Agentic LLM for long-horizon, environment-driven workflows: decomposes goals, generates and executes code/tool calls, evaluates outputs, and iterates. The Pro variant emphasizes coding and terminal execution and is published for use with sglang and multi-node H100 deployment.
Open-weight frontier LLM for agentic reasoning and long-context analysis (up to 1M tokens). Uses a LatentMoE + Mamba-2 hybrid with Multi-Token Prediction and NVFP4 efficiency (550B total / 55B active). Suited for multilingual agents, RAG, and heavy tool-use workloads.
Converts text into expressive conversational speech across 100+ languages with zero-shot voice cloning and inline control tokens for emotion, style, prosody, pauses, and sound effects. Released under a research/non-commercial license; commercial use requires separate licensing.
Multilingual frontier LLM optimized for long-context reasoning and agentic workflows, combining a LatentMoE (Mamba-2 + MoE) hybrid architecture with Multi-Token Prediction and NVFP4 quantization; targeted for NVIDIA GPU deployments and governed by the OpenMDW-1.1 license.
Provides compact, agentic text-generation for long-horizon, tool-enabled workflows — trading some peak capability for lower latency and easier on-prem deployment. Key features: adaptive/coherent thinking traces, function-calling support, and sglang/docker-ready serving.
Provides a GGUF-ready QAT (Q4_0) quantized build of Gemma 4 12B that preserves near-bfloat16 quality while reducing memory footprint for local inference; compatible with Transformers-based and GGUF runtimes.
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.
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.
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.
Code-focused sparse Mixture-of-Experts LLM designed for agentic coding and terminal/tool use, offering very long context (256K) and long outputs. Released with open weights under Apache-2.0 and optimized for transformers/vLLM workflows.
Experimental, uncensored fine-tune of Google Gemma-4-12B-it that applies an 'abliteration' technique to remove refusal behaviors; intended for research and testing only and carries elevated safety and legal risks.
Adds discrete audio tokens and an audio encoder to a 30B MoE text LLM so a single model can perform ASR, speech translation, TTS, text-to-audio and speech-to-speech while preserving text reasoning and long-context capabilities; supports thinking/instruct modes and up to 1M-token context.