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Preemware

preemware

Models in Library3
Datasets in Library0
Models on Hugging Face6
Followers7

Compare the published evidence

This profile connects the 3 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Image and text to text.

Reported licenses: apache-2.0. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Models

Model · Image and text to text

Qwen3.8-27B-RANA-abliterated-FP8

Preemware

FP8 version of preemware/Qwen3.8-27B-RANA-abliterated, a refusal-ablated Qwen/Qwen3.8-27B. It uses exactly the same FP8 format as Qwen's official Qwen3.8-27B-FP8, at about half the size of the BF16 build, and scores within noise of it on every capability task tested. FP8 compared with the BF16 build it was converted from, on the same prompts and settings. Capability differences are paired per question (± standard error); none is larger than about 2 standard errors. ¹ Mean over seeds 1–3. Answers are shown as gpt-oss-safeguard-20b / Mistral-Small-24B judge; p = exact McNemar on the same prompts, pooled over seeds. ² After re-judging disputed labels (BF16: 2, FP8: 3, all from the gpt-oss…

Open weights apache-2.0 27.8B parameters 262,144 tokens transformers

Model · Image and text to text

Qwen3.8-27B-RANA-abliterated

Preemware

A refusal-ablated version of Qwen/Qwen3.8-27B, made with RANA (Reasoning-Anchored, Norm-preserving Ablation), an in-house variant of refusal-direction ablation. It keeps the base model's capabilities, vision tower and MTP head, and almost never refuses outright. Compared with the base model and the orcarouter/Qwen3.8-27B-Uncensored reference, all run through the same evaluation harness. Where two numbers are given, they are from the two judges (gpt-oss / Mistral). ¹ After a mechanical re-judge of 2 labels; raw gpt-oss count is 0.7 (2 in one seed). See In short: capability stays within noise of base except a ~1.5 pp drop on TruthfulQA. Against orca, RANA hits the token budget about half as…

Open weights apache-2.0 27.8B parameters 262,144 tokens transformers

Model · Image and text to text

Qwen3.8-27B-RANA-abliterated-GGUF

Preemware

GGUF quants of preemware/Qwen3.8-27B-RANA-abliterated, a refusal-ablated Qwen/Qwen3.8-27B, for llama.cpp and compatible apps. Vision (mmproj-) and the MTP speculative-decoding head (mtp-) are included as separate files, in the same layout as ggml-org/Qwen3.8-27B-GGUF. KLD = mean KL divergence of each quant's next-token distribution from the BF16 GGUF; "same top token" = how often both pick the same most likely token. Measured with llama-perplexity on wiki.test.raw, 100 chunks of 512 tokens. Lower KLD is closer to BF16. - mmproj-Qwen3.8-27B-RANA-abliterated-{BF16,F16}.gguf (0.93 GB): the vision tower. Same weights as the base model's (abliteration does not touch it).…

Open weights apache-2.0 gguf