Alignment dataset tracked in the /datasets sub-catalog.
Converted to be directly supported in MLX-LM and 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/hermes-3-mlx \
--iters 100 \
--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/hermes-3-mlx \
--iters 100 \
--max-seq-length 4096
mlx_lm_lora.train \
--model mlx-community/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1 \
--train \
--data mlx-community/hermes-3-mlx \
--iters 100 \
--max-seq-length 4096
mlx_lm.lora \
--model mlx-community/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1 \
--train \
--data mlx-community/hermes-3-mlx \
--iters 100 \
--max-seq-length 4096
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.
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.
Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.
Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.
from datasets import load_dataset
ds = load_dataset("mlx-community/hermes-3")from huggingface_hub import snapshot_download
snapshot_download(repo_id="mlx-community/hermes-3", repo_type="dataset")git clone https://huggingface.co/datasets/mlx-community/hermes-3Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.