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+Bonsai-27B-antidoom-1bit-Q1_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+base_model: prism-ml/Bonsai-27B-gguf
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- bonsai
+- qwen3_5
+- antidoom
+- ftpo
+- anti-repetition
+- 1-bit
+- q1_0
+- gguf
+- speculative-decoding
+- dspark
+- llama.cpp
+- blackwell
+---
+
+# Bonsai-27B-antidoom-1bit-DSpark
+
+A **[DSpark](https://github.com/PrismML-Eng/Bonsai-demo)-accelerated + [antidoom](https://github.com/Liquid4All/antidoom)
+(FTPO anti-repetition)** build of [`prism-ml/Bonsai-27B`](https://huggingface.co/prism-ml/Bonsai-27B-gguf) —
+the 1-bit (`Q1_0`, 1.125 bpw g128) quantization of the Qwen3.5-27B hybrid-attention model.
+
+Runs in the **[PrismML-Eng/llama.cpp](https://github.com/PrismML-Eng/llama.cpp)** fork (CUDA, the
+Q1_0 hybrid-attention kernels). Benchmarked on a single **RTX 5090 (32 GB, Blackwell sm_120)**.
+
+## Contents
+| File | What |
+|---|---|
+| `Bonsai-27B-antidoom-1bit-Q1_0.gguf` | antidoom-tuned 1-bit model (4.5 GB) |
+| `Bonsai-27B-dspark-Q4_1.gguf` | DSpark speculative drafter (from prism-ml) |
+| `Bonsai-27B-mmproj-Q8_0.gguf` | vision projector (from prism-ml) |
+
+## Run with DSpark (fastest)
+```bash
+build/bin/llama-server \
+  -m Bonsai-27B-antidoom-1bit-Q1_0.gguf \
+  -md Bonsai-27B-dspark-Q4_1.gguf \
+  --spec-type draft-dspark --spec-draft-n-max 4 \
+  -ngl 999 -ngld 999 -fa on -c 16384 -np 1
+```
+
+## Benchmark (RTX 5090, greedy, bs=1, 256 tok)
+| Config | tok/s | accept | note |
+|---|---|---|---|
+| **DSpark, code prompt** | **186.2** | 78.6% | **1.16× over native** |
+| Native (llama-bench tg256) | 161.2 | — | reference |
+| Native (llama-server decode) | 157.5 | — | |
+| DSpark, prose prompt | 132.8 | 51% | low-accept workloads slower |
+
+DSpark speedup is workload-dependent: on predictable content (code, high accept) it beats native;
+the 1-bit target is so bandwidth-cheap (3.5 GB) that low-accept drafting overhead can dominate.
+The 5090 exceeds prism-ml's published H100 native (104.8 t/s) — this 1-bit model is memory-bandwidth-bound.
+
+## Antidoom (FTPO) pass — what was done
+Bonsai-27B is unusually **doom-loop resistant** (it self-corrects repetition and stays coherent even
+under forced long generation), so runaway-repetition pairs were surfaced by **priming**: contexts that
+have already begun repeating a phrase, sent to the raw completion endpoint, where the model continues
+the loop. Antidoom's own detector + chosen-token sampler then extracted **40 FTPO preference pairs**
+(rejected = the loop-continuation token, chosen = coherent escapes). FTPO trained a LoRA
+(r=32, q/k/v/o + gate/up/down, 12 epochs, lr 3e-5) on the FP16 unpacked model:
+
+- **chosen-win (prefers the coherent escape over continuing the loop): 0.11 → 0.66** (early-stopped).
+- LoRA merged (CPU) into FP16, re-quantized to `Q1_0`. Note the aggressive 1-bit format partially
+  dilutes the LoRA's fine adjustments, so the anti-repetition effect is strongest at higher precision.
+
+Output remains coherent at 1-bit. This is a demonstration of the antidoom method on a
+1-bit hybrid-attention target, packaged with the shipped DSpark drafter.
+
+## Provenance
+Base weights, DSpark drafter and mmproj are from [prism-ml](https://huggingface.co/prism-ml)
+(re-quantized after the antidoom LoRA merge for the model weights; drafter/mmproj copied unchanged).
+Built with [antidoom](https://github.com/Liquid4All/antidoom) and the PrismML llama.cpp fork.