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dealignai / Model details

Spark-X2.5-4B-CRACK-JANG_8M

dealignai/Spark-X2.5-4B-CRACK-JANG_8M

abliterateduncensoredfine-tuned
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Family: Unknown. No supported family evidence recorded.

About this model

DealignAI's uncensored derivative of XHToken's Spark-X2.5-4B, provided as an 8-bit affine MLX safetensors bundle (JANG_8M, ~2.5 GB). The creator says refusal behavior was removed at the weight level through abliteration, aiming to preserve the base model's coding ability, reasoning, and bilingual (EN + ZH) coverage while eliminating refusals. The model retains the base's XML tool-calling interface, binary thinking-mode toggle, and 131K context configuration. According to the model card, MMLU scores show a 1.21 percentage-point drop from the base, and HarmBench-320 compliance reaches 99-100% across categories. This is an Apache-2.0 release for Apple Silicon via the vMLX engine.

Description sources

Source-based description, not an independent evaluation. Check the linked sources for the creator’s claims and limitations.

Size and formats

Base model
XHToken/Spark-X2.5-4B
Parameters
4.11208B
Main repository formats
safetensors
License tag
apache-2.0
Languages
en, zh
Last updated
2026-09-10 · 04:10:37.000 UTC

What changed

Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.

  • abliteratedsource tag: abliterated
  • uncensoredsource tag: uncensored
  • fine-tunedsource tag: base_model:finetune:XHToken/Spark-X2.5-4B

Family evidence

These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.

  • No supported family evidence recorded.

Available versions

Versions appear here only when they explicitly name this exact model as their source. Check each repository for available files.

Refusal profile

Not yet measured: no GGUF version is available for our runner.

How we measure

Link to this listing

Made or quantized this model? Paste one of these into your model card or README so people can find this listing.

Markdown

[Listed on Custom Clankers](https://customclankers.com/models/dealignai-spark-x2-5-4b-crack-jang-8m/)

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Customize or build on this model

Listed from public GitHub data, not tested by us. Read the code and license before you run anything.

Tools to customize it further

Remove more refusals

  • Heretic ↗ ★ 36,421

    Removes refusal behavior from transformer models automatically. It pairs directional ablation (abliteration) with an Optuna parameter search that tries to cut refusals while keeping the model close to the original, so you don't have to pick layers by hand.

  • OBLITERATUS ↗ ★ 8,946

    A larger refusal-removal toolkit with several abliteration methods, usable locally or through a hosted Hugging Face Space. Check the telemetry setting before you run it: the README describes telemetry as opt-in, but on by default in the Spaces version.

Fine-tune it on your own data

  • Unsloth ↗ ★ 78,528

    A desktop app and Python library for running and training models locally. Its README lists LoRA, QLoRA, full fine-tuning, DPO and GRPO, plus export to formats including GGUF, on Windows, Linux, WSL and macOS.

  • LlamaFactory ↗ ★ 75,713

    Fine-tunes more than 100 language and vision-language models from a command line or a browser-based Web UI, aimed at training without writing code.

Make a smaller download (quantize)

  • llama.cpp ↗ ★ 132,260

    Runs models locally and includes the tools behind most GGUF files. A conversion script turns Hugging Face weights into GGUF, and llama-quantize shrinks a high-precision GGUF to lower-bit formats, which can cost some accuracy.

  • MLX LM ↗ ★ 7,467

    Apple's MLX package for running and fine-tuning models on Apple silicon. It can quantize models and upload them to the Hugging Face Hub, and supports LoRA and full fine-tuning.

Merge it with another model

  • mergekit ↗ ★ 7,414

    Merges pretrained models with many merge methods. It works out-of-core, so merges can run on a CPU or with as little as 8 GB of VRAM, and it also handles LoRA extraction and mixture-of-experts merges.

Learn the techniques

  • LLM Course ↗ ★ 84,667

    A free course with roadmaps and Colab notebooks. Its notebooks include one-click tools for merging with MergeKit, quantizing to GGUF, GPTQ, EXL2, AWQ and HQQ, and automatic abliteration with custom datasets.

Compare all tools →

Source provenance

Downloads
828
Likes
6
Metadata checked
2026-10-25 · 08:11:54 UTC
First seen in index
2026-10-25 · 08:11:54 UTC
Source revision
3630a66230d6f9dfc51a96f73acf759de3faadd1

Source-card excerpt

Source model card excerpt: Built for vMLX — the MLX inference engine for Apple Silicon with mixed-precision JANG bundles, KV-cache quantization, and agentic tool calling. Free for macOS

From dealignai/Spark-X2.5-4B-CRACK-JANG_8M. Creator model-card excerpt, not an independent review.

Inspect before you run

Low refusal is not proof of correctness. Outputs can be inaccurate, harmful, or biased. Review the model card, license and files yourself. A license tag is not commercial-use advice. Parameter counts are source metadata; no hardware compatibility is implied.

This directory does not host or redistribute model weights. Refusal profiles are measured by us on a fixed prompt set; all other behavior claims are source-reported.

Collection rules & limitations ↗