datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mlx-model-explorer-data
MLX Model Explorer Data
An anonymous record of how people use MLX Model Explorer to choose an MLX model for their Mac: which model families, sizes, quantizations, memory classes and context lengths they look at, and which models they go on to open, compare or download. It also holds the community reports ("it worked", "too slow") and real MLX benchmark results that people choose to contribute.
The goal is to answer, with data: what is the MLX community actually trying to run… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/mlx-model-explorer-data.optiq-lab-traces
OptiQ Lab Traces
Research and tool-calling sessions produced by OptiQ Lab, the local web UI that ships with mlx-optiq. Each session is a complete run: a deep-research report built from live web sources, or a multi-turn agent loop driving the Lab's own sandboxed tools.
The dataset is 866 sessions in HuggingFace Session-Traces format (the agent-traces viewer). Each .jsonl file is one session: a header line carrying the run's metadata, then one message per turn.
The two… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/optiq-lab-traces.optiq-code-traces
OptiQ Code Traces
Gold-verified agentic software-engineering trajectories, produced by OptiQ Code, the terminal coding agent for local models on a Mac. Each trajectory is a full tool-calling run against a real repository bug, and every resolved label is set by executing the gold tests (FAIL_TO_PASS + PASS_TO_PASS) after applying the model's patch, never by the agent's own self-report.
The dataset is 1,789 agent sessions in HuggingFace Session-Traces format (the agent-traces… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/optiq-code-traces.wikisql
mlx-community/wikisql
This dataset is processed from salesforce/WikiSQL and is used to demonstrate the fine-tuning function of LLM in the mlx_lm library.
source file form: mlx-examples
dots.mocr-mlx-evals
dots.mocr-mlx-evals
olmOCR-bench evals of dots.mocr MLX quants.
There is directory per model with the md files extracted from the original PDFs.
Directory logs contains the output from the harness run.
mlx-benchmarks
MLX Benchmarks
Structured benchmark results for MLX-quantized and other locally-hosted
LLMs on Apple Silicon. Covers throughput, time-to-first-token, tool-calling,
code generation, reasoning, knowledge, and math suites.
Results are produced by a sweep harness that wires upstream evaluation tools
against a local vllm-mlx inference server:
EleutherAI/lm-evaluation-harness — coding, reasoning, knowledge, math
linusvwe/MLXBench — throughput and time-to-first-token
vllm… See the full description on the dataset page: https://huggingface.co/datasets/JacobPEvans/mlx-benchmarks.magenta-realtime-mlx-cpp
Magenta RealTime — C++ MLX runtime bundle
This dataset is a re-packaging of
Google's Magenta RealTime weights
for the C++ MLX runtime in
rhymeswithlion/magenta-realtime-mlx-cpp.
It contains exactly what mlx-stream needs at startup; nothing more, nothing
less. The upstream .pt / .npy checkpoints are intentionally not
mirrored here — they're only useful for the (Python) re-export tooling on the
project's main distribution.
Contents
.
├──… See the full description on the dataset page: https://huggingface.co/datasets/rhymeswithlion/magenta-realtime-mlx-cpp.seeclick-web-commercial-mlx
SeeClick Web Commercial Dataset (MLX-VLM Format)
Commercial-use friendly GUI grounding dataset from SeeClick Web data.
Apache 2.0 licensed - safe for commercial applications.
Dataset Description
This dataset contains ~20k examples for training Vision-Language Models to predict
click coordinates given a screenshot and instruction. Derived from SeeClick Web
crawled data (Apache 2.0).
Key Features
License: Apache 2.0 (commercial use allowed)
Format: MLX-VLM… See the full description on the dataset page: https://huggingface.co/datasets/pierretokns/seeclick-web-commercial-mlx.mlx-omni-lora-stt-tts-demoWill be used in the development of the trainer backend of mlx-omni by Neywa Labs.
Apertus-v1.5-QAT-10K
mlx-community/Apertus-v1.5-QAT-10K
This is a 2000 sample subset of the chosen pairs inside swiss-ai/Apertus_v1p5_Preference_Data for MLX-LM-LoRA and MLX-LoRA-Studio and the Quantization Aware Trained Appertus models.
hermes-reasoning-tool-useJOSIE-v2-Instruct-5K
JOSIE v2 Instruct 5K
A high-quality instruction-following dataset featuring J.O.S.I.E. (Just One Super Intelligent Entity) - an advanced AI assistant with a distinctive personality combining intellectual rigor, dry wit, and genuine helpfulness.
Dataset Overview
Size: 5,000 conversational samples
Format: JSONL (JSON Lines)
Source Model: GPT-5.4-nano via OpenAI Batch API
Use Case: Finetuning language models on Apple Silicon using mlx-lm or mlx-lm-lora
License: MIT… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/JOSIE-v2-Instruct-5K.qwen3_dwq_calibration_1332_235b
Qwen3 DWQ Calibration Dataset (235B, 1332 samples)
This dataset contains 1,332 samples for calibrating dynamic weight quantization (DWQ) of Qwen3-235B models. It is created following the methodology of mlx-community/qwen3_dwq_calibration_1332 but using the larger Qwen3-235B model.
Dataset Description
This dataset is derived from allenai/tulu-3-sft-mixture and consists of:
610 samples processed through Qwen3-235B with explicit reasoning
722 original samples from the… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/qwen3_dwq_calibration_1332_235b.fineweb-edu-1m
train splitt = 1M
validation nsplitt = 1024
traces.claude-code.mlx-lm-granitemoehybridopenhermes-2.5-mlxLiterally just https://huggingface.co/datasets/teknium/OpenHermes-2.5 but converted to be usable in MLX lora training (assumes ChatML format)
mlx7-two-tower-data
mlx7-two-tower-data
This repository contains datasets used for training Two-Tower (Dual Encoder) models for document retrieval.
