Model · Text generation
Ghost
A refusal-removed (abliterated) build of Qwen's Qwen3.8-Flash-Next, quantized to EXL3 2.50 bpw so the full model runs on a single 24 GB card (RTX 3090 / 4090) using MoE CPU-offload — with the vision tower, MTP head, native 262,144-token context, and the PLE n-gram table all intact. Requires the same MoE CPU-offload setup as the stock 2.50bpw pack. Needs ~59 GB host RAM for the CPU expert tail and a fast NVMe for the streamed n-gram table. Expected on an RTX 3090: ~38 tok/s decode with MTP on (~28 without), ~20 tok/s at 175K depth, ~664 tok/s prefill. See the upstream repo for the full measured ledger; this quant uses the identical flags and layout, so numbers should track closely. Fired on…
Open weights
other
22.3B parameters
262,144 tokens
/ Cockpit & Void Palette / / Hyperspace & Instrument Lights / / Low-framerate flicker keyframes (stepped timing) / / Deep radial hyperspace tunnel backdrop through cockpit window glass / radial-gradient(circle at 50% 45%, rgba(255, 230, 150, 0.25) 0%, rgba(255, 42, 75, 0.25) 18%, rgba(189, 0, 255, 0.2) 36%, rgba(0, 229, 255, 0.15) 54%, transparent 75%), repeating-conic-gradient(from 0deg at 50% 45%, #05070a 0deg 10deg, #0e131d 15deg, #05070a 20deg), radial-gradient(ellipse at center, transparent 30%, #000000 95%); / Low-framerate scanline overlay / / Heavy Flight Deck Chassis Container / 0 0 50px rgba(0, 0, 0, 0.9), inset 0 0 35px rgba(0, 0, 0, 0.8), inset 0 1px 0 rgba(255, 255, 255, 0.1)…
Open weights
apache-2.0
23.6B parameters
131,072 tokens
transformers
"Abliterated Dolphin" is a result of my 3AM brain reading about technique called abliteration and then thinking what would happen if I tried to abliterate a model that is already relatively free, such as Dolphin. Heavily inspired by mlabonne's article on abliteration on how to redirect refusals and effectively remove, or ablate, censorship from a language model. There is really no deeper meaning to any of this than pure curiosity. This is basically a dumbed down version of the original Dolphin model I used as a base, as I have not done any DPOs to heal the damage caused by abliteration. Don't try to do anything meaningful with this model. Use the original Dolphin3.0-R1-Mistral-24B instead.…
Open weights
apache-2.0
23.6B parameters
32,768 tokens
transformers
Model · Text generation
OpenAI
Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. We’re releasing two flavors of these open models: - gpt-oss-120b — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters) - gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters) Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise. You can use gpt-oss-120b and gpt-oss-20b with Transformers.…
Open weights
apache-2.0
20.9B parameters
131,072 tokens
transformers
Model · Text generation
NVIDIA
Gemma 4 31B IT is an open multimodal model built by Google DeepMind that handles text and image inputs, can process video as sequences of frames, and generates text output. It is designed to deliver frontier-level performance for reasoning, agentic workflows, coding, and multimodal understanding on consumer GPUs and workstations, with a 256K-token context window and support for over 140 languages. The model uses a hybrid attention mechanism that interleaves local sliding-window and full global attention, with unified Keys and Values in global layers and Proportional RoPE (p-RoPE) to support long-context performance. The NVIDIA Gemma 4 31B IT NVFP4 model is quantized with NVIDIA Model…
Open weights
other
20.9B parameters
262,144 tokens
Model Optimizer
GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. Its architecture intentionally resembles that of GPT-3, and is almost identical to that of GPT-J- 6B. Its training dataset contains a multitude of English-language texts, reflecting the general-purpose nature of this model. See the accompanying paper for details about model architecture (including how it differs from GPT-3), training procedure, and additional evaluations. Model](https://arxiv.org/abs/2204.06745). For details about the training dataset, see the Pile paper, and its data sheet. Discord](https://discord.gg/zBGx3azzUn), and post them in #release-discussion. Please…
Open weights
apache-2.0
20.7B parameters
2,048 tokens
transformers
