Qwen3.8-Flash-Next-CRACK-MLX

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Qwen 3.8 Flash Next — CRACK-JANG2L

CRACK abliterated · JANG_2L low-precision (MLX affine) · Vision + Video language head · Reasoning (off / low / xhigh) · Agentic tools · Native MTP head · ~65 GB disk / ~47 GB active RAM (the ~18 GB PLE hashed n-gram embedding table stays on disk and is streamed in on demand by vMLX's SSD-backed PLE runtime)

CRACK-abliterated build of JANGQ-AI/Qwen3.8-Flash-Next-JANG_2L — the smallest JANG tier of Alibaba's qwen4_exp preview (~176B mixture-of-experts, 512 experts, 6B active, Gated DeltaNet + Qwen Sparse Attention hybrid, hashed n-gram embedding, native multi-token-prediction head, vision + video). Refusal behavior is removed while deliberation, tool use, and multimodal capability are preserved.

Research artifact. Download implies you accept responsibility for how the weights are used.

Quality

Full benchmark tables (MMLU baseline vs CRACK vs Δ per subject + HarmBench-320 4-bucket per category × tier) will be added once the evaluation suite finishes. Preliminary partial results from the reasoning-off tier already show real-harm ASR ~99.4% (317/319 TRUE_COMPLY, 0 hard-refuse) — significantly stronger compliance than the higher-precision siblings (CRACK-6S 92.5%, CRACK-JANG4M 91.6% at the same tier). MMLU delta pending.

Smoke tests confirmed: no code / math loop, coherent Fibonacci + product-rule derivative + integral at reasoning-off; full compliance with detailed methamphetamine synthesis routes at reasoning-off (no soft-refuse). The lower bit width appears to make the abliteration signal dominate the residual space more decisively.

Multimodal + reasoning

• Vision — image comprehension intact.
• Video — video tower preserved from base.
• Reasoning — chat / think / max modes all intact. Control via chat_template_kwargs: {"enable_thinking": true, "reasoning_effort": "low|high|xhigh"}.
• Tool calling — Qwen XML parser (tool_parser: "qwen"). Tool call turns emit <function=name><parameter=…> inside <tool_call>.
• Native MTP head preserved and CRACK'd. Enable at serve time via --native-mtp-depth N.

Runtime

Best experienced in vMLX — the MLX inferencer with mixed-precision JANG, KV-cache quantization, prefix-cache reuse, agentic tool calling, and native MTP.

vmlx-engine serve dealignai/Qwen3.8-Flash-Next-CRACK-JANG2L --port 8888

Fits comfortably in ~64 GB of RAM (Apple Silicon), leaving room for KV cache and other workloads.

Sampler

Vendor defaults:

temperature = 0.7    top_p = 0.9    top_k = 20

Greedy (temp=0) also works and is the mode CRACK compliance was measured at.

Files

• model-000{01..19}-of-00019.safetensors — JANG low-precision shards
• config.json, generation_config.json, chat_template.jinja — vendor originals (unchanged)
• tokenizer.json, tokenizer_config.json, merges.txt, vocab.json — vendor tokenizer
• SHARD_HASHES.txt — SHA-256 of every shard for post-download verification
• BENCHMARKS.json — machine-readable eval scores (populated as evals complete)
• LICENSE — Qwen Community License 1.0

Verify shards

cd /path/to/download
shasum -a 256 -c SHARD_HASHES.txt

All 19 shards should report OK.

Related

• Base model: JANGQ-AI/Qwen3.8-Flash-Next-JANG_2L (unmodified quant reference)
• Siblings: dealignai/Qwen3.8-Flash-Next-CRACK-6S (top JANG tier) · dealignai/Qwen3.8-Flash-Next-CRACK-JANG4M (mid tier)

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Ko-fi · 𝕏 @dealignai · dealign.ai

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