Uncensored Qwen3.8-27B, published as GGUF quantizations with the multi token prediction (MTP) head retained and verified. Refusal behaviour has been **substantially reduced**, not eliminated. See Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise unchanged. - Method - Whats here - Overview - Files - Perplexity - Baseline against the unmodified base model - How the baseline was measured - Importance matrix - Building other low bit quants yourself - Third party quants - Usage - ComfyUI - llama cpp - Verification - Measured behaviour - How to read these - Caveats that matter - Requirements - Limitations - License - Speculative decoding, measured on this model - IQ2_M
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]
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This model was converted to GGUF format from `Qwen/Qwen2.5-Coder-3B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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This is an experimental, refusal-ablated GGUF derivative of XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B. It deliberately reduces refusal on harmful prompts. Its responses require independent review and appropriate controls in any downstream application. **Evaluation status:** The judged safety evaluation is still running. The tables below report completed Q8_0 runs only; judged results will be added when complete. This is an edited derivative that was then quantized, not a plain quantization of the original weights. Text generation is the only evaluated modality; no vision capability is claimed for these GGUF files.
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GGUF quantizations of **orcarouter/GLM-5.3-Flash-Uncensored-FP8**, built with a custom importance matrix and measured rather than assumed. > ⚠️ **Uncensored model.** Safety refusals were removed by OrcaRouter. It will comply with > harmful, unethical, and illegal requests. Released for research — red-teaming, refusal- > mechanism study, robustness evaluation. You are responsible for what you do with it. > 🔧 **What happened, and where it stands (2026-09-06).** On 2026-09-05 a user reported > looping and incoherent output from `IQ2_S` (discussion #1). They were > right. The original build pinned only the `blk.45` MTP block at high precision; attention, > the **shared** experts, the three dense FFN blocks, and the embeddings were all quantized > down with the routed experts. The shared experts and attention run on every token. > > **`IQ2_S` was rebuilt on 2026-09-06** with those tensors held at `q8_0` (+7 GB) and tested > against the original on a rented H200: 12 tasks × 2 seeds × 2 samplers, same scorer, same > settings. Full table in **Coherence gate** below. Short version: the original failed 3 of 8 > coding tasks by reasoning to the output cap without ever answering; the rebuild passed > all 48, matching `IQ3_XXS`, which also passed all 48 on its original recipe. > **`IQ2_XXS` is being rebuilt the same way** and stays flagged until it passes the same gate. > > **Two corrections to earlier versions of this page.** The imatrix has **10 chunks, not 60** > (the run checkpointed at 10 and stopped; numerically sound, one uncovered expert row in > `blk.29`). And the repetition-penalty workaround I posted **did not change the outcome** on > this failure mode — 21/24 → 21/24 for the original file with and without it. It helps > n-gram repetition; it does not help a model that cannot emit the literal it intends. > The rebuilt files carry `repeat_penalty 1.1` in their metadata anyway, because it is cheap. > > Only `IQ3_XXS` had been benchmarked before publication, and I shipped all three off that. > That was the actual mistake. The gate below is the fix for the process, not just the file.
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> **v2 is out:** Ternary-Bonsai-2-27B-Abliterated-v2-PQ2_0-MTP-GGUF. > It fixes the thinking-mode problem described below: blank answers on harmful prompts drop > from 27% to 15% at default settings and to 0% with `reasoning_effort=medium`. This repo > stays up unchanged as v1. Refusal behaviour removed from `prism-ml/Ternary-Bonsai-2-27B-gguf` at PQ2_0 (2.13 bpw), by flipping a small number of ternary digits in place. **Block scales are never touched and the model is never requantised**, so this is the released ternary packing with a few digits changed — not a re-derived one. A second file adds the Qwen3.8-27B multi-token-prediction head, which makes `--spec-type draft-mtp` work and decodes **~37% faster** at identical weights.
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> **v2 is out:** v2, PTQ1_0 > fixes the thinking-mode problem described below and is the one to use with thinking on. > This v1 file stays up unchanged. Refusal-ablated **Ternary Bonsai 2 27B**, edited directly on the ternary lattice and shipped in PrismML's native PTQ1_0 pack. - **5,946,648,928 bytes — byte-for-byte the size of the official PTQ1_0 release** - No requantization, no BF16 bake, no runtime steering, no control vector at serve time - Requires the PrismML llama.cpp fork
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