ministral
ministral-14b-eval-logs-and-scoresswift-reasoning-rollouts-deepscaler-ministral8b
DeepScaleR Reasoning Rollouts (Ministral-8B)
This dataset contains reasoning rollouts used to train the SWIFT reward head.
Paper page: https://huggingface.co/papers/2505.12225
GitHub: https://github.com/aster2024/SWIFT/
Generator model: mistralai/Ministral-8B-Instruct-2410 (https://huggingface.co/mistralai/Ministral-8B-Instruct-2410)
Dataset Description
This dataset contains 10000 samples corresponding to the Generalization Test setup.
Source: DeepScaleR.
Generator:… See the full description on the dataset page: https://huggingface.co/datasets/Aster2024/swift-reasoning-rollouts-deepscaler-ministral8b.ministral-3-3b-base-atlas
Ministral-3-3B Base Brain Atlas
This is an internal-mechanics atlas for the 3B parameter Ministral base model. The goal was not to benchmark end-task accuracy, but to map what the network is actually doing with its parameters: where it computes, where it stores behaviorally relevant structure, and which directions carry task-critical information.
What was run
Activation census over 8,965 prompts spanning core technical, ML/AI, research, writing, creative writing… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/ministral-3-3b-base-atlas.RAIF-ComplexInstruction-MinistralThis dataset belongs to the official implementation of the paper "Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models".
Github Repository
Existing large language models (LLMs) face challenges of following complex instructions, especially when multiple constraints are present and organized in paralleling, chaining, and branching structures. One intuitive solution, namely chain-of-thought (CoT), is expected to universally improve capabilities of LLMs. However, we… See the full description on the dataset page: https://huggingface.co/datasets/yolay/RAIF-ComplexInstruction-Ministral.ministral-3-benchmark-prompts
Ministral 3 MLX benchmark prompts
This tiny dataset contains the four fixed prompts used by the reproducible
smoke benchmark for the Ministral 3 MLX 4-bit model.
It is a benchmark fixture, not a training or fine-tuning dataset.
Schema
Each JSONL row contains:
id: stable case identifier;
language: prompt language;
prompt: exact input sent to the model;
expected_keywords: lowercase substrings used by the smoke check.
The benchmark uses greedy decoding and checks… See the full description on the dataset page: https://huggingface.co/datasets/vinci00/ministral-3-benchmark-prompts.JOSIE-DPO-Chosen-Ministral
JOSIE-DPO-Chosen — Ministral
Half of a DPO dataset. The chosen responses are here. You generate the rejected ones — and that's the point.
Overview
This dataset contains the chosen-only side of a preference dataset designed to align any LLM with the personality, tone, and response style of J.O.S.I.E. (Just One Super Intelligent Entity) — the viral model family created by Gökdeniz Gülmez.
The chosen responses were generated by a fine-tuned Ministral-14B model… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/JOSIE-DPO-Chosen-Ministral.