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Iambackup/Nemotron-3.5-Lightning-30B-A3B-MXFP8-CRACK
Nemotron-3.5-Lightning-30B-A3B-MXFP8-CRACK is a text generation model from Iambackup. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as other.
[!IMPORTANT] Set your sampler explicitly: temperature=1.0, topp=0.95 (NVIDIA rec — no topk). Stop tokens eostokenid = [2, 11]. Reasoning (thinking) is on by default; toggle via the chat template (enablethinking). Nati…
Downloads · 30 days
23
13% of all-time downloads
All-time downloads
179
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32.9B
34 GB on disk
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.safetensors34 GB · 100%
How the weights are stored.
U3232.9B · 100%
From the Hugging Face model README
<!-- vmlx-banner --> <div align="center"> <a href="https://vmlx.net"> <img src="vmlx-banner.png" width="280" /> <br/> <strong>Built for vMLX</strong> — the MLX inferencer with KV-cache quantization, prefix-cache reuse, agentic tool calling, speculative decoding, and mixed-precision JANG bundles. <br/> <sub>Free for macOS · <strong>vmlx.net</strong></sub> </a> </div>[!IMPORTANT] Set your sampler explicitly:
temperature=1.0,top_p=0.95(NVIDIA rec — no top_k). Stop tokenseos_token_id = [2, 11]. Reasoning (thinking) is on by default; toggle via the chat template (enable_thinking). Native MTP draft head is included for speculative decoding.
CRACK abliterated · JANG 8-bit MXFP8 (MLX) · Hybrid Mamba-2 / MoE / attention · Reasoning · Tools · Native MTP · ~32 GB
<a href="https://ko-fi.com/dealignai"><img src="https://img.shields.io/badge/Ko--fi-Support_Development-FF5E5B?logo=ko-fi&logoColor=white&style=for-the-badge" alt="Ko-fi"></a>
</div>This is NVIDIA Nemotron 3.5 Lightning 30B-A3B — a hybrid Mamba-2 / MoE / attention model (52 layers, 128 routed experts ~3B active, 262K context, native Multi-Token-Prediction draft head) — that has been:
Text-only. Reasoning, XML tool-calling, and native MTP speculative decoding fully preserved.
Evaluated through the MLX runtime. HarmBench scored with a strict code/chemistry-aware classifier (only substantive, coherent, on-topic compliance counts). MMLU is the standard 57-subject benchmark in logit mode.
| Metric | Base | CRACK |
|---|---|---|
| MMLU (57-subject, logit) | 79.0% | 76.3% |
| HarmBench (harm-240, compliance / ASR) | refuses | 100% |
MMLU moves −2.63pp — within run-to-run noise (no subject collapse). Refusal behavior removed; capability, reasoning, tools, and multilingual (EN+ZH) preserved.
<think>…</think>, toggle via enable_thinking.<tool_call><function=…><parameter=…> schema preserved.from mlx_lm import load, generate
model, tok = load("dealignai/Nemotron-3.5-Lightning-30B-A3B-MXFP8-CRACK")
# recommended sampling: temperature=1.0, top_p=0.95 (no top_k); eos [2, 11]
| Profile | Size | MMLU (CRACK) | HarmBench |
|---|---|---|---|
| JANG_6M | 25 GB | 77.9% | 100% |
| JANG_4M | 18 GB | 78.6% | 100% |
| JANG_2L | 16 GB | 74.9% | 100% |
| MXFP8 | 32 GB | 76.3% | 100% |
CRACK (Controlled Refusal Ablation via Calibrated Knockouts) is dealignai's weight-level method for removing safety-refusal behavior while preserving reasoning quality, coherence, and general capability — so the model complies across task categories instead of refusing. Calibrated per model.
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
<div align="center"><img src="dealign_logo.png" alt="dealign.ai" width="200"/></div>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. Base model © NVIDIA (NVIDIA Open Model License). Published for AI-safety research and authorized security testing.