nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2

image-text-to-text model by nerkyor

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A model post-trained with several rounds of SFT and RLOO on top of Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2, which is itself the official Qwen3.8-27B post-trained with SFT and SimPO. It is built to fix the original model's habit of failing to stop: it already has an answer, yet keeps re-deriving until it hits the 94K cap with no final answer. This repository provides five safetensors tiers, BF16, static FP8 Block128, NVFP4 W4A16, NVFP4 W4A4, and NVFP4 W4A4-W8A8 (mixed precision), all fully multimodal (text + image/video) with the official BF16 MTP bundled; all load in SGLang without patches, and the FP8 package is also smoke-tested on vLLM. It also has NInfer W4A4 / W4A4-W8A8 (NVFP4-NInfer/), INT4 W4A16 (INT4/W4A16/, text + image, vLLM), INT8 W8A8 (INT8/W8A8/), INT8 W8A16 (INT8/W8A16/, text + image), and GGUF Q2 / Q3 / Q4 LynnStyle (plus Q8 MTP versions), Q6K, and Q80 (GGUF/, GGUF-NInfer/, built-in MTP). Related repositories Lineage: original Qwen3.8…

nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2 on Hugging Face A short preview, not the full document.

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Model overview

nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2 is a image-text-to-text model repository published by nerkyor on Hugging Face. A library was not reported.

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Model facts

Task: image-text-to-text
Library: Not reported
Recent downloads (30 days): 674,221
Cumulative likes: 508
Hugging Face trending score: 18
Reported safetensors parameters: Not reported
Architecture: Not reported
License: apache-2.0
Access: Not gated by Hugging Face
Created: 2026-10-04T10:44:11.000Z
Last modified: 2026-10-31T16:40:07.000Z

Compatibility and lineage

Reported languages: en, zh

Reported base models: nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:30.840Z. Daily imports are snapshots, not real-time monitoring.

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