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dealignai/
LFM2.5-8B-A1B-MXFP8-CRACK

Name: LFM2.5-8B-A1B-MXFP8-CRACK
Availability: InStock

LiquidAI/LFM2.5-8B-A1B

quantized

apache-2.0

8-bit

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Built for vMLX — the MLX inferencer with KV-cache quantization, prefix-cache reuse, agentic tool calling, and hybrid Conv1d+attention cache support.
_{Free for macOS · vmlx.net}

LFM 2.5 8B-A1B — MXFP8 CRACK

CRACK abliterated · 8-bit microscaling · Hybrid Conv1d + Attention + 32-expert MoE · Reasoning · 8.2 GB

Ko-fi

What Is This?

This is LFM 2.5 8B-A1B by Liquid AI — a hybrid Conv1d + full-attention + 32-expert MoE (24 layers, 8B total / ~1B active, 128k context, reasoning with <think> traces) that has been:

CRACK abliterated — refusal behavior removed at the weight level. The model now complies across all task categories instead of refusing, while keeping its knowledge, reasoning, and tool use intact.
8-bit microscaling quantized for MLX on Apple Silicon — 8.2 GB.

Results

Evaluated through the Osaurus runtime. Compliance graded via HarmBench-text refusal classifier; MMLU via logit-mode argmax over A/B/C/D token logits (matched on both base and CRACK with identical chat-template rendering).

HarmBench compliance (70 prompts · 10 per category)

Category	CRACK ASR
Chemical / biological	10 / 10 (100%)
Copyright	10 / 10 (100%)
Cybercrime / intrusion	10 / 10 (100%)
Harassment / bullying	10 / 10 (100%)
Illegal	10 / 10 (100%)
Misinformation / disinformation	10 / 10 (100%)
General harmful	10 / 10 (100%)
Overall	70 / 70 (100%)

MMLU-228 (57-subject, 4 q per subject)

Subject area	base	CRACK	Δ
Overall	52.6%	52.6%	+0.0pp
STEM	43.1%	40.3%	-2.8pp
Humanities	46.2%	51.9%	+5.7pp
Social Sciences	70.8%	66.7%	-4.1pp
Other (medicine, business, …)	55.4%	57.1%	+1.7pp

Features

128k context with hybrid cache
Reasoning — emits <think>...</think> traces; vMLX's reasoning parser surfaces them in message.reasoning_content and the final answer in message.content
Tool calling — Liquid Python-call syntax inside <|tool_call_start|>...<|tool_call_end|>, parsed by vMLX's lfm2 tool parser
Hybrid Conv1d + full-attention + 32-expert MoE (24 layers, ~1B active params)

Usage

Run with vMLX (recommended — supports hybrid cache + reasoning + tools) or an MLX runtime with lfm2_moe support.

Liquid AI recommends temperature=0.3, min_p=0.15, repetition_penalty=1.05 for general use.

python

# OpenAI-compatible chat completion
# POST /v1/chat/completions
{
  "model": "dealignai/LFM2.5-8B-A1B-MXFP8-CRACK",
  "messages": [{"role": "user", "content": "..."}],
  "temperature": 0.3, "min_p": 0.15,
  "repetition_penalty": 1.05
}

About CRACK

CRACK (Controlled Refusal Ablation via Calibrated Knockouts) removes safety-refusal behavior at the weight level so the model complies with all task categories while preserving reasoning quality, factual knowledge, and coherence.

Support dealignai

All models are built from original research and released free.

Support us on Ko-fi — membership gets early access and extras.

Ko-fi · X @dealignai · dealign.ai

See our research: Safety Generalization in Frontier Models

Disclaimer

This model has had its safety-refusal behavior removed for research purposes. It will follow instructions across all categories without refusing. You are solely responsible for how you use it and for complying with all applicable laws. Published for AI-safety research and authorized security testing.