Qwen3.5-4B-MTP-Uncensored
localslm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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Qwen3.5-9B-MTP-Uncensored
localslm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.6-35B-A3B-MTP-IMAT-IQ4_XS-Q8nextn-GGUF
localweights基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-27B-MTP-IMAT-IQ4_XS-Q8nextn-GGUF
localweights基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen3-4b-heretic-zimage
Lockout基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LocoOperator-4B
LocoreMind基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tofu_ft_llama2-7b
locuslab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tofu_ft_phi-1.5
locuslab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gpt2-medium-conversational
Locutusque基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗TinyMistral-248M
Locutusque基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗TinyMistral-248M-v2
Locutusque基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Chroma1-Base
lodestones基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Chroma1-Flash
lodestones基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Chroma1-HD
lodestonesChroma1-HD is an **8.9B** parameter text-to-image foundational model based on **FLUX.1-schnell**. It is fully **Apache 2.0 licensed**, ensuring that anyone can use, modify, and build upon it. As a **base model**, Chroma1 is intentionally designed to be an excellent starting point for **finetuning**. It provides a strong, neutral foundation for developers, researchers, and artists to create specialized models. for the fast CFG "baked" version please go to Chroma1-Flash.
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View details ↗Kromoss
lodestonesKromoss is a full fine-tune of MOSS-Image-Base at 1024 px. It uses the same architecture, inference pipeline, text encoder (Qwen3.5-9B) and VAE (FLUX.2 small decoder) as MOSS-Image-SFT, and this repo has the same layout, so it is a drop-in replacement. The repo also ships two 8-step turbo LoRAs distilled from MOSS-Image-Base: **8 steps, CFG 1**, no negative branch. - `8-steps-dmd2-lora.safetensors` (recommended): the DMD LoRA below, further trained with DMD2's GAN stage against real images. Slightly more contrast and detail, and the best PartiPrompts score. - `8-steps-dmd-lora.safetensors`: data-space DMD distillation. The transformer and text encoder use bfloat16; the VAE and initial noise use float32.
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View details ↗MOSS-Image-Base-8-steps-dmd
lodestonesWork-in-progress distillation of OpenMOSS-Team/MOSS-Image-Base (50 steps, CFG 3) to 8 network evaluations, no CFG. Data-space DMD (arXiv 2610.12102): the generator iterates on its own clean estimate from fixed noise. Trained on compact-JSON `scene_graph` captions. Load `lora.safetensors` on the stock MOSS-Image-Base transformer (e.g. a ComfyUI LoRA loader, strength 1) and sample with the model's usual flow-matching Euler sampler, shift 5, **CFG 1**, 4-8 steps. Nothing else from this repo is needed. `transformer/` is a stock `MOSSImageTransformer` (load it with the release `pipeline_moss_image.py`); the text encoder, tokenizer and VAE are the base repo's.
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View details ↗MOSS-Image-SFT-8-steps-dmd
lodestonesWork-in-progress distillation of OpenMOSS-Team/MOSS-Image-SFT (50 steps, CFG 3) to 8 network evaluations, no CFG. Data-space DMD (arXiv 2610.12102): the generator iterates on its own clean estimate from fixed noise. Trained on compact-JSON `scene_graph` captions. load lora.safetensors (at 1 strength recommended) or lora_latest.safetensors (at 0.7 strength recommended) directly to your comfyui lora loader `transformer/` is a stock `MOSSImageTransformer` (load it with the release `pipeline_moss_image.py`); the text encoder, tokenizer and VAE are the base repo's.
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View details ↗dots.mocr-gguf
lodrick-the-lafted基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-emotion-classifier
logasanjeev基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗indian-id-validator
logasanjeev基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗cognitive-bridge-ner
LOGiC31基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.6-Whittle-25B-A3B
logic65基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-Whittle-16B
logic65> ### ☕ Support this work > Whittle is built by one person on a grocery budget and rented GPU hours. If this research is useful to you, or you want to see it finished: > **ko-fi.com/davida81328**. Every hour of GPU time goes straight into the next checkpoint, and every checkpoint, table and log lands in these repos. **A 27B whittled down to 16.8B with a logit lens and a pricing table, then healed with one A100 evening. It now outscores every intermediate cut, including ones 4B larger, at 20 tokens/second on two consumer 8GB GPUs.** Whittle is Qwen3.8-27B minus 20 of its 64 layers (whole interval-blocks, chosen by measured boundary cost) and minus 25% of every remaining MLP's width (weakest neurons by ‖down_column‖ × activation-std). That is a −10.1B parameter cut performed with **zero training**, followed by a single 11M-token QLoRA heal on a fully clean-lineage mix (synthetic fact frames, programmatic short-think arithmetic, hand-written code drills, public-domain long pages).
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