Dataset Description
The datasets provided here are structured for training dual encoder models with various sampling strategies:
classic_triplets: 48.2 MB
intra_query_neg: 47.6 MB
multi_pos_multi_neg: 126.5 MB
Dataset Details
classic_triplets.parquet: Standard triplet format with (query, positive_document, negative_document)… See the full description on the dataset page: https://huggingface.co/datasets/Azuremis/mlx7-two-tower-data.Dolci-Instruct-SFT-No-Tools-100KFor MLX-LM and MLX-LM-LoRA.
pip install -U mlx-lm-lora
Example with MLX-LM-LoRA:
mlx_lm_lora.train \
--model mlx-community/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1 \
--train \
--data mlx-community/Dolci-Instruct-SFT-No-Tools-100K \
--epoch 2 \
--max-seq-length 4096
Example with MLX-LM:
mlx_lm.lora \
--model mlx-community/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1 \
--train \
--data mlx-community/Dolci-Instruct-SFT-No-Tools-100K \
--iters 100 \
--max-seq-length… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/Dolci-Instruct-SFT-No-Tools-100K.fineweb-edu-100KCode used to create this:
import json
from itertools import islice
from datasets import load_dataset
from tqdm import tqdm
DATASET_NAME = "HuggingFaceFW/fineweb-edu"
DATASET_CONFIG = "sample-10BT"
NUM_SAMPLES = 100_000
BATCH_SIZE = 5_000
OUTPUT_PATH = "pretrain_100k.jsonl"
dataset = load_dataset(
DATASET_NAME,
DATASET_CONFIG,
split="train",
streaming=True,
)
dataset_iter = iter(dataset)
written = 0
with open(OUTPUT_PATH, "w", encoding="utf-8") as f:
progress = tqdm(… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/fineweb-edu-100K.Intermediate-Thinking-130k
Intermediate-Thinking-130k
A comprehensive dataset of 135,000 high-quality samples designed to advance language model reasoning capabilities through structured intermediate thinking processes. This dataset enables training and evaluation of models with sophisticated self-correction and iterative reasoning abilities across 42 languages.
OG Link
Overview
Intermediate-Thinking-130k addresses a fundamental limitation in current language models: their inability to pause… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/Intermediate-Thinking-130k.medical-dataset-test-mlxflatgsm8kOpenAI's GSM8K dataset converted to be compatibel with MLX-LM-LoRA.
example uasge:
pip install -U mlx-lm-lora
python -m mlx_lm_lora.train \
--model mlx-community/Josiefied-Qwen3-0.6B-abliterated-v1-4bit \
--train \
--train-mode grpo \
--data mlx-community/gsm8k \
--iters 100 \
--steps-per-report 1 \
--batch-size 1 \
--max-completion-length 512
dolma3_mix-common_crawl-art_and_design-160kThe 160K subset of AllenAI's common_crawl-art_and_design Pretraining dataset split into train a valid saamples.
Train set size: 159436
Valid set size: 160
Direct usage in MLX-LM-LoRA:
python -m mlx_lm.lora \
--train \
--model Qwen/Qwen3-0.6B-Base \
--data mlx-community/dolma3_mix-common_crawl-art_and_design-160k \
--num-layers 4 \
--iters 1000 \
--batch-size 1 \
--steps-per-report 50 \
--max-seq-length 1028 \
--adapter-path path/to/adapter
Direct usage in MLX-LM:
python -m mlx_lm.lora \… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/dolma3_mix-common_crawl-art_and_design-160k.MLX_Week2orpo-dpo-mix-40k-flat-mlx
Dataset Description
This dataset is a split version of orpo-dpo-mix-40k-flat for direct use with mlx-lm-lora, specifically tailored to be compatible with DPO and CPO training.
The dataset has been divided into three parts:
Train Set: 90%
Validation Set: 6%
Test Set: 4%
Example Usage
To train a model using this dataset, you can use the following command:
mlx_lm_lora.train \
--model Qwen/Qwen2.5-3B-Instruct \
--train \
--test \
--num-layers 8 \
--data… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/orpo-dpo-mix-40k-flat-mlx.Hermes-Reasoning-Tool-Use-MLXsimple-grpoMLX-Benchmark-V2
MLX Benchmark Dataset
Dataset Summary
The MLX Benchmark Dataset is a curated evaluation benchmark consisting of 520 questions designed to measure large language model (LLM) proficiency in Apple's MLX machine learning framework. MLX is an array framework for machine learning on Apple Silicon that leverages unified memory architecture, and this dataset is the first comprehensive benchmark specifically targeting MLX knowledge and coding ability.
The dataset covers the… See the full description on the dataset page: https://huggingface.co/datasets/Goekdeniz-Guelmez/MLX-Benchmark-V2.zer0-books
scraped_at_utc: 2026-03-24T12:45:50.800626+00:00,
source: https://www.collectiveinkbooks.com/zer0-books/our-books/all-books/&s=0,
listing_total_books: 383,
listing_total_pages: 16,
scraped_books: 383,
books_with_reviews: 341,
books_without_reviews: 42.
This dataset contains the list of all the books that have been, as of 17.04.26, published by a British independent philosophical publisher zer0 books founded by Mark Fisher. No copyright infringement had been made, and all the content is… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/zer0-books.