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View details ↗DISCOVER & COMPARE
Explore AI models 84997
Ternary-Bonsai-2-27B-vllm
fraserpriceIt needs the `prism_ternary` vLLM plugin from fraserprice/bonsai-vllm, which has the run command, kernels and throughput numbers: Built and tested for the RTX PRO 6000 Blackwell only; other NVIDIA GPUs are untested. Problems: open an issue. Produced by `prism-ternary-convert` from the repo above. For benchmarks, sampling parameters and everything else about the model, see the original model card.
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View details ↗immunomatch-kappa
fraternalilab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 ↗immunomatch-lambda
fraternalilab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 ↗elmod-2.7b-it
fraunhofer-iis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-SmoothQuant-W8A8-INT8
Freaksterz**v3 (`main`, 2026-09-03):** mean full-vocab KLD vs BF16 **0.00556** — lower than the official `Qwen/Qwen3.8-27B-FP8` on the same harness (0.00584) — and the stock **DFlash2 drafter works unchanged** (acceptance length 4.34, identical to a W8A16 target). No residual-stream rotation. 288 of 400 Linear modules run INT8×INT8 on Ampere tensor cores; prefill is 1.42× a W8A16 checkpoint of the same model. Previous revisions stay available: `v2-rotated` (KLD 0.01098, rotation-based; **breaks DFlash2**, MTP only) and `v1-smoothquant-rtn` (0.01414, DFlash2-compatible). If you are here because DFlash2 gave 0% acceptance on v2: `main` fixes it. Teacher-forced full-vocab KLD, BF16 reference captured offline (transformers), candidates served through vLLM (so INT8 *activation* rounding is included). 240 × 512-token WikiText-2 windows = 122,640 positions, top-K 512 with reported tail bound. "Floor-free" is the paired estimator that removes the truncation floor (see `eval/` in the repo).
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View details ↗apex-flash-1-abliterated-GGUF
freakyskittle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 ↗CYBER-FROST-3.8-GGUF
freakyskittleThe queued trunk quants are uploaded. Each one passed a short Vulkan generation test. `CYBER-FROST-3.8-Q2_K_S.gguf` now carries the MTP head inside the file. The separate `mtp-` files still do not load. Source checkpoint: Blackfrost-AI/CYBER-FROST-3.8-BF16. Architecture `qwen4exp` (`Qwen4ExpForConditionalGeneration`), from `Qwen/Qwen3.8-Flash-Next`. The vision tower is in `mmproj-CYBER-FROST-3.8-f16.gguf`. One mmproj works with every trunk. About 180B parameters, about 6B active. 48 layers, 512 experts, 10 experts per token, one shared expert. Configured context is 262144. Smoke tests used 512 tokens of context. Long context is not tested.
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View details ↗K2-Horizon-MoVA-36B-A4B-DERISKED-GGUF
freakyskittle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 ↗LFM2.5-24B-A2B-DERISKED-GGUF
freakyskittle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 ↗MiniMax-3.1-Flash-DeRisked-GGUF
freakyskittleGGUF conversion of Blackfrost-Research/MiniMax-3.1-Flash-DeRisked, converted with llama.cpp `convert_hf_to_gguf.py`. Vision tower and multimodal projector are included as a separate mmproj GGUF — image understanding works through `llama-mtmd-cli` / `llama-server`. The upstream DeRisked checkpoint ships experts pre-quantized to NVFP4 (compressed-tensors `nvfp4-pack-quantized`) and the rest as MXFP8. This conversion repacks the NVFP4 expert tensors losslessly into GGUF's native NVFP4 layout (per-expert `weight` + `.scale` + `.input_scale` companions) and dequantizes MXFP8 blocks to BF16 in the F16 master before quantizing. *Approximate; experts dominate size at every quant level. All language-model files are split GGUF — download **every** `-0000N-of-0000M` shard of the quant you want.
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View details ↗NVIDIA-Nemotron-3.5-Bolt-74B-DERISKED-GGUF
freakyskittle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 ↗qwen36-35b-a3b-distill
freakyskittle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 ↗RED-SNOW-5.3-FLASH-GGUF
freakyskittle> GGUF quants of Blackfrost-AI/RED-SNOW-5.3-FLASH-BF16 - a red-team operations model built on a GLM-5.3-Flash (`glm5_next`) foundation with targeted security adaptation. Full-functionality GGUF exports for llama.cpp: text + **vision included** (the GLM-5.3 vision tower ships as a separate `mmproj` file, keeping every quant pairable with it). Every file was smoke-tested end to end before upload - text runs with `llama-cli`, vision with `llama-server --mmproj`. RED-SNOW 5.3 FLASH is built for authorized offensive-security work: attack-path reasoning, cross-domain operations (identity, cloud, network, app, endpoint, OT), exploit and tradecraft analysis, detection-aware execution, and purple-team handoff. See the base model card for the full picture.
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View details ↗MiniCPM-V-2.6-Arm-AArch64-GGUF
freakyXO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 ↗Legal-Qwen-SFT-Indo
freakzy-spec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 ↗LongCat-Video-Avatar-ComfyUI-GGUF
Frederic75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 ↗LTX-2-GGUF
Frederic75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 ↗Qwen-Image-2512-GGUF
Frederic75💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github    We are excited to introduce Qwen-Image-2512, the December update of Qwen-Image’s text-to-image foundational model. You are welcome to try the latest model at Qwen Chat. Compared to the base Qwen-Image model released in August, Qwen-Image-2512 features the following key improvements:
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View details ↗whisper-medium-finnish-onnx
FredrikKarlssonSpeech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 ↗deberta-v3-base-prompt-injection-v3
fredstrey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 ↗anime-anything-promptgen-v2
FredZhang7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 ↗distilgpt2-stable-diffusion-v2
FredZhang7Fast Anime PromptGen generates descriptive safebooru and danbooru tags for anime text-to-image models. This model was trained on 2,470,000 descriptive stable diffusion prompts on the FredZhang7/distilgpt2-stable-diffusion checkpoint for another 4,270,000 steps. Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.
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