datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
minicpm5-stock-v2-forward-return
MiniCPM5 Stock v2 — Forward-Return Labels
Binary BUY/SELL stock-direction dataset where labels come from actual forward
5-day returns (BUY > +2%, SELL < -2%, middle band dropped), not news sentiment.
All features are strictly causal (no look-ahead): last 20 daily returns, RSI(14),
volume ratio vs 20d MA, 20d volatility, 5d/20d momentum, 20d relative strength vs SPY.
train_minicpm5_v2.jsonl — 5,056 rows, 16 tickers, class-balanced
val_minicpm5_v2.jsonl — 1,586 rows, 4 held-out… See the full description on the dataset page: https://huggingface.co/datasets/ewin-reg/minicpm5-stock-v2-forward-return.minicpm5-2b-damage-labels
MiniCPM5-2B Damage Labels (MERNIK teacher)
Per-group measured quantization damage for MiniCPM5-2B (dense 2.6B, 42 layers).
What
damage_minicpm5_2b.jsonl — 169 rows: 1 BASELINE + 168 tied-group units.
Each unit row: the group dropped Q5_K → Q3_K while everything else stays at
Q5_K, scored by wikitext-2 PPL (-c 1024 -n 64 --seed 7).
{"unit": "ffn_down@7", "tensors": ["blk.7.ffn_down.weight"],
"ppl": 13.5364, "damage": 0.1732}
ssim_minicpm.npz — measured structural… See the full description on the dataset page: https://huggingface.co/datasets/wepiqx/minicpm5-2b-damage-labels.minicpm5-stage1-dataminicpm5-1b-quantization-benchmark
openbmb/MiniCPM5-1B 次世代量子化（Quanto FP8 / INT4 vs BNB 4bit）実測ベンチマークレポート
対象モデル: openbmb/MiniCPM5-1B (1.16B parameters, 128k context, LlamaForCausalLM)
検証ハードウェア: NVIDIA GeForce RTX 4070 Ti (12GB GDDR6X, Ada Lovelace, Compute Capability 8.9, 第4世代Tensor Core)
実行環境: Windows / Python 3.13 / PyTorch 2.6.0+cu124 / transformers 4.57.6 / optimum-quanto 0.2.7 / bitsandbytes 0.50.0
検証日: 2026-09-19 12:12:34
1. エグゼクティブサマリー（全体比較）
NVIDIA GeForce RTX 4070 Ti 実機環境において、標準ネイティブ… See the full description on the dataset page: https://huggingface.co/datasets/aoiandroid/minicpm5-1b-quantization-benchmark.minicpm5-vivamais-text-sft-v1
MiniCPM5 Viva Mais Text SFT v1
This dataset is the exact JSONL training/evaluation package used for the
MiniCPM5 Viva Mais text QA candidate v1 run.
Files
minicpm5_text_sft.jsonl: 12,000 SFT rows.
vivamais_qa_eval.jsonl: 32 fixed Viva Mais dashboard QA eval rows.
Training Mix
The SFT mix was generated by the Viva Mais repository pipeline from the Modal
volume minicpm5-vivamais-text-data:
2,400 rows from Polygl0t/gigaverbo-v2-sft
5,400 Viva Mais… See the full description on the dataset page: https://huggingface.co/datasets/marinarosa/minicpm5-vivamais-text-sft-v1.minicpm5-vivamais-text-sft-v4
MiniCPM5 Viva Mais text SFT v4
This dataset contains the redacted training and evaluation artifacts used for
marinarosa/minicpm5-1b-vivamais-v4. It was built for Viva Mais, a local-first Portuguese WhatsApp
travel-agency copilot that answers grounded questions from an extracted CRM
context.
Files
data/train.jsonl: 4000 chat-format SFT rows.
data/eval/vivamais_qa_eval.jsonl: 158 dashboard QA eval
rows.
data/teacher/rio31_teacher_distill.jsonl: 80 accepted
rows… See the full description on the dataset page: https://huggingface.co/datasets/marinarosa/minicpm5-vivamais-text-sft-v4.minicpm5-tool-calling-xmlminicpm5-computer-browser-coding-v2minicpm5-vivamais-text-sft-v3
MiniCPM5 Viva Mais text SFT v3
This dataset package contains the exact redacted JSONL artifacts produced by
the Viva Mais MiniCPM5 text v3 Rio-distillation candidate run.
The v3 model candidate was not published because the eval gate caught more
cross-client leakage than the published v1 model. The dataset is published for
auditability and for future ablations, not as an endorsement of the v3 model.
Files
data/train.jsonl: 3500 SFT rows.… See the full description on the dataset page: https://huggingface.co/datasets/marinarosa/minicpm5-vivamais-text-sft-v3.openbmb__MiniCPM-S-1B-sft-llama-format-details
Dataset Card for Evaluation run of openbmb/MiniCPM-S-1B-sft-llama-format
Dataset automatically created during the evaluation run of model openbmb/MiniCPM-S-1B-sft-llama-format
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/openbmb__MiniCPM-S-1B-sft-llama-format-details.