HyperCLOVA-X-KETI-HAECHI-32B

Weight-only quantization of built to a hard 22 GB budget with the lowest perplexity achievable inside it. This is not a uniform W4A16. Bit-width was allocated by measurement: every candidate was quantized, served by vLLM, and scored on the same held-out corpus, and the budget was spent where it bought the most. - Served by vLLM's Marlin kernels throughout — no fallback kernels. - Text + MTP speculative decoding + vision all verified. 1. The MLP, GatedDeltaNet and lmhead tensors are round-to-nearest, not AutoRound-tuned. Only qproj/kproj/vproj carry AutoRound tuning (they are inherited unchanged from an AutoRound int4 run). The measurements below were taken on round-to-nearest tensors, so…

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Qwen2.5-VL-32B-Instruct-AWQ

Qwen

In addition to the original formula, we have further enhanced Qwen2.5-VL-32B's mathematical and problem-solving abilities through reinforcement learning. This has also significantly improved the model's subjective user experience, with response styles adjusted to better align with human preferences. Particularly for objective queries such as mathematics, logical reasoning, and knowledge-based Q&A, the level of detail in responses and the clarity of formatting have been noticeably enhanced. In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on…

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Qwen2.5-VL-32B-Instruct

Qwen

In addition to the original formula, we have further enhanced Qwen2.5-VL-32B's mathematical and problem-solving abilities through reinforcement learning. This has also significantly improved the model's subjective user experience, with response styles adjusted to better align with human preferences. Particularly for objective queries such as mathematics, logical reasoning, and knowledge-based Q&A, the level of detail in responses and the clarity of formatting have been noticeably enhanced. In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on…

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Qwen3.6-35B-A3B-NVFP4

Red Hat AI

NVFP4 variant of Qwen 3.6 35B for reasoning and tool calling.

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gemma-4-31B-it

Google

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…