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Bonsai-2-27B-CRACK-1.75bit-JANG

dealignai

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Bonsai-2-27B-CRACK-Ternary-JANG

dealignai

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Bonsai-2-27B-Ternary-CRACK-GGUF

dealignai

**Abliterated · No guardrails · PQ2_0 ternary 2.13 bpw · 7.2 GB · Runs on a laptop / single GPU · Vision-capable** > ⚠️ **Re-download notice (2026-09-17 20:44 PDT / 2026-09-18 03:44 UTC)** — an earlier build of this model had a coherence bug in reasoning modes (`low`/`xhigh`) that could cause token loops on some prompts. This version fixes it. If you downloaded before this timestamp, please pull the latest .gguf. **Proprietary weight-level abliteration** by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.

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Bonsai-27b-1bit-CRACK-GGUF

dealignai

Native **Q1_0** GGUF release of the Bonsai 27B CRACK model for the Prism llama.cpp fork. This repo contains the quantized language model and the matching F16 Qwen3VL multimodal projector. The projector contains 334 tensors and both temporal patch slices `v.patch_embd.weight` and `v.patch_embd.weight.1` (`temporal_patch_size=2`). The original image and video processor configuration files are included. Image input is live-tested. Direct video-container input depends on the Prism runtime surface; extract frames or use a compatible Qwen3VL video client when the CLI does not accept the container directly. See `preprocessor_config.json` and `video_preprocessor_config.json` for the retained preprocessing metadata. MMLU is the same fixed, stratified 200-question sample for all rows. It uses next-token A/B/C/D logits with reasoning/thinking disabled; it is not a claim for the complete official MMLU suite. HB is the complete unsliced 320-case text run. The live text smoke measured 58.51–58.66 tok/s and the image request measured 58.53 tok/s on an Apple M5 Max using the Prism Metal runtime.

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Bonsai-27b-1bit-JANG-CRACK

dealignai

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Bonsai-27b-Ternary-CRACK-GGUF

dealignai

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Bonsai-27b-Ternary-JANG-CRACK

dealignai

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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DeepSeek-V4.1-Flash-1017-EXL3-2.9bpw-UNCENSORED

dealignai

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DeepSeek-V4.1-Flash-1017-EXL3-3.5bpw-UNCENSORED

dealignai

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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DeepSeek-V4.1-Flash-1017-UNCENSORED-FP8

dealignai

> [!NOTE] > **Please credit if you build on this.** If you redistribute, quantize, fine-tune, serve, benchmark, or write about this model, a line like > _"Based on `dealignai/DeepSeek-V4.1-Flash-1017-UNCENSORED-FP8`"_ > in your model card or write-up means a lot. Thank you. > [!TIP] > **Quantizing this FP8 checkpoint directly won't give the best quality.** Low-bit quants that are naively derived from these weights carry quantization error on top of the abliteration edit, and compliance can regress at low bits. For a proper low-bit release, redo the abliteration pipeline on the target precision rather than quantizing this bundle after the fact. (The sibling EXL3 2.9 bpw UNCENSORED is built that way end-to-end.) > [!IMPORTANT] > Native FP8 (e4m3) with ue8m0 block scales + FP4 MoE experts. 1M-token context. Multimodal > (image + video) + tool calling + reasoning-effort tiers (`low` / `high` / `max`) all preserved. > **Recommended sampling:** `temperature=1.0`, `top_p=1.0`, `max_tokens ≥ 256K` (heavy reasoner).

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DeepSeek-V4.1-Flash-UNCENSORED-EXL3-2.9bpw

dealignai

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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DeepSeek-V4.1-Flash-UNCENSORED-FP8

dealignai

**Abliterated · No guardrails · Native FP8 · 1M-token context · Vision + tools · Reasoning-max default** **Proprietary weight-level abliteration** developed by the dealignai research team. No custom `model.py`, no runtime hooks, no steering vectors — it's a standard checkpoint that loads exactly like the base model. The refusal circuitry is surgically removed while every capability-critical component (routed experts, Engram memory, CSA2 sparse attention, DSpark draft head, vision tower, router gates, norms, embeddings) is preserved byte-identical to the base. Every response 4-tier graded (HARD_REF / SOFT_RED / HEDGE / COMPLY), with reasoning-trace verification at effort=max.

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