mj_relax_video
Midjourney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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mj_relax_zoom
Midjourney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 ↗IceBERT
mideind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 ↗IceBERT-large
mideind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 ↗Midjourney
MidjourneyMidjourney API（APIMart）：支持 Imagine 与图像提示词生图，覆盖 v8.1、v7、v6.1、v5.x、Niji 7 及速度模式。开通会员，API 调用立省20%起。
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View details ↗MDNT_Illus
MidnightRunner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 ↗MIDNIGHT_NAI-XL_vPredV1
MidnightRunner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 ↗MIDNIGHT_NAI-XL_vPredV2
MidnightRunner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 ↗Ultralytics
MidnightRunner3. Place your .pt files: • Segmentation → models/ultralytics/segm/ • Detection (bbox) → models/ultralytics/bbox/ 4. Restart ComfyUI 5. In the UI: Add Node → Model → Ultralytics → choose segm/… or bbox/…. Connect an Image Loader → Ultralytics node → Previewer Since getattr is classified as a dangerous pickle function, any segmentation model that uses it is classified as unsafe.
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View details ↗child-gemma4-e2b-v3-gguf
mie-edu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 ↗child-gemma4-e2b-v5-gguf
mie-edu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 ↗ViT-BERT-Chess-V4
Migga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 ↗ViT-chess-V4
Migga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 ↗Llama-3-70B-Synthia-v3.5
migtisseraLlama-3-70B-Synthia-v3.5 (Synthetic Intelligent Agent) is a general purpose Large Language Model (LLM). It was trained on the Synthia-v3.5 dataset that contains the varied system contexts, plus some other publicly available datasets. We evaluated Llama-3-70B-Synthia-v3.5 on a wide range of tasks using Language Model Evaluation Harness from EleutherAI. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
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View details ↗Llama-3-8B-Synthia-v3.5
migtisseraLlama-3-8B-Synthia-v3.5 (Synthetic Intelligent Agent) is a general purpose Large Language Model (LLM). It was trained on the Synthia-v3.5 dataset that contains the varied system contexts, plus some other publicly available datasets. We evaluated Llama-3-8B-Synthia-v3.5 on a wide range of tasks using Language Model Evaluation Harness from EleutherAI. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
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View details ↗Synthia-4-27B-GGUF
migtissera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 ↗Tess-2.0-Llama-3-70B
migtisseraTess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-70B was trained on the meta-llama/Meta-Llama-3-70B base. Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 2-epochs with a low learning rate to try and preserve its entropy as much as possible.
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View details ↗Tess-2.0-Llama-3-70B-v0.2
migtisseraTess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-70B-v0.2 was trained on the meta-llama/Meta-Llama-3-70B base. The change between v0.1 and this version, v0.2 is that v0.2 has undergone an additional step of uncensoring. Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 2-epochs with a low learning rate to try and preserve its entropy as much as possible. In v0.2, an uncensored dataset was also added to the mix.
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View details ↗Tess-2.0-Llama-3-8B
migtisseraTess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-8B was trained on the meta-llama/Meta-Llama-3-8B base. Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 1-epoch with a low learning rate to try and preserve its entropy as much as possible.
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View details ↗Tess-4-27B
migtissera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 ↗Tess-4-27B-GGUF
migtisseraGGUF quantizations of **migtissera/Tess-4-27B** — an agentic, reasoning-native 27B model that **scales its deliberation to the difficulty of the task**: tight on the easy calls, deep on the hard ones. Built on **Qwen/Qwen3.6-27B**. > 👉 **Full model card, capabilities, and details:** **migtissera/Tess-4-27B** **LM Studio / Ollama** — load any text quant. For image input in **LM Studio**, keep `mmproj-Tess-4-27B-F16.gguf` in the **same folder** as the model and it's auto-detected. Use a recent llama.cpp runtime — older builds won't recognize the `qwen35` architecture.
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View details ↗Tess-4.5-27B
migtissera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 ↗Tess-4.5-27B-GGUF
migtisseraTess-4.5 is a 27-billion-parameter open-weights model for real engineering work. It reasons through every step, works with the tools and files actually in front of it, and runs on a single GPU or a laptop. On BenchLocal it matches its base, Qwen3.8-27B, with 25% fewer tokens. This repository holds the GGUF quantizations for llama.cpp, the vision projector and the multi-token-prediction draft heads. MTP speculative decoding needs a recent upstream llama.cpp build. On a 24 GB GPU at very long contexts, quantize the KV caches (`--cache-type-k q4_0 --cache-type-v q4_0`, and `-ctkd q4_0 -ctvd q4_0` for the draft) or lower `-c`. Tess works best when it knows where it is. State the working directory in the system prompt and attach your real tool definitions:
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