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The extrapolated (ExPO) model based on `allenai/tulu-2-dpo-70b` and `allenai/tulu-2-70b`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.5)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):

Text generation
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**A new version trained with GRPO for more consistent, high-quality, and aligned character roleplaying.** > **Welcome to V2!** I'm Chun (@chun121), and this is the next evolution of the Qwen3-4B Roleplay model. This version moves beyond standard fine-tuning and leverages **GRPO (Generative Responsive Preference Optimization)** to align the model's behavior with the core principles of great roleplaying. Character Consistency High-Quality Dialogue Intent Understanding Structured Format

Text generation
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GLInt

chungimungi

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Embeddings & similarity
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Visual QA
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This repository contains one independently downloadable `Q3_K_M` GGUF of `Qwen/Qwen3.8-27B`. It is an unmodified vanilla quantization, not a fine-tune, merge, ablation, alignment change, or chat-template modification. The source weights are pinned to `1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0`. The official checkpoint's internal `Qwen3_5ForConditionalGeneration` / `qwen3_5` architecture identifier does **not** mean this is a Qwen3.5 model. `validation_result.json` records the exact per-file smoke result and its inherited aggregate functional gates. These split repositories do not claim a new benchmark run; they preserve byte-identical, already-validated artifacts.

Visual QA
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**This quantization was created to allow the entire model to fit into the memory of an NVIDIA graphics card with 16GB of VRAM.** **Importan - please use "export GGML_CUDA_ENABLE_UNIFIED_MEMORY=1"** after that just use ctx-cliff.py to check if you server config fit in you vram. This model has the token_emd.weight tensor bumped up to q8_0, which increases its download size but makes no difference in VRAM. By default, the token_emd.weight tensor is loaded entirely into system RAM. Upgrading it to q8_0 is therefore, in practice, a free upgrade to the model's quality, for example in interpreting non-English languages.

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