MiniMax-H3
ibuildproducts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 ↗DISCOVER & COMPARE
Explore AI models 84999
Lightricks-LTX-2
ibyteohdearLTX-2.5 — Video, Audio & World Simulation Full control and customization — self-host on your infrastructure. LTX-2.5 — Video, Audio & World Simulation Full control and customization — self-host on your infrastructure. Under $10M annual revenue Commercial and production use at no cost under the LTX-2.x Community License. Transfer of fine-tunes may require a paid license, in accordance with the LTX-2.x Community License. Read the Documentation
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View details ↗Lightricks-LTX-2-DISTILLED-10-Eros
ibyteohdearAn uncensored video generation model based on LTX 2.3 supporting both t2v and i2v natively, as well as all of the other ltx 2.3 formats. Support the next version of the project, even just a few dollars would go a long way: **Kofi** **Get Started:** To get started with the model, I recommend downloading either of the dev versions, (fp8mixed or bf16) and downloading the distill lora provided. By the way, I'm aware the workflows contain sulphur_final right now, just use the lora or use the full models, don't use both at the same time.
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View details ↗ClinLinker-KB-GP
ICB-UMA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 ↗DeepSeek-V4.1-Flash-1017-dsx-2.6bit
icecubetrDeepSeek-V4.1-Flash-1017 (552B MoE + 196B Engram) quantized to **187.6 GB**, intended for two RTX 3090s and 192 GB of RAM with exact routing. Runs on **dsx**, a Rust + CUDA engine made for this model. This is a V4.1 GSQ GGUF for dsx, not a llama.cpp / Ollama / LM Studio GGUF. Measured with `dsx eval` on Lucebox's 1017 eval set: 8 × 1024 held-out tokens, KL and top-1 against the FP8/FP4 teacher's top-256 log-probs. All experts were resident (`0 left in the file`). Measured on this machine with the new `dsx` engine: 2× RTX 3090, 192 GB RAM, NVMe SSD, `--ctx 262144`, all experts resident.
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View details ↗Kumru-2B-GGUF
icecubetr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 ↗FLux_Klein_9B_NSFW
IcedSundae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 ↗IceWhiskeyRP-7b-8bpw-exl2
icefog72If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
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View details ↗Uncensored_codegemma_7b
ICEPVP8977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 ↗Uncensored_gemma_2b
ICEPVP8977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 ↗Uncensored_gemma_7b
ICEPVP8977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 ↗Uncensored_Replete_Coder_Qwen2_1.5b
ICEPVP8977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 ↗ichkil
ichkil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 ↗Merak-7B-v4
Ichsan2895This model is based on Mistral-7B-OpenOrca and fine tuned by some of Indonesia Wikipedia articles that I cleaned before. Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0) Merak-7B empowers AI enthusiasts, researchers alike.
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View details ↗Merak-7B-v4-GGUF
Ichsan2895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 ↗neural-chat-7b-v3-1-GGUF
Ichsan2895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 ↗ThinkSafe-Qwen3-0.6B
icml2026-7516Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗BOOM_4B_v1
ICT-TIME-and-Querit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 ↗ICT-TIME-and-Querit-embedding-v1
ICT-TIME-and-Querit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 ↗Unofficial mirror of orcarouter/Qwen3.8-27B-Uncensored-FP8 in the original **FP8 safetensors** format, intended for **NVIDIA GPUs** (vLLM / transformers with FP8 support).
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View details ↗PropagandaDetection
IDA-SERICS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 ↗Qwen2.5-7B-Instruct-Q5_K_M-GGUF
idasummer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 ↗Erlangshen-DeBERTa-v2-320M-Chinese
IDEA-CCNL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 ↗No matching models
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