The official NInfer Qwen3.8-27B artifact with one change: its prefill GEMMs may quantize their activations to int8 and run on int8 tensor cores. **The weights are byte-for-byte the official ones** (Q4/Q5 projections, Q8 vocabulary, MTP head, DFlash2 drafter, vision tower, indexed proposal head, tokenizer and chat template); only the activation permission of 512 projection inputs changes from `A16Only` to `AllowA8`. **This file only runs on the NInfer build from JGamboa/ninfer-4090-windows (branch `main`), on an NVIDIA RTX 4090 (`sm_89`).** It does not load in llama.cpp, vLLM or Transformers, nor in engines without the int8 prefill route. Current figures for this artifact (2026-09-27, `main` at `e7d309e3`, one RTX 4090 at stock clocks without a display, Windows 11, CUDA 13.4):
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View details ↗Prism ML's Ternary Bonsai 2 27B converted to NInfer's `.ninfer` format, with the vision tower and a Qwen3.8 MTP head for speculative decoding. One self-contained file with text, vision and MTP. **These files only run on the NInfer build from JGamboa/ninfer-4090-windows (branch `main`), on an NVIDIA RTX 4090 (`sm_89`), on Windows or Linux.** They do not load in llama.cpp, vLLM or Transformers. Each file has a `.conversion.json` report next to it: sources, formats and the conversion command.
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View details ↗This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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