GGUF

Unsloth has released optimized GGUF quantizations of DeepSeek-V4-Flash, a 284B parameter Mixture-of-Experts model that activates 13B parameters and supports 1 million token context windows. The Q8 quantization (UD-Q8_K_XL) runs at 162GB with claimed lossless precision, only 7GB larger than the Q4 variant.

Google DeepMind has released quantization-aware training (QAT) optimized versions of its Gemma 4 model family in GGUF Q4_0 format. The QAT versions preserve similar quality to bfloat16 while dramatically reducing memory requirements, with models available across the entire Gemma 4 lineup: E2B, E4B, 12B, 26B A4B, and 31B.

StepFun has released Step-3.7-Flash, a 198B-parameter sparse Mixture-of-Experts vision-language model that activates approximately 11B parameters per token. The model supports a 256K context window, native image understanding via a 1.8B-parameter vision encoder, and offers three selectable reasoning levels.

Liquid AI has released LFM2.5-8B-A1B, an 8-billion parameter hybrid model designed specifically for edge AI and on-device deployment. The model is available in multiple GGUF quantized formats ranging from 4-bit (4.84 GB) to 16-bit (16.9 GB), optimized for memory efficiency.

IBM has released the Granite 4.1 family of language models under Apache 2.0 license. The models come in 3B, 8B, and 30B parameter sizes. Unsloth has released 21 GGUF quantized variants of the 3B model ranging from 1.2GB to 6.34GB.

Zhipu AI released GLM-5.1, a 754-parameter model optimized for agentic engineering tasks. The model scores 58.4% on SWE-Bench Pro, outperforming Claude 3.5 Sonnet (57.3%), and demonstrates sustained reasoning capability over hundreds of iterations.