| ---
|
| 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. |
