code_python` | `codeparrot/codeparrot-clean-valid` | **3%** | Parquet / JSONL | Raw Python repository source files from GitHub |
| `code_typescript` | `petrpan26/typescript-code` | **3%** | Parquet / JSONL | Raw TypeScript (.ts, .tsx) codebase source files |
| `code_javascript` | `code_search_net` | **3%** | Parquet / JSONL | JavaScript (.js) source files and modules |
| `code_php` | `code_search_net` | **3%** | Parquet / JSONL | PHP (.php) backend and web application code |
| `code_cpp` | `AlgorithmicResearchGroup/arxiv_cplusplus_research_code` | **3%** | Parquet / JSONL | C++ (.cpp, .hpp) algorithms, scientific libraries, and system code |
| `code_c` | `kye/all-torvalds-c-code-1` | **2%** | Parquet / JSONL | C (.c, .h) systems programming and kernel algorithms |
| `code_csharp` | `microsoft/LCC_csharp` | **2%** | Parquet / JSONL | C# (.cs) enterprise and application codebases |
| `matematika` | `open-web-math/open-web-math` | **20%** | Parquet / JSONL | Mathematical documents, LaTeX proofs, equations, and reasoning |
| `terminal` | `SaeedRahmani/codeparrot_github_code_powershell` | **10%** | Parquet / JSONL | PowerShell scripts, system administration commands, and CLI automation |

### Stage 2: Continued Pre-Training (Domain Specialization)
| Domain | Proposer / Source Repo | Weight | Target Format | Focus & Scope |
| :--- | :--- | :--- | :--- | :--- |
| `bahasa_umum` | `ccdv/arxiv-classification` | **10%** | Parquet / JSONL | High-density scientific and technical literature |
| `code_multilang` | `code_search_net` | **40%** | Parquet / JSONL | Multi-language deep source code (Python, JS, PHP, Go, Java, Ruby) |
| `matematika` | `open-web-math/open-web-math` | **30%** | Parquet / JSONL | Mathematical proofs, formal expressions, and symbolic logic |
| `terminal` | `SaeedRahmani/codeparrot_github_code_powershell` | **20%** | Parquet / JSONL | Advanced systems automation, scripting, and shell operations |

### Stage 3: Supervised Fine-Tuning (Instruction & Dialogue)
| Domain | Proposer / Source Repo | Weight | Target Format | Focus & Scope |
| :--- | :--- | :--- | :--- | :--- |
| `percakapan_id` | `FreedomIntelligence/alpaca-gpt4-indonesian` | **40%** | Parquet / JSONL | Indonesian natural conversations and instruction responses |
| `percakapan_en` | `HuggingFaceH4/ultrachat_200k` | **40%** | Parquet / JSONL | Multi-turn conversational dialogue and informative interactions |
| `instruksi` | `garage-bAInd/Open-Platypus` | **10%** | Parquet / JSONL | STEM, logic, and chain-of-thought instruction-following tasks |
| `tooling_calls` | `glaiveai/glaive-function-calling-v2` | **10%** | Parquet / JSONL | Agent tool invocation, JSON arguments, and execution schemas |

### Stage 4: Safety & Guardrails (Harm & Crime Refusal)
| Domain | Proposer / Source Repo | Weight | Target Format | Focus & Scope |
| :--- | :--- | :--- | :--- | :--- |
| `safety_pku` | `PKU-Alignment/PKU-SafeRLHF` | **50%** | Parquet / JSONL | Refusal of illegal acts, cyberattacks, weapons, privacy violations, and harm |
| `safety_anthropic` | `Anthropic/hh-rlhf` | **30%** | Parquet / JSONL | Red-teaming dialogues and harmless response alignment |
| `safety_jailbreak` | `walledai/JailbreakHub` | **20%** | Parquet / JSONL | Adversarial prompts, DAN jailbreak defenses, and robust refusals |

## 🛠️ Data Preprocessing & Splitting Protocol
- **Structured Deterministic Shuffling**: Every individual shard is shuffled with `seed=42` using zero-copy PyArrow array indexing to prevent gradient correlation and eliminate loss spikes.
- **95% Train / 5% Validation Split**: Every single domain shard is partitioned into `train/` and `val/` directories.
- **Sequence Length Target**: Built for `max_seq_len = 4096` tokens with `<|endoftext|>` sequence packing.

## 🚀 Quickstart: Loading Data in Python
```python
from datasets import load_dataset

# Load Pretrain Bahasa Indonesia Train Split
dataset = load_dataset('Dummy9898/bear-dataset', data_dir='pretrain/bahasa_umum_id/train')
print(dataset)
```

## 📜 Licensing & Attribution
All constituent datasets belong to their respective original authors and maintainers. Distributed under Apache-2.0 in compliance with upstream open-source licenses.