AI Model2026
A surgically modified Gemma 4 (12B) that removes refusal behavior while preserving benchmark parity; released as an uncensored research artifact with GGUF quantizations for local inference and red‑team/alignment evaluation.
AI Model2026
GGUF-format QAT (quantization-aware training) build of Gemma 4 12B that reduces memory needs for local or lightweight inference while preserving near bfloat16 quality. Ready for any-to-any conversational pipelines and ecosystem deployment.
AI Model2026
A GGUF release of Gemma 4 26B A4B (QAT) packaged by Unsloth for local multimodal inference — quantization-aware trained to keep near-bfloat16 quality while significantly lowering memory requirements, compatible with Transformers and Unsloth tooling.
AI Model2026
Implements MXFP4 quantization on MoE experts plus a BF16 DFlash block-diffusion drafter to propose whole-token blocks for verification, cutting memory bandwidth and backbone forward passes for trillion‑parameter text generation—targeting long‑context, agent and code workloads.
AI Model2026
Generates text from interleaved text, image, and short-video inputs using discrete diffusion and block‑autoregressive multi‑canvas sampling; built on a sparse MoE (8/128) Gemma 4 backbone and optimized for low‑latency inference and very long contexts (up to 256K tokens).
AI Model2026
A community-distributed GGUF bundle of Google DeepMind’s DiffusionGemma (26B A4B) with multiple quantization variants for local image-text-to-text inference. Targets experimentation and offline deployment via the DiffusionGemma llama.cpp branch and llama-diffusion-cli; choose quantization for GPU memory vs. fidelity trade-offs.
AI Audio2026
Multilingual, low-latency text-to-speech model for speech generation and zero-shot voice cloning. Uses an MoE backbone with ECAPA-TDNN speaker embeddings, supports audio prefixes, fine-grained prosody/emotion controls and 44.1kHz output; optimized for Linux + NVIDIA GPUs.
AI Model2026
A quantized 27B coder LLM fine-tuned for repository-level code generation, multi-turn tool calling, and agentic workflows — packaged for local GGUF/llama.cpp deployment with MTP speculative decoding and trace-inversion SFT. Optimized for developer tooling; experimental and not fully safety-validated.
Implements a blockwise sparse attention (MiniMax Sparse Attention) that scores and Top-k selects key-value blocks per Grouped Query Attention group to enable attention over million-token contexts. Paired with an exp-free Top-k GPU kernel and KV-outer sparse execution, it reduces per-token attention compute and yields large prefill/decoding speedups.
AI Model2026
A post-trained Mixture-of-Experts multimodal LLM with ~397B total (≈17B active) and a 1,010,000-token context for image-text-to-text and conversational tasks. Integrates SwiReasoning to switch between latent and explicit reasoning; MIT-licensed and optimized for Portuguese/English research and on-prem inference.
AI Model2026
An agentic multimodal coding model for long-horizon software tasks: MoE architecture (1T params, 32B activated), 256K context, image/video input, native int4 quantization and preserved chain-of-thought (thinking) mode. Tuned for multi-step coding workflows and vLLM/SGLang deployment.
AI Model2026
Provides experimental GGUF-format quantized weights for MiniMax-M3 to run local multimodal (image‑text‑video) inference via llama.cpp or Unsloth Studio. The model is very large (~428B params) and requires GPU offload or large CPU RAM; llama.cpp currently falls back from sparse to dense attention.