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AI Models 85003
- Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF↗
IsValorum
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. > > *Independently computed on private cloud clusters. If this handcrafted release saves you VRAM and runs faster on your GPU, consider fueling the community compute fund on Ko-fi.* > > Explore the new canonical V3 releases. MiniPlus prioritizes Q5-class to near-Q6 fidelity without a fixed file-size ceiling; NanoPlus targets Q4-class quality at a practical footprint. > > - **Qwen3.8 27B — MiniPlus V3** — 14.16 GB **(+13.21% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Qwen3.8 27B — NanoPlus V3** — 11.35 GB **(+11.49% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — MiniPlus V3** — 14.28 GB **(+5.70% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — NanoPlus V3** — 11.08 GB **(+2.26% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > > Percentages compare the reported WikiText-2 PPL with GSQ-RCO IQ3_S (7.07). The linked 27B cards document the evaluation protocol and model variants. > > **Explore the APEX-I MiniPlus V2.1 collection** > > ### EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP > These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do: > > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1** — a versatile frontier MoE for broad reasoning, multilingual work, agents, tool use, and multimodal tasks. > - **Best for:** General reasoning, agent workflows, tool calling, and flexible multimodal use. > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 Abliterated** — the V2.1 refusal-ablated Qwen3.6 edition for users who deliberately prefer reduced refusal behavior. > - **Best for:** Workflows where an abliterated Qwen3.6 variant is explicitly desired. > - **Ornith 1.5 APEX-I-MiniP
- DeepSeek-V4.1-Flash-DS4-IQ2↗
ivanfioravanti
- Hermes3.6-35B-A3B-Uncensored-Genesis-NVFP4-GGUF↗
jan1k
- qwen3-8b-heretic↗
kalpakjian
- Qwen3.5-2B-Hermes↗
keithlol
- gemma-2-27b-it-IMat-GGUF↗
legraphista
Original Model: google/gemma-2-27b-it Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp b3790 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
- Llama-3.1-8B-Stheno-v3.4-GGUF-IQ-Imatrix↗
Lewdiculous
- LFM2-2.6B↗
LiquidAI
LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. We're releasing the weights of four post-trained checkpoints with 350M, 700M, 1.2B, and 2.6B parameters. They provide the following key features to create AI-powered edge applications: Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance. They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
- Qwen2.5-0.5B-Instruct↗
litert-community
This model provides a few variants of Qwen/Qwen2.5-0.5B-Instruct that are ready for deployment on Android using the LiteRT (fka TFLite) stack and MediaPipe LLM Inference API (in maintenance-only mode). *Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device.* The source of this demo app has been removed from the MediaPipe samples repository. This repository has no `.litertlm` file for LiteRT-LM.
- Luqwen5-4B↗
LLiserginov