BgGPT 3.0 is a series of Bulgarian-adapted LLMs based on Gemma 3, developed by INSAIT. Available in 4B, 12B and 27B sizes. 1. **Vision-language understanding** — The models understand both text and images within the same context. 2. **Instruction-following** — Trained on a broader range of tasks, multi-turn conversations, complex instructions, and system prompts. 3. **Longer context** — Effective context of 131k tokens for longer conversations and complex instructions. 4. **Updated knowledge cut-off** — Pretraining data up to May 2025, instruction fine-tuning up to October 2025. Load the model in FP8 at runtime for ~2x memory reduction with minimal quality loss — no separate quantized checkpoint needed:
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View details ↗GGUF quantized versions of BgGPT-Gemma-3-27B-IT for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible tools. BgGPT 3.0 is a series of Bulgarian-adapted LLMs based on Gemma 3, developed by INSAIT. 1. **Vision-language understanding** — The models understand both text and images within the same context. 2. **Instruction-following** — Trained on a broader range of tasks, multi-turn conversations, complex instructions, and system prompts. 3. **Longer context** — Effective context of 131k tokens for longer conversations and complex instructions. 4. **Updated knowledge cut-off** — Pretraining data up to May 2025, instruction fine-tuning up to October 2025. Search for `BgGPT-Gemma-3-27B-IT-GGUF` in the model browser, or download a GGUF file manually and load it.
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View details ↗This repository contains the extracted components and GGUF quantizations of CyberRealistic XL v10.0 by Cyberdelia, intended for use with ComfyUI-GGUF. The scripts and workflows used to generate these files are documented in https://github.com/insecure-erasure/safetensors-goodies. The following table shows the difference between some of the K-quants available with `llama-quantize` from llama.cpp using city96's diffusion model patch.
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View details ↗This is a **partial NVFP4 quantization** of Wan2.2-I2V-A14B-Moe-Distill-Lightx2v by lightx2v, produced using convert_to_quant by silveroxides. Wan2.2-I2V-A14B-Moe-Distill-Lightx2v is an image-to-video generation model built on Wan2.2-I2V-A14B. It applies step distillation and classifier-free guidance distillation to reduce inference to **4 steps** without CFG, cutting generation time substantially while preserving output quality. Since NVFP4 is only supported on NVIDIA Blackwell architecture GPUs, running this model requires a Blackwell GPU with its corresponding support enabled in torch, along with a recent version of ComfyUI and comfy-kitchen built against CUDA 13.
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View details ↗Tasks Datasets used for training and evaluation. Colour represents the type of tasks. Yellow and blue datasets are single-domain, typically requiring regression/classification losses or generation in the target domain (natural language or SMILES strings). Gradients from yellow to blue represent cross-domain generation tasks that require natural language input and SMILES output, or vise versa. The simplest way to use the large version of the model is to run the megatron_t5_seq2seq_eval.py or megatron_t5_seq2seq_finetune.py script. Prior to executing the script, prepare the input (prompts) and output (responses) files and set up the config file. The input file should have prompts on each line, which need to be preprocessed using the `add_special_symbols` function mentioned above.
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View details ↗GGUF of insraq/Qwen3.5-4B-EmperoAI-Qwen3.8-Distill-Heretic-Abliterated, with MTP drafter merged from unsloth/Qwen3.5-4B-MTP-GGUF > [!TIP] > **This model is reproducible!** > > See the README in the `reproduce` directory for more information. > [!Note] > This repository contains model weights and configuration files in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, and other standard runtimes with Qwen3.5 architecture support.
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