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mlx-community/Dolci-Instruct-SFT-No-Tools-200K

published by @mlx-community

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
288
Last 30 days
36
Likes
1
Size
196.8 MB
Created on HF
2025-11-21
Age
11 months ago

Description

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-200K \

--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-200K \

--iters 100 \

--max-seq-length 4096

Tags

size_categories:100K<n<1Mformat:parquetformat:optimized-parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:us

README current version from Hugging Face


size_categories:

  • 100K<n<1M

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-200K \
--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-200K \
--iters 100 \
--max-seq-length 4096

README history 3 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-01-30Create README.mdf479901500 B
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  2. 2025-11-21Upload 3 filesfa8d58f0 B
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  3. 2025-11-21initial commitfc5d5050 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

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Read further

Repository

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("mlx-community/Dolci-Instruct-SFT-No-Tools-200K")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="mlx-community/Dolci-Instruct-SFT-No-Tools-200K", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/mlx-community/Dolci-Instruct-SFT-No-Tools-200K
